To effectively solve the high-dimensional problem of water resources carrying capacity classification and evaluation， and reveal the dynamic changes in water resources carrying capacity， an improved projection pursuit clustering model is proposed. For the improved projection pursuit clustering model and the projection pursuit clustering model based on the maximum information entropy principle， the reasonable range and optimal value of density window width were derived by analyzing the negative entropy change law of projection values； When the density window width is the optimal value， the improved projection pursuit clustering model performs better in classification evaluation than the projection pursuit clustering model based on the maximum information entropy principle. This article uses an improved projection pursuit clustering model to dynamically evaluate the water resources carrying capacity status in Jiangsu Province. The water resources carrying capacity level from 2009 to 2020 and 2022 was level III， and the water resources carrying capacity level in 2021 was level II. A grey GM （1，1） model was established to predict the water resources carrying capacity status of the province as level II from 2023 to 2030. The improved projection pursuit clustering model more effectively extracts the structural feature information of high-dimensional data for water resources carrying capacity evaluation indicators， further enhancing the accuracy of the water resources carrying capacity classification evaluation model and making the evaluation results more objective and reasonable. Through comparative analysis of the contribution rates of indicators in 2022 and 2009， the measures taken by the province to save water in industry and agriculture， accelerate socio-economic development， and protect water resources have promoted the continuous improvement of water resources carrying capacity. Based on the contribution rate of evaluation indicators to the water resources carrying capacity status and evaluation standards in Jiangsu Province， this paper deeply analyzes the shortcomings of the water resources carrying capacity in the province， and puts forward relevant suggestions to improve the water resources carrying capacity status， ensuring that the water resources system carrying capacity status in Jiangsu Province reaches level I as soon as possible and achieving sustainable utilization of water resources.

Prestressed concrete cylinder pipe （ PCCP ） plays an important role in China 's water diversion projects. The safety of PCCP operation is very important to the allocation of water resources and is related to the national economy and people 's livelihood. When harmful cracks occur on the inner surface of the core concrete， the inner core concrete will be damaged under the action of water pressure， resulting in the corrosion of the steel cylinder and the formation of safety hazards. Therefore， research on the prevention and control of concrete cracks on the inner wall of the core is of great significance to the rational utilization of water resources. In order to prevent and reduce harmful cracks on the inner wall of PCCP， according to the research results of scholars at home and abroad， the types and forms of concrete cracks on the inner wall of PCCP core are summarized， including longitudinal cracks， spiral cracks and socket cracks. The relationship between factors such as drying shrinkage， settlement shrinkage， temperature shrinkage， welding seam， winding stress， hoisting and transportation， and concrete cracks on the inner wall of PCCP is summarized. The prevention and control methods of cracks in the inner wall of PCCP core are summarized from the aspects of PCCP design， raw materials， mix ratio， maintenance system， weld， steel cylinder， socket and crack self-healing， fiber reinforced materials and so on. Finally， the control and prevention of cracks in the inner wall of PCCP core concrete are prospected， which provides ideas for further research.

This study aims to explore the feasibility of estimating the content of water-soluble soil ions by combining multispectral remote sensing technology with soil physicochemical properties. The research area is situated in the saline soil regions of southern Xinjiang， where the concentrations of major water-soluble cations and anions （K⁺、Na⁺、Ca²⁺、Mg²⁺、HCO₃ ^-、Cl^-、SO₄ ^2-） were measured. Machine learning algorithms such as Random Forest （RF）， Gradient Boosting Regression （GBR）， and Extreme Gradient Boosting （XGBoost） were employed to construct soil ion content inversion models based on remote sensing spectral features and soil information. Additionally， the study compared the estimation accuracy of models incorporating soil variables with those that did not. Results indicate that when only multispectral remote sensing data were used as input variables， all three models could only differentiate between high and low levels of soil ion content， with limited ability to accurately estimate the concentrations of individual ions. Incorporation of soil variables into the models significantly enhanced estimation accuracy. Among the methods used， the RF model exhibited the highest prediction accuracy， followed by XGBoost， and GBR had the lowest accuracy. Regarding the estimation of specific ions， the concentrations of Mg²⁺， Ca²⁺， and Na⁺ were predicted with relatively high precision and model performance was stable； SO₄ ^2-， Cl^-， and K⁺ showed moderate performance with quantitative prediction capabilities； whereas HCO₃ ^- content estimation was only feasible to a certain extent with the GBR model. Optimal models varied for different ions， with the RF model providing the best inversion results for K⁺， Mg²⁺， and Cl^-； the XGBoost model excelling in the inversion of Ca²⁺， Na⁺， and SO₄ ^2-； and the GBR model performing well for HCO₃ ^- inversion. Notably， the optimal relative analysis errors for Mg²⁺， Ca²⁺， and Na⁺ content estimation were 2.829， 1.951， and 1.870， respectively， indicating that these models are highly reliable for estimating the concentrations of these ions. The findings of this study provide a scientific reference for the regional-scale estimation of major ion concentrations in soil salinity within arid regions.

Dyes are widely used in production and daily life， leading to frequent dye pollution in the environment， especially in water bodies， which poses potential risks to ecosystem and human health. Biochar has a wide range of sources and meets the requirements of “dual carbon”， and is widely used in environmental pollution control. However， due to its poor adsorption capacity and lack of selectivity， modification is the most commonly used method to improve its sorption capacity. In order to improve the adsorption of crystal violet （CV） by biochar， magnetic biochar modified with cobalt-zinc ferrite （Co_0.5Zn_0.5Fe₂O₄@BC） was prepared from orange peel by coprecipitation-pyrolysis method. X-ray diffraction （XRD）， scanning electron microscope （SEM）， Fourier transform infrared spectroscopy （FT-IR）， specific surface area and pore size analyzer （BET） and Zeta potentiometer were used to characterize the biochar before and after modification. The experimental results showed that Co_0.5Zn_0.5Fe₂O₄@BC produced mesoporous structure， which significantly increased the specific surface area of the material. Moreover， the surface positive charge of the material is increased， and the pH adaptability of the material is improved. Thus， it promoted the adsorption of CV by biochar. The effects of different experimental conditions on the adsorption of CV by modified biochar were investigated through batch experiments. The adsorption kinetics and isotherm were fitted using different models. It was found that the adsorption capacity of Co_0.5Zn_0.5Fe₂O₄@BC for CV was significantly improved， and the removal rate reached 99.64%， which was significantly improved compared with the raw biochar （41.94%） and Co_0.5Zn_0.5Fe₂O₄ （26.78%）. The first-order kinetics and Langmuir model better fit the adsorption process and adsorption isotherm. The adsorption process was monolayer adsorption， mainly physical adsorption， and the maximum adsorption capacity was 227.59 mg/g. The modified biochar could effectively adsorb CV at pH 5~11. Common co-existing cations and anions slightly promoted the adsorption of CV by modified biochar. The adsorption performace was better in tap water and Yangtze River water than pure water， which shows that the material has a good anti-interference ability. After 5 cycles， the removal rate of CV can still reach 69.75%. This study provides a potential material for the treatment of CV polluted water. It also provides a new approach for the adsorption of dye wastewater by biochar modification.

To improve the accuracy of multi-step prediction of daily runoff， reduce the computational scale of the model， and enhance the performance of the Coati Optimization Algorithm （COA） and Hybrid Kernel Extreme Learning Machine （HKELM）， a Multi Pole Wavelet Packet Transform （MWPT） - Improved COA（ICOA） algorithm - HKELM daily runoff time series prediction model is proposed. Firstly， using MWPT， the daily runoff time series data is decomposed into 1 low-frequency component and 2 high-frequency components， and a HKELM is constructed by combining local Gaussian radial basis function kernel and global polynomial kernel function； Secondly， the principle of COA algorithm is briefly introduced， and by improving COA based on strategies such as Circle mapping， we propose the ICOA algorithm. The ICOA algorithm is simulated and verified through 8 typical functions， and is compared with the basic COA algorithm， Whale Optimization Algorithm （WOA）， and Grey Wolf Optimization Algorithm （GWO） to verify the optimization performance of the ICOA algorithm； Finally， using ICOA to optimize HKELM hyperparameters （regularization parameters， kernel parameters， weight coefficients）， a MWPT-ICOA-HKELM model is established， and MWPT-COA-HKELM， MWPT-WOA-HKELM， MWPT-GWO-HKELM， Wavelet Packet Transform （WPT） - ICOA-HKELM， Wavelet Transform （WT） - ICOA-HKELM， and MWPT-ICOA-BP models are compared and analyzed. The models are validated through multi-step prediction examples of daily runoff time series from Jingdong and Baobian hydrological stations in Yunnan Province from 2016 to 2020. The results show that： ① ICOA has a good improvement effect， and the simulation accuracy is better than COA， WOA， and GWO algorithms. ② The MWPT-ICOA-HKELM model has better prediction performance than other comparative models， with the best single step prediction performance for instances， better results with 3 and 5 steps ahead， and worse results with 7 steps ahead. The prediction accuracy decreases with the increase of prediction step size. ③ Optimizing HKELM hyperparameters using ICOA can significantly improve HKELM prediction performance， and the hyperparameter optimization effect is better than COA， WOA， and GWO algorithms.

Automated precise identification of rivers in high-resolution remote sensing images holds significant importance and research value in river and lake environmental monitoring， as well as watershed change studies. However， due to the relatively small area occupied by rivers in the images， it can lead to an imbalance between positive and negative samples in the dataset. Additionally， the morphological variability and complex scale transformations inherent in rivers contribute to challenges in river identification， resulting in issues such as discontinuous boundaries and grid effects. In response to these challenges， this paper proposes a cross-scale river precise identification method with fusion of global multilevel features. The method can be divided into three main parts. Firstly， we construct a multi-feature river dataset by selecting globally distinctive meandering and braided rivers to enhance data diversity. Secondly， we construct the R-Seg model， utilizing the lightweight semantic segmentation model Segformer as the backbone network. We design the Global and Adaptive Scale Pyramid Pooling （GASPP） module for extracting multi-scale features. This module， coupled with Transformers， facilitates the extraction of multi-scale features， enabling the model to capture contextual information in river images， reduce information loss， and amplify global dimension interaction features. Lastly， we propose a cross-scale river image prediction method based on mask-weighted voting. By employing sliding window cropping on large-scale river images， we obtain sub-prediction results by multiplying each unit prediction block with a specific mask weight. These results are then sequentially concatenated through overlapping voting， achieving precise identification of river images at different scales. The experiments demonstrate that， in the constructed multi-feature dataset encompassing meandering and braided rivers， a comparative analysis with other methods reveals the following： qualitatively， the overall structure of the R-Seg network ensures high identification accuracy for main rivers and effectively mitigates interruptions in smaller river flows， smoothing river boundaries with good robustness for 500×500 small-scale river image identification. Moreover， the use of mask-weighted voting method significantly reduces the edge loss problem caused by grid effects in unit blocks， making full use of unit block prediction results， improving river prediction accuracy for larger scenes， and achieving accurate identification of river images of different scales. From a quantitative perspective， the method achieves an overall accuracy of 99.49% with optimal performance across various accuracy evaluation metrics. Also， the single-image identification time is less than 1 second， meeting the efficiency requirements of most practical applications. Furthermore， the mask-weighted voting strategy exhibits an overall higher river identification accuracy of approximately 0.28% to 6.93% compared to a pure overlap prediction strategy. By adjusting the overlap parameter， it is observed that accuracy and overlap are not positively correlated； an accuracy of approximately 12.5% achieves relative optimization. This approach， through the design of the R-Seg network model and the introduction of the mask-weighted voting prediction method， effectively alleviates issues such as discontinuity in river boundary recognition and grid effects. It significantly enhances the accuracy of river identification in remote sensing images across diverse scenarios， demonstrating strong robustness and visual performance. The identification outcomes hold crucial application value in geological exploration of rivers and studies on watershed changes.

In order to study the clogging mechanism of the drip irrigation system of Yellow River diversion in the field， and to reduce the clogging of the irrigation system in the field， based on the characteristics of sediment in Zuncun irrigation district of Yuncheng， Shanxi Province，SolidWorks is used to model and design the flow path of emitters. The simulation parameters were set according to the actual engineering conditions of drip irrigation， and Ansys Fluent simulation software is used to simulate the flow passage of different types of emitters. The turbulent characteristics such as pressure， velocity and turbulent kinetic energy of the emitters， as well as the particle trajectories are studied to reveal the internal flow mechanism of the emitters. The pressure of the fluid basin inside the emitters decreases linearly with the flow channel units， and the pressure drop gradient is linearly related to the number of flow channel units. The fluid in the mainstream area is updated quickly， and is not prone to sedimentation. The vortex area of the flow channel and the chamfered parts of the storage tank are the main parts where sedimentation occurs.The turbulent kinetic energy is lower in the vortex region of the channel and the chamfering part of the water storage tank than in the other regions of the channel， so the turbulent flow can not be formed or maintained， which is not conducive to sediment transport， thus forming a plug. The simulation results show that， the analysis of flow velocity， turbulent kinetic energy and streamline in the inner structure of the emitters shows that the low velocity zone and the chamfer and side wall of the storage tank are the main locations of sediment deposition in the emitters.

The Three Gorges Hydropower Station has 34 units with a total installed capacity of 22 500 MW. It is a key power source for the backbone power grid of State Grid Corporation of China′s “West-to-East Power Transmission” project and “North-South Interconnection” project， supporting the safe and stable operation of the grid. However， the annual maintenance work at the Three Gorges Hydropower Station is complex and highly uncertain due to the large number of equipment， diverse unit models， and varied demands for equipment technological upgrades. Additionally， the workload for manual operations is significant. To address these challenges， a maintenance planning platform has been developed based on the SpringBoot microservices architecture. The platform includes the Three Gorges Hydropower Station Operation Scheduling and Optimal Maintenance Arrangement System. It provides a hierarchical operational service framework for decision-makers， managers， implementers， and operators at the power plant. This platform enables comprehensive digital management and control of the yearly maintenance planning， execution， feedback， and adjustments for the equipment at the Three Gorges Hydropower Plant. Furthermore， considering different maintenance requirements and prioritizing safety and stability during the maintenance process， multiple maintenance plan options have been proposed and optimized. Currently， this platform effectively serves the routine maintenance scheduling work at the Three Gorges Hydropower Station.

Water distribution is an important mode of the pipe network operation. In order to explore the basic water hydraulic characteristics near the right-angled branch of the circular channel， DN315 PVC round pipes were used to carry out tests under 5 flow rates （30， 35， 40， 45， and 50 L/s） and 5 split ratios （0.1， 0.2， 0.3， 0.4， and 0.5） in this paper. Measurements of the water depth and three-dimensional instantaneous velocity on typical sections were recorded， using the Acoustic Doppler Velocimeter （ADV）， and the mean velocity distribution and the head loss near the unpressurized pipe branch were performed. The research shows that： the surface line of the main pipe near the right-angled branch generally decreased first and then increased. High plugged water level of the large flow （45 L/s） requires attention to the headroom. The split ratio mainly affected the water level in the mouth part. The longitudinal flow velocity through the branch decreased， while the transverse flow velocity through the branch increased. The vertical and transverse flow velocity of the upper and lower the branch were distributed symmetrically， and the velocity near the right-angled branch side was larger in the area of the bleeder. The longitudinal velocity was distributed in a parabolic vertical direction， and the longitudinal velocity appeared negative at the middle axial section of the bleeder， that is， there was a reflux phenomenon. There was no negative velocity of the transverse velocity， that is， there was no lateral circulation， and the transverse velocity was distributed in a concave vertical direction. The diverter widths of horizontal flow field increased with the increase of the split ratios， and the diverter widths of middle section were larger than that of other layers. Head losses were different under different flow rates and spilt ratios， and the coefficient of total head loss was the smallest when the diversion ratio was 0.4 and the flow rate was 50 L/s. This study is of great significance to guide the design and operation of pipe networks.

This paper analyzed the pre-stressing mechanism of the steel wire and the damage process of the pipeline， and proposes a method of PCCP wire broken warning and alarm threshold assessment based on mechanical simulation analysis. The PCCP finite element model was established by ABAQUS program， and the complete construction process including laying bedding， installing pipelines， backfilling in steps was simulated， and the stress state of pipeline prestressing steel wires， steel cylinders and pipe core concrete was calculated under nearly 100 working conditions with different depths of burial and different design working pressures. It was proposed to take the maximum permissible number of broken wires of the pipe core concrete cracked under the action of water hammer as the early warning threshold for the bursting of the pipeline， and the maximum permissible number of broken wires in the limit state of the buried bearing capacity of the pipeline as the alarm threshold for pipe bursting， and analyzed the relationship between the PCCP wire broken warning and the alarm threshold under different working pressures and burial depths.

Soil water content is one of the important factors affecting the growth of crops， and plays an important role in crop yield estimation and drought monitoring. In soil water content calculation， multiple spectral variables are generally extracted for inversion， but the spectral information contained between the variables may have redundancy and overlap. In order to extract effective feature variables and make them independent of each other， the thesis selects the feature variable screening method and verifies the applicability in soil water content inversion. Based on the UAV multispectral images， 12 types of vegetation indices such as Normalized Difference Vegetation Index （NDVI） are calculated， combined with UAV thermal infrared（TIR）data to calculate the Land Surface Temperature （LST） and the corresponding Temperature Vegetation Dryness Index （TVDI）， as well as four backscattering coefficients obtained from miniSAR data processing. XGBoost feature variables and the Best Subset Selection （BSS） algorithm were used to screen the optimal variable combinations， and then Partial Least Squares Regression （PLSR） and Random Forest Regression （RFR） algorithm was used to invert， the soil water content at the tasseling stage of winter wheat in the experimental area. The research results show that： ① The inversion results of 0~20 cm depth are better than those of 0~10 cm depth； ②Comparing the four soil moisture inversion models of XGBoost-PLSR， XGBoost-RFR， BSS-PLSR and BSS-RFR， the inversion accuracy of the RFR model at different depths is the highest； ③The inversion accuracy of the XGBoost-PLSR model is better than that of XGBoost-RFR at a soil depth of 0~10 cm， but the inversion accuracy is the opposite at a depth of 0~20 cm， where the inversion accuracy of the BSS-RFR model is higher than that of BSS-PLSR. The research results can provide theoretical and technical support for UAV multispectral remote sensing inversion of soil water content， and provide test basis for satellite remote sensing large-scale soil moisture monitoring.

It is very easy to encounter the phenomenon of water and mud gushing during construction and excavation of tunnel through karst area. How to choose appropriate anti-seepage measures to reduce the seepage flow of tunnel has always been a hot issue. In this paper， a three-dimensional finite element model of complex strata in karst area is established based on a water diversion project. Three anti-seepage measures， namely back-filling concrete on the roof of the tunnel， grouting ring around the tunnel and concrete cutoff wall， are mainly considered. The change law of pore water pressure and seepage flow of the tunnel during tunnel excavation is studied， and the influence of permeability coefficient of the cutoff wall and grouting ring on seepage characteristics is analyzed. The results show that the concrete cutoff wall has a great influence on the distribution of pore water pressure in the tunnel， the water level between the cutoff walls decreases obviously， and the grouting ring around the tunnel has the greatest influence on the seepage flow of the tunnel， and the quality of the grouting ring should be ensured during construction. The pore water pressure increases with the increase of the horizontal distance from the tunnel center， and increases first and then decreases with the increase of the vertical distance from the tunnel center. With the increase of the permeability coefficient of the grouting ring or the cutoff wall， the pore pressure growth rate in the area of the grouting ring or the wall decreases gradually， and the seepage flow of the tunnel increases continuously.

In Argentina's Mendoza River Basin （33°S）， glaciers provide an essential supply of freshwater. They act as sensitive climate change indicators and are crucial for comprehending and evaluating changes in local and global climate. Based on Landsat remote sensing imagery from 1990 to 2020， this study aims to understand the distribution and changing patterns of glaciers in the Mendoza Basin and their influence on regional water supplies and ecosystems. It makes use of ratio threshold algorithms and manual interpretation to get glacier boundary data from seven different eras， using the Argentine National Glacier Inventory and the RGI glacier catalog. Furthermore， by integrating climate data from TerraClimate， the study examined the influence of regional climate change on glacier fluctuations and demonstrated the trajectory of climate change in this area during the study period. The findings show that： ① The Mendoza Basin's glacier area was 134.09±11.86 km2 in 2020. The glacier area decreased considerably （p<0.01） between 1990 and 2020， totaling 86.87±21.30 km2 （39.31±10.14%）. The greatest rate of retreat was recorded between 2010 and 2020. ② The majority of glaciers are more than 10 km2， while the least common are less than 0.1 km2. The biggest glaciers are melting the quickest. The majority of the glaciers are found on the southern slopes， with the northwest slopes having the fewest and the southeast and northeast slopes seeing the quickest rate of retreat. Their range of slopes is mostly 5° to 40°， with 50° to 55° being the quickest of retreat. With the greatest retreat rates below 4000 meters， the bulk are located at elevations between 4 200 and 5 400 meters. ③ The Mendoza region has seen a considerable increase in temperature since the late 1950s （p<0.001）， with an average decadal rise in maximum and minimum temperatures of 0.53 ℃ and 0.29 ℃， respectively. In the Mendoza Basin， glacier retreat is mostly caused by long-term increases in temperature； short-term factors include variations in precipitation. This research offers a guide for mitigating the effects of glacier fluctuations on the Mendoza Basin's water resource availability and geological calamities.

Based on fractal geometry， the description of soil pore size distribution is established， and a suction model of unsaturated soil matrix considering the fractal characteristic of pores is proposed in combination with Young-Laplace theory. For the loess in Shaanxi region， the soil-water characteristic curves of different dry densities were collected， and the effects of dry density on pore distribution characteristics and matrix suction were analyzed. The results show that the loess pores have obvious fractal properties， which can be well described by the Menger Sponge fractal model. The pore fractal model parameter analysis shows that with the increase of dry density， the fractal dimension D increases approximately linearly， while the maximum aperture L decreases as a power function. Through function fitting， the characteristic relationship between loess pore size distribution and dry density is further established， and the suction capacity of loess matrix suction under different dry densities is quickly predicted.

Based on the knowledge and understanding of modernization of the physical irrigation district and digitalization of the analog irrigation district， this paper analyzes and summarizes the “five major functions” of the construction of modernized digital irrigation district including “Identification system of irrigation district”， “Stereoscopic Perception System”， “Precise Control System”， “Information Exchange System” and “Water Allocation System”. In order to meet the requirements of intelligent water projects and efficient operation and management of irrigation district， to efficiently simulate water flow in canal system， the generalized map of irrigation district with “joint flow process” as input and output is systematically sorted out， and the key application structure system of modernized digital irrigation district is studied and proposed. That is to build an interactive overall structure with “database” as the carrier， “mathematical model” as the support layer， “nine professional applications” as the interaction layer including “organization management”， “project management”， “security management”， “pumping station management”， “water saving and water supply management”， “economic management”， “information management” and “public services” on the basis of “one digital map of the irrigation district”. It is expected to effectively improve the safety， equity， reliability and flexibility of irrigation water supply services.

Researches on hydro-wind-solar hybrid energy systems have mostly focused on coordinated operation and enhancing the new energy consumption but often overlooked the potential impact on the water levels of reservoirs. In this paper， a long-term optimal operation model for hydro-wind-solar hybrid energy systems， nested with short-term energy curtailment， is established. Firstly， the curtailment loss function was fitted based on actual hydro-wind-solar output data simulating potential curtailment scenarios， which was incorporated into a long-term optimal operation simulation； Secondly， simulations of the reservoir dispatching operation process before and after the integration of wind and solar energy were conducted； Finally， the impacts of wind and solar energy consumption on the annual， sub-annual， and monthly operating water levels of the cascade reservoir group were analyzed. A case study was carried out using the hydro-wind-solar hybrid energy system from Longyangxia to Liujiaxia cascade reservoirs in the upper reaches of the Yellow River. The results show that： ① After wind and solar energy integration， the annual average output of the cascade reservoirs decreases by approximately 0.5% to enhance the consumption of wind and solar energy， increasing the wind-solar power in grid and total system output by approximately 2.2%. ② The wind and solar energy consumption leads to increased annual and monthly water level fluctuations in the cascade reservoir group. The end-of-year drawdown and annual average water level fluctuations ranges of Longyangxia and Liujiaxia reservoirs increase by 2.5%， 101.6%， 0.8% and 78.9%， respectively. Longyangxia experiences no change in monthly water level fluctuation， while Liujiaxia sees a reduction of 14.7% in the lower limit of monthly water levels. ③ In wet years， the wind and solar energy integration leads to a significant decrease in water levels of the cascade reservoirs. In normal years， Longyangxia reservoir experiences a slight decrease in water levels， while water level of Liujiaxia reservoir decreases in pre-flood spring and increases in post-flood autumn. In dry years， wind and solar energy consumption has almost no impact on reservoir water levels. These research findings hold significant practical implications for better understanding the complex relationship between wind and solar energy integration and reservoir operations and for optimizing the operational strategies of hydropower-wind-solar complementary systems.

Accurate groundwater modeling is essential for the scientific management and decision-making of groundwater resources， as it involves hydraulic conductivity， a key hydrogeological parameter. To fully understand and effectively utilize groundwater， we not only need to accurately estimate the spatial distribution of hydraulic conductivity but also need to quantify the uncertainty of the parameter to evaluate its credibility. In this study， parameter inversion and uncertainty analysis of hydraulic conductivity were investigated using the Bayesian Convolutional Neural Network （BCNN）. To test the validity of the method， a synthetic numerical experiment of a two-dimensional steady-state hydraulic tomography pumping test was conducted. The baseline model is a convolutional neural network with an encoder-decoder structure， which builds an inverse mapping that estimates the parameter field directly from the head fields obtained by spatial interpolation. Based on this deterministic model， we trained the Bayesian Convolutional Neural Network. The results show that the BCNN outperforms the deterministic model in accuracy under various training data sizes， with a more significant advantage when the data is scarce. By analyzing the test set samples， we observe that the models exhibit different levels of confidence for their estimates across different regions. A well-trained BCNN can faithfully capture the approximate pattern of the hydraulic conductivity distribution. Moreover， the BCNN also excels in estimating the more challenging multimodal non-Gaussian logarithmic hydraulic conductivity field compared to the generative model， which indicates the wide applicability of the BCNN under diverse geological media conditions. The use of Bayesian Convolutional Neural Networks enables accurate inversion of hydraulic conductivity and evaluating uncertainty， providing a solid basis for subsequent physical processes such as groundwater flow simulation.

The processes of rising water temperature， photosynthesis， and high dam discharge may all cause the problem of supersaturated dissolved gas， which can affect the biodiversity of fish in rivers and other water bodies. Aeration technology has been proven to significantly promote the release of supersaturated TDG， DO， and DN， and is an important method for mitigating the impact of supersaturated water bodies. This article conducted experiments on the release of supersaturated dissolved gas under different aeration rates and water depths using pinhole aeration discs and microporous aeration pipes， exploring the release laws of supersaturated dissolved gas. The experimental results show that the release processes of supersaturated TDG， DO， and DN under the action of microporous aeration conforms to the first-order dynamic equation. All supersaturated DO under experimental conditions can be released to an equilibrium state， while the release rate of supersaturated DN is slow and cannot reach an equilibrium state for a long time， showing k _DO>k _TDG>k _DN； The release coefficients of supersaturated TDG， DO， and DN increase with the increase of aeration volume， but decrease with the increase of aeration depth. The release effect of supersaturated TDG， DO， and DN using microporous aeration pipes is significantly better than that of pinhole aeration discs under the same aeration volume and aeration depth. A binary linear regression model was used to establish the release coefficient relationships of supersaturated TDG， DO， and DN under microporous aeration， which can effectively predict k _TDG with the correlation coefficient of 0.958. This study reveals the release characteristics of supersaturated dissolved gases under microporous aeration， providing certain reference value for aeration technology to mitigate the impact of supersaturated dissolved gases.

Irrigation is one of the types of land management that has the greatest impact on regional climate， and its impact has been increasing in recent decades. A large number of papers have demonstrated the cooling effect of irrigation， particularly in arid and semi-arid regions. However， the cooling effect in humid regions has been found to be not significant， and the investigation of the cooling effect pertaining to extreme high temperatures has received comparatively less attention.In this study， a sliding window search algorithm is used to investigate the impact of augmenting the proportion of irrigated areas on the occurrence of extreme high temperatures in the Yangtze River Basin based on a global gridded temperature dataset and a historical irrigation distribution dataset. To isolate the climatic effects of various factors in the observed data， the multiple linear regression techniques are employed. Furthermore， a control test is set up by using the WRF model of the coupled irrigation module to perform numerical simulations for verification.The results show that irrigation in the Yangtze River Basin has a cooling effect on extreme high temperatures， and the intensity of the cooling effect depends on the proportion of irrigated area， which becomes more obvious with the increase in irrigated area， but when the proportion of irrigated area exceeds the threshold （0.25~0.30）， the cooling effect gradually diminishes. Numerical simulation experiments verified the results of the analysis based on observed data， but the model would overestimate the cooling effect of irrigation to some extent. Meanwhile， it was found that irrigation could increase soil moisture and latent heat flux and decrease sensible heat flux and soil heat flux， and the cooling effect would be weakened as the proportion of irrigated area increased， the surface moisture increased and the albedo decreased. The results of this research can provide a theoretical basis for the scientific management of agricultural irrigation in the Yangtze River Basin， which is of great scientific significance.

Soil salinization and sodification is one of the key factors affecting the sustainable agricultural development in cold arid irrigated districts. Traditionally， the total soil salt contents were used as the indicator to evaluate the evolution trend of salinization and sodification， but little attention is paid to the change of ion composition. For a further study of the distribution and migration patterns of salt ions among various water in agricultural irrigation and drainage systems， this paper analyzes the distribution and change characteristics of salt and ions in different water in irrigation areas systematically based on the observation data of various water during the irrigation and drainage process. Research has shown that the composition of salt ions in waters in the irrigated districts is different obviously/ Irrigation water is dominated by Ca²⁺ and HCO₃ ^–， while groundwater and drainage are mainly Na⁺， Cl^– and SO₄ ^2–. The quality of irrigation water is relatively good， and it is generally HCO₃ ^–－Ca²⁺ type water. Groundwater shows weak alkalinity， and is generally of Cl^–·SO₄ ^2–－Na⁺ and Cl^–·SO₄ ^2–－Na⁺·Mg²⁺ type water. Drainage is mainly influenced by factors such as regional groundwater and irrigation recession， with Cl^–·SO₄ ^2–－Na⁺ and Cl^–·SO₄ ^2–－Na⁺·Mg²⁺ type.

In order to study the internal flow law of centrifugal pump during cavitation process and optimize its cavitation performance， this paper proposes an intelligent optimization method combining neural network and genetic algorithm on the basis of combining traditional optimization methods. Through the Plackett Burman experimental design， three optimization design variables are selected from 7 design parameters of the centrifugal pump， including impeller inlet and outlet diameter， inlet and outlet placement angle， number of blades， and blade wrap angle. The significance of the three optimized design variables’ impact on cavitation performance from large to small is ranked as follows：blade outlet width＞blade wrap angle ＞impeller inlet and outlet diameter. The Latin hypercube sampling method is used to extract 30 groups of design schemes， and the corresponding NPSH values are obtained by numerical simulation. The neural network model is established， and the optimal design variable combination and the optimal NPSH value are obtained by combining the genetic algorithm to optimize within the specified range. By taking the optimized parameters for numerical simulation calculation， the NPSH of the optimized centrifugal pump decreases by 43.1% under the same working conditions， indicating that the anti-cavitation performance of the optimized centrifugal pump was significantly improved.

In response to the current problems in water quality prediction models， such as the complexity of the data itself， noise interference in signal processing， and insufficient decomposition depth， which make it difficult for a single decomposition to fully capture the nonlinear features of the signal， this paper proposes a water quality prediction model based on secondary decomposition. This innovative method firstly uses the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise （CEEMDAN） to decompose the raw data. Then， Variational Mode Decomposition （VMD） is adopted to perform a secondary decomposition of the Intrinsic Mode Function （IMF） with the highest entropy value. Finally， the processed time series are put into the TCN-light GBM multi-feature prediction model. At the same time， the Sparrow Search Algorithm （SSA） is used to optimize the prediction model. By taking the water quality of Yufu River in Shandong Province as an example， the Root Mean Square Error （RMSE） of this model is 0.105 3， the Mean Absolute Error （MAE） is 0.081 5， and the coefficient of determination （R ²） is 0.947 1.The predictive metrics of the model are compared with those from popular contemporary deep learning and neural network algorithms， such as the Gated Recurrent Unit （GRU）， Long Short-Term Memory （LSTM）， Light Gradient Boosting Machine （Light GBM）， and Temporal Convolutional Networks （TCN）， among others. The results show that， in terms of R ²， the model achieved improvements of 53.04%， 70.41%， 66.07%， and 65.20% respectively. In terms of RMSE， the model represented reductions of 62.76%， 65.50%， 64.93%， and 64.80% respectively. And in terms of MAE， the model witnessed decreases of 62.76%， 66.24%， 63.80%， and 65.24% respectively. Therefore， it is evident that the model based on CEEMDAN-VMD-TCN-light GBM exhibits superior predictive performance， which can reduce the fluctuation of water quality sequences more effectively and improve the ability to capture nonlinear features of signals.

A multi-strategy fusion improved Golden Jackal Optimization Algorithm （MGJO） is proposed to address the shortcomings of the Golden Jackal Optimization Algorithm in solving complex or high-dimensional optimization problems， such as being prone to local optima， slow convergence speed， and low computational accuracy. Firstly， by introducing a chaotic mapping strategy to initialize the population instead of random parameters， the algorithm can generate initial solutions with good diversity in the search space and avoid the initial population distribution deviating from the optimal value. Secondly， a nonlinear dynamic inertia weight is proposed to make the search process more realistic， effectively balancing the algorithm′s global and local search capabilities. Finally， the position update strategy of Cauchy mutation is introduced to fully utilize the guiding role of the optimal individual to improve population diversity， effectively exploring unknown regions and avoiding the algorithm falling into local optima. In order to verify the optimization accuracy， convergence performance， and stability of the improved Golden Jackal Optimization Algorithm， eight benchmark test functions with different features are selected for experiments. The results show that among the 8 benchmark test functions， the improved Golden Jackal Optimization Algorithm has achieved optimal results in terms of mean， standard deviation， and optimal value. In addition， the results of Wilcoxon’s sign rank test indicate that the improved Golden Jackal Optimization Algorithm is significantly superior in statistics. Through practical applications， it shows that the Golden Jackal Optimization Algorithm based on multi-strategy fusion improvement can effectively estimate the parameters of the Muskingum Model， and the optimization effect is significantly better than the particle swarm optimization algorithm， the sine cosine optimization algorithm， and the Golden Jackal Optimization Algorithm. This further verifies the effectiveness of multi-strategy fusion improvement and the superiority of the improved algorithm in parameter optimization. This provides an effective new method for more accurate estimation of the parameters of the nonlinear Muskingum Model.

In view of the fact that the multi-stage pump type intermediate guide vane designed according to the pump working condition cannot meet the runner’s requirements for flow circulation when used in hydraulic turbine devices， the influence of key structural parameters of pump type equal diameter positive and negative guide vanes on water head loss， efficiency and working head of hydraulic turbine devices is studied by combining the hydraulic turbine design theory and CFD numerical simulation. The results show that appropriately increasing the diameter of the inlet edge of the reverse guide vane， chamfering the edge of the baffle， and chamfering the inner and outer walls of the inter-stage guide vane inlet can reduce the head loss of the inter-stage guide vane and improve the working efficiency of the hydraulic turbine； the shape and number of guide vanes have a significant impact on their outlet water flow velocity circulation； increasing the wrap angle or increasing the number of blades can enhance the forcing effect of the guide vanes on water flow， increase the outlet water circulation， but increase the head loss of the guide vanes. This paper can provide a reference for the design of the same diameter forward and backward guide vanes used in hydraulic turbines.

To meet the requirements of developing high head and huge capacity hydropower plants， this paper proposes a new Pelton turbine that joints three runners by one vertical shaft to have a rated head of 1 000 m and a single unit capacity close to 800 MW. The design concept of increasing capacity by increasing the number of runners and the structural components of the new turbine-generator unit are explained， the theoretical design parameters of the runner are calculated and presented， the performance characteristics and the flow patterns in runner and casing are predicted and optimized by CFD simulation. The results show that the rated operating efficiency of the turbine can reach 87%， and the operating characteristic curves are smooth for different heads and outputs， the water splashing interference in the casing is an important factor affecting the efficiency of the turbine. By optimizing the layout of the runners and nozzles in the casing to suppress the splashing interference， the efficiency level， torque oscillation， and operation stability of the Pelton turbine can be improved. This preliminary attempt has demonstrated the feasibility of the concept and also found the importance of optimizing the water splashing in the casing， which has reference values for further researches.

Preferential flow during on-farm irrigation management can reduce water and fertilizer use efficiency and exacerbate the risk of groundwater contamination. Based on potassium iodide-starch dye tracing experiment， this paper analyzes the soil water distribution characteristics under surface irrigation DM， micro-sprinkler irrigation WP1 （20 mm/h）， irrigation WP2 （40 mm/h） treatments， verifies the validity of dual-permeability model based on principle of water volume balance， and uses four levels of antecedent water content （ 0.20， 0.25， 0.30， 0.35 cm³/cm³） with five levels of irrigation intensity （12.0， 24.0， 36.0， 48.0， 60.0 mm/h） in a rotating combination design for application analysis. The results show that water infiltrated in the form of uniform matrix flow in WP1 and WP2 treatments as a whole； the soil profile staining area under DM treatment can be clearly divided into substrate flow area （0~6.9 cm） and preferential flow area （>6.9 cm） in the vertical direction. In addition， the matrix flow depth and irrigation uniformity under DM treatment are significantly （P<0.05） smaller than those under WP1 and WP2 treatments， while their the fraction of preferential flow and wetting front curvature are highly significantly larger （P<0.01） than those under WP1 and WP2 treatments， which indicates that surface irrigation can activate more preferential flow paths， increase the degree of preferential flow development and spatial heterogeneity， and reduce irrigation quality. The dual-permeability model based on principle of water volume balance can effectively predict the trends of matrix flow depth and soil profile staining area ratio under different irrigation intensities （R ²≥0.927 6， NSE≥0.884 4， RSR≤0.023 0）， and the simulation results of the rotational combination design of antecedent volumetric water content and irrigation intensity show that increasing irrigation intensity or decreasing antecedent water content will increase the degree of preferential flow and reduce irrigation quality. Therefore， this paper recommend using “high-frequency， low-flow” irrigation mode in irrigation management to reduce the reduction of water and fertilizer use efficiency due to priority flow. The research results can provide a theoretical basis for irrigation decision， and the experimental data can provide important data support for the optimization and validation of the preferential flow model.

The installation of groynes along the riverbanks is a widely adopted practice in stream corridor restoration projects， aiming to create backflow zones （also known as dead-water zones） that can effectively enhance the river geomorphological diversity.The presence of low-velocity circulation pattern in dead-water zones can promote sediment deposition and nutrient accumulation， thus creating a conductive environment for the growth of aquatic plants. Simultaneously， it can also influence material transport and diffusion processes within rivers， which holds immense importance to river ecosystems.The mean residence time relationship for the dead-water zone with emergent vegetation is investigated here by using a combination of dimensional analysis and genetic algorithm. For vegetated dead-water zones， the factors influencing the mean residence time can be classified into three categories： the hydraulic characteristics of the mixing layer， the morphology features of side-cavities， and the drag effect caused by vegetation. Firstly， the parameter 1+C_Dad_c， which represents the obstructive impact of vegetation， is introduced via π theorem with reference to previous work. It should be noted that in the absence of vegetation， i.e.， 1+C_Dad_c =1， the material exchange activities are not affected by the canopy factor 1+C_Dad_c . However， in the presence of vegetation， the equation 1+C_Dad_c >1 suggests an influence on exchange processes. Secondly， other dominant factors， including the mainstream Froude number Fr which reflects the inlet flow intensity， as well as the three-dimensional shape factor （Wd_c ）^0.5/L and the width-to-length ratioW/L，which reflect the morphological features of cavities， are identified through a comprehensive analysis and comparison. Then， the aforementioned four factors are used as independent variables to construct a general predictive model for the mean residence time in the vegetated cavity， i.e.， a product model of power functions incorporating these four factors. Finally， based on 85 groups of data gathered from previous studies， the genetic program Eureqa is employed to train this general model， and subsequently， a mean residence time relationship is developed for vegetated dead-water zones. The evaluation on the coefficient of determination R ² and the mean absolute error MAE demonstrates that the present formula possesses good predictive ability， and the analysis of the value ranges of each factor reveals that this formula exhibits a broad range of applicability. In addition， based on a comparative analysis of the impact of the four factors on the model results， the cavity aspect ratioW/L is considered as a critical parameter that significantly influences water residence characteristics in dead-water zones and should be duly taken into account when relevant engineering designs are conducted.

Based on BP neural network and higher-order moment method， combined with measured data， a time-varying reliability analysis method for the bending resistance of reinforced concrete aqueduct side walls is proposed. In order to evaluate the bending time-varying reliability of aqueduct side walls under the influence of steel corrosion， this paper considers the effective cross-sectional area loss effect of steel bars in concrete structures， and derives the bending time-varying performance function under the limit state of bearing capacity of the aqueduct side wall. Then， combined with the measured data of steel corrosion and the principle of BP neural network， the prediction model of steel corrosion rate is designed， and the calculation formula of effective cross-sectional area of steel bars in concrete structures is established through the hemispherical pitting model. On this basis， the point estimation and high-order moment reliability theory is introduced to develop the time-varying reliability analysis method of aqueduct structure， and finally the proposed method is applied to the bending time-varying reliability analysis of the side wall of an actual aqueduct. The results show that compared with the previous eight practical empirical models， the prediction model of BP neural network built in this paper can predict the corrosion rate of rebar in concrete structures more accurately， comprehensively， easily and quickly. By comparing with Monte Carlo simulation method， the time-varying reliability index of this method is efficient and accurate， which can provide an effective way for the evaluation and prediction of time-varying reliability of aqueducts.

In order to investigate the dynamic stress characteristics of runner and distributor of pump-turbine， this paper establishes the solid domain models of stay vanes， guide vanes and runner， and carries out the FSI-based computation of runner and distributor. The calculation results show that the stress concentration at the “T” junction between the runner blade and crown is related to the vortex development state at the runner inlet； the dynamic and static interference between runner and guide vanes is the main source of pressure pulsation in the vaneless zone， where the main frequency is the blade passing frequency； the dynamic stress of guide vanes and runner is generally high because the pump-turbine is easily involved in the unstable area while working under smaller GVO （guide vanes opening） or low head conditions. The dynamic stress of guide vanes is greater than that of the runner owing to its constraint guidance effect on water flow， and increased flow rate is beneficial to reducing the dynamic stress of the runner. So for the purpose of improving the dynamic stress of runner， the measures could be taken， such as changing the distance between the guide vanes tail and runner inlet， i.e. changing the width of vaneless zone， or adjusting the unit operation area， i.e. avoiding working in low road or small opening conditions for long.

In our present stage， agricultural water right trading is dispersed and the scale is smaller， and the market activity of agricultural water right trading is not in full swing. With the contradiction between supply and demand of water resources becoming increasingly tense， agriculture has necessity to stimulate the decisive role of market in water resources allocation， and further create greater trading space for agricultural water rights. Agricultural water rights involve multi-level management systems， but few studies have analyzed the factors affecting the development of agricultural water rights trading from different dimensions in detail， so it is impossible to systematically promote the development of agricultural water rights trading. Therefore， this paper divides the key factors affecting the development of agricultural water rights trading into three dimensions： macro social economy， middle market players and their mutual relations， and micro trading elements. A qualitative analysis is made of the substantive attributes of each factor index in different dimensions and its impact on the development of agricultural water rights trading， and the macro-support for the development of agricultural water rights trading is found. This paper clarifies the development ideas of agricultural water rights trading market， and then focus on the role of micro-trading elements in the process of water rights trading. From three dimensions， it puts forward suggestions on how to strengthen the agricultural water rights trading policy and promote the construction of rural water system connectivity in multiple ways， clarify the role positioning of government and enterprises， and encourage the synchronous transfer of agricultural water rights trading and land to activate the agricultural water rights trading market so as to improve the agricultural water rights trading market to provide the construction foundation and ideas.

Within the hierarchical decision structure of reservoir pre-impoundment operations， the parameter equifinality of hydropower generation leads to non-uniqueness of optimal solutions， i.e.， the “ill-posedness” of solving reservoir operation optimization problems. Under such circumstances， the realization of operation benefits is affected by whether the reservoir operator selects the refill plan in favor of flood safety， implying that not only competitive relationship but also cooperative potential exists between flood control and water conservation. In light of this， a cooperation incentive （CI） model based on the lower-level satisfaction is developed to provide a mechanism to promote the water conservation department’s cooperation with the flood control department and enhance the reservoir operation benefit. Based on the framework of ill-posed bilevel programming， regarding the actual decision characteristics， the model described the nonlinear correlation between the cooperation willingness and the expected benefit of the water conservation department given certain flood control rule， so that the probability of selecting the refill plan in favor of flood safety can be derived. The CI model is solved by using multi-swarm evolutionary particle swarm optimization algorithms. Quantitative indicators are proposed to evaluate the Pareto efficiency loss and overall goal achievement of the reservoir operation optimization under cooperation. In the Three Gorges Reservoir pre-impoundment case study， the results are compared with those of the optimistic， pessimistic， and partial cooperation models. Results show that the CI mechanism motivates the benefit concession of the flood control department to increase hydropower generation and encourage the water conservation department’s choice in favor of flood safety. Further， the efficiency loss in operation decisions due to competitive gaming process can be more prominently reduced. Findings also indicate that nonlinear satisfaction-expected benefit relationship can better describe the practical decision making in reservoir operation.

To address the insufficient consideration on water level safety requirements at flood control stations in conventional reservoir flood control operation， this study proposes an optimal flood control operation method for reservoir group coupled with water level calculation at basin flood control stations. First， the station water level characteristics are analyzed through the multi-site water level and flow relationship to identify the key factors affecting the water level. The input features for water level calculation are screened according to the correlation between the influencing factors and the water level. The multi-layer feed-forward back propagation neural network （BP neural network） is introduced to fit the complex non-linear relationship between the input features and the observed water level， to realize the accurate calculation of water level at the station. Then， the safety margin index is proposed to quantify the station water level safety. With the objective of maximizing the overall safety margin of the downstream flood control stations， the optimal flood control operation model for cascade reservoirs， coupled with BP neural network， is constructed. Finally， a hybrid optimization algorithm （DPSA-POA-PSO） is developed to solve the model by combining the advantages of the Dynamic Programming Successive Approximation （DPSA）， Progressive Optimization Algorithm （POA） and Particle Swarm Optimization （PSO）. The results show that the water level calculation accuracy of the BP neural network is significantly improved by considering the station water level characteristics， and the flood level fitting deviation for typical floods is limited within 0.05m. Compared with the DPSA and PSO algorithms， the downstream overall safety margin from the hybrid optimization algorithm is improved by 0.88% and 2.58% in the joint operation of three cascade reservoirs， and is improved by 0.85% and 1.87% in the joint operation of five cascade reservoirs. Meanwhile， the derived operation schemes satisfies all the guaranteed water level requirements， which provides reliable support for improving the basin flood control safety.

The purpose of this paper is to study the effect of the subsurface drainpipe with triangular section and pipeless wall on inhibiting water flow around the cross section in unsaturated soil. In this study， the water drainage tests are carried out by using the subsurface drainpipe with triangular section （symbol T1） in indoor soil tank， as well as the subsurface drainpipe with circular section （symbol CK） done as a control test， both kinds of drainpipes are pipeless wall. The MATLAB system is utilized to simulate the process of soil water flowing around pipe section. The experimental results show that the soil water content at the bottom of the drainpipe T1 treatment is less than that of CK treatment， and the soil salt content at the bottom of the drainpipe T1 treatment is more than that of CK treatment， indicating that the inhibitory effect of T1 treatment on soil water flow around drainpipe cross section is better than that of CK treatment. The beginning time of soil water discharging from the outlet of T1 treatment is 3.89 h earlier than that of CK treatment， and there is no significant difference between the two treatments in drainage and salt discharge（p>0.05）. The simulation results reveal the characteristics of the drainage of this kind of subsurface drainpipe in unsaturated soil， which proves theoretically that the water content under the T1 treatment is lower than that of the CK treatment， and T1 treatment has a better inhibitory effect on the flow detour around drainpipe cross section. In unsaturated soil， the subsurface drainpipe with triangular section and pipeless wall can effectively inhibit soil water flow around the cross section than the circular section subsurface drainpipe， which is conducive to the drainage for this king of subsurface drainpipe in unsaturated soil.

In the multi-energy complementary power generation system， the wind-photovoltaic output is unstable and discontinuous， which makes the hydropower units in the system need to change the working condition frequently， which brings challenges to the economy， safety and stability of hydropower station operation. When the unit vibration avoidance strategy is considered in the load distribution of the hydropower plant， the decision variables of the ordinary particle swarm optimization model will become a complex matrix with high dimensionality， mutual coupling and discontinuity. The convergence result of the particle velocity updating process in the model is greatly reduced due to the reduced suitability of the process. In this paper， a bidirectional updating multi-objective particle swarm model is proposed innovatively. Based on the day-ahead hour-level scheduling mode and vibration avoidance strategy of the unit， two objectives of the unit combined crossing number and the sum of the optimal working conditions of the unit throughout the day are optimized. The results show that compared with the ordinary particle swarm optimization algorithm， the calculation speed of the optimization model is improved by 14.7%， and the convergence accuracy range is improved by 4% to 6% on average， which can provide a new theoretical support for the optimization operation of hydropower station with both economy and safety stability.

The tidal level fluctuations in the estuarine area trigger fluctuations in the water level of the coastal diving layer， exhibiting a non-synchronous fluctuation pattern with the tidal level. This phenomenon has a profound impact on the stability of the bank slope. The varying water levels disrupt the equilibrium and increase the risk of slope instability， posing significant challenges to the structural integrity and long-term stability of the slope. During the ebb tide stage， the groundwater table within the interior of the bank slope tends to be higher than the tidal level. Consequently， this leads to a greater water holding capacity in the saturated area and creates conditions that are more susceptible to inducing bank slope collapse and instability. The higher water levels during this stage increase the potential for instability， posing a significant risk to the structural integrity and stability of the bank slope. In order to address the specific challenges of complex estuaries， this paper presents SUBS， a bank collapse warning model. SUBS is developed by improving the BSTEM （Bank Stability and Toe Erosion Model） and coupling it with SUTRA （Saturated-Unsaturated Transport）， a groundwater model that incorporates unsaturated flow movement. By combining these advancements， SUBS provides a comprehensive and accurate framework for predicting bank collapse events in intricate estuarine environments， contributing to improved bank collapse risk assessment. The research findings show that the fluctuation of groundwater caused by the fluctuation of tidal level in estuarine area has quite a strong hysteretic property. The Factor of Safety of the bank slope and the most dangerous failure surface will change periodically with the tide. And the Factor of Safety will increase during the rising tide， reflecting improved stability. Conversely， during the ebb tide， the Factor of Safety decreases due to the lag in groundwater drainage. Especially during the ebb stage of the spring tide， the water level plummets rapidly， significantly heightening the risk of bank slope collapse. Additionally， the presence of seepage faces on the bank slope further increases the vulnerability to instability. By comparing the stability of homogeneous and composite bank slopes， the results strongly indicate that soil cohesion plays a significant role in determining the stability of the slope. This finding underscores the importance of considering the cohesive properties of the soil when bank slope stability is assessed. The stability of the bank slope is significantly enhanced in the composite structure， where clay is present on the upper part and sand on the lower part. This configuration outperforms the bank slope composed of homogeneous sand with low cohesion. The presence of clay provides increased cohesion， resulting in improved resistance to slope failure and greater overall stability of the composite bank slope. In this context， more attention should be paid to the influence of groundwater level fluctuation. This paper expands the application scope of the traditional BSTEM model by coupling a groundwater model and considering real-time fluctuations in groundwater levels. It introduces a new research method for predicting the slope stability in estuarine areas， thereby enhancing our understanding and analysis of slope behavior in these complex environments.

This paper evaluates the spatial equilibrium status of water resources in Yunnan Province from 2006 to 2022 and 2025 scientifically， and proposes a water resource spatial equilibrium evaluation model based on social network search （SNS） algorithm， mountaineering team optimization （MTBO） algorithm， bi-directional gated loop unit （BIGRU） optimization， and bi-directional long short-term memory （BILSTM） network optimization. First， 15 indicators are selected from three aspects of water resources support， water resources pressure and water resources regulation to build the evaluation index system and grade standard of water resources spatial balance， and the samples are generated by linear interpolation and random selection methods to build BiGRU and BiLSTM fitness function. Secondly， the principles of SNS and MTBO algorithms are briefly introduced. SNS-BIGRU， MTBO-BIGRU， SNS-BILSTM， and MTBO-BILSTM models are constructed by using SNS and MTBO to optimize the number of neurons in the hidden layer of BiGRU and BilSTM and the learning rate （hyperparameter）. The robustness of four models， including SNS-BIGRU， MTBO-BIGRU， SNS-BILSTM， and MTBO-BILSTM， is verified through different sample sizes and 10 consecutive runs. Finally， SNS-BIGRU， MTBO-BIGRU， SNS-BILSTM， and MTBO-BILSTM models are used to evaluate the spatial balance of water resources in Yunnan Province from 2006 to 2022 and 2025， and the evaluation results are compared with those of SNS-Support Vector Machine （SVM）， MTBO-SVM， and fuzzy comprehensive evaluation methods. The results show that： ① the models such as SNS-BIGRU have good recognition accuracy and robustness. SNS and MTBO can effectively optimize the hyper-parameter of BIGRU and BILSTM， and improve the prediction performance of BIGRU and BiLSTM. ② SNS-BIGRU and other four models evaluate the spatial balance of water resources in Yunnan Province from 2006 to 2011 as “unbalanced”， from 2012 to 2013 as “relatively unbalanced”， from 2014 to 2018 as “critical equilibrium”， from 2019 to 2022 as “relatively balanced”， and by 2025， it can basically reach the level of “balanced”. There is a 3-year difference in the evaluation results of the four models compared to SNS-SVM， MTBO-SVM， and fuzzy comprehensive evaluation methods. The model method constructed and proposed in this paper serve as a reference for the spatial balance evaluation of water resources.

Model parameter calibration is an important way to improve the simulation effect of hydrological models. In order to solve the problems of low initial population quality， prematurity and poor local search ability of traditional genetic algorithm， this paper proposes an improved adaptive genetic algorithm （IAGA） to optimize the parameters of Xin?anjiang model. The Ergodic characteristics of chaos variables are used to randomly generate the initial population and select the best， so as to improve the individual quality of the initial population. Aiming at the evolutionary process of crossover and mutation， this paper designs the discrete coefficient of population objective function to reflect the degree of population dispersion. By using this coefficient， the adaptive adjustment crossover and mutation probability operators are constructed to prevent premature convergence of genetic algorithm. Based on the ring crossover operator， the global search ability of the algorithm is improved. The adaptive non-uniform mutation operator is used to optimize the local search ability of the algorithm in real time and avoid falling into local optimum. The IAGA algorithm， traditional genetic algorithm （GA） and adaptive genetic algorithm （AGA） are applied to the parameter calibration of the Xin′anjiang model in the Qinhuai River Basin， and the performances are compared from the aspects of convergence， time-consuming， stability and effect. The results show that the IAGA algorithm has more excellent optimization ability， better convergence results， higher stability and accuracy. The flood simulation results are better than the GA algorithm and the AGA algorithm. During the calibration period and the verification period， the deterministic coefficients are higher than 0.85， and Nash-Sutcliffe efficiency coefficient are higher than 0.8， which generally meets the second-level standard of hydrological forecasting. The results show that it is feasible to improve the traditional genetic algorithm by using the above comprehensive means， and the improved IAGA algorithm has a good application prospect， which provides an effective way for the automatic calibration of Xin′anjiang model.

In order to explore the influence of the staggered impeller on the pressure fluctuation characteristics of the double entry two-stage double suction centrifugal pump， this paper uses the method of combining experiment and numerical simulation to carry out a research on the same pump with symmetrical impeller and staggered impeller as the contrast scheme， and quantifies the pressure pulsation characteristics of the suction chamber， inter-stage flow channel and volute under three typical working conditions of 0.6Q， 1.0Q and 1.2Q. The results show that： ① in terms of external characteristics， the head and efficiency of the pump with staggered impeller are higher than those with symmetrical impeller at all working conditions， and they can be 2% and 3% higher than those at 1.0Q working condition. ② In terms of pressure pulsation characteristics， for the suction chamber， the influence of staggered impellers on pressure pulsation is small at all working conditions， and the difference of dominant frequency amplitude is less than 2%. For the inter-stage flow channel， the staggered impeller can restrain the pressure fluctuation at all working conditions. For example， the dominant frequency amplitude at the tongue of the inter-stage flow channel can be reduced by 13.5% compared with that of the symmetrical impeller. For the volute， the staggered impeller can improve the pressure fluctuation under all working conditions. For example， the dominant frequency amplitude at the tongue of the volute can be reduced by 31.9% at most. This finding can provide a reference for the application of staggered impeller in double inlet two-stage double suction centrifugal pump.

While China’s economy has progressed exceedingly rapidly， its adverse impact on the environment has been overlooked， thus resulting in numerous issues related to environmental contamination， including water resources pollution. The submersible pusher， due to its diminutive size， facile installation process， and excellent maintenance qualities， is capable of being installed within treatment pools of limited volumetric capacity. By enabling enhanced processing efficiency within each pool， this approach facilitates system-wide efficiency without being subject to the limitations of a singular step’s efficiency. During the procedure of wastewater treatment， the submersible mixer plays a paramount role as one of the essential core components. Its performance and function bear a significant impact on the ultimate quality of the entire sewage treatment process. Hence， the subaqueous propeller has emerged as a crucial apparatus that facilitates the process of amalgamating and propelling fluid within sewage treatment facilities. An integration of experimentation and computational fluid dynamics （CFD） numerical simulation is employed to investigate the various performance indicators， including thrust， power， and thrust-to-power ratio of a submersible thruster operating at a rotating speed of 980 r/min and having a diameter of 400 mm. Additionally， a three-dimensional geometric model is established， followed by the conduction of structured mesh delineation and mesh-independence analysis in order to ensure accurate simulation results. Finally， non-stationary calculations are conducted on the submersible thruster using the aforementioned methodology. Through numerical calculations， it is determined that the submersible mixer equipped with the deflector generated a thrust of 759.33 N， consumed 888.29 W of power， and yielded a thrust to power ratio of 0.85 N/W. Conversely， the submersible mixer without the deflector produced a thrust of 1 003.39 N， utilized 1 144.72 W of power， and resulted in a thrust to power ratio of 0.88 N/W. The present study examines the impact of a deflector on a submersible thruster’s thrust， power， and thrust-to-power ratio. Results indicate that the inclusion of a deflector reduces the aforementioned variables by 24.3%， 22.4%， and 2.4%， respectively， as compared to a submersible thruster lacking a deflector. Through the experiment， it is determined that the submersible mixer equipped with the deflector generated a thrust of 740 N， consumed 903 W of power， and yields a thrust to power ratio of 0.82 N/W. Conversely， the submersible mixer without the deflector produces a thrust of 977 N， utilized 1 164 W of power， and results in a thrust to power ratio of 0.84 N/W. The thrust， power， and thrust-to-power ratio of a submersible thruster with a deflector are reduced by 24.3%， 22.4%， and 2.4%， respectively， for a submersible thruster without a deflector. The discrepancy observed in the comparison of the simulated and experimental measurements of thrust， power， and thrust-to-power ratio falls under 5%， hence confirming the reliability of the experimental results and effectively attesting the precision of numerical simulation. The application of a deflector to the thruster has a positive impact on the thruster's effective propulsive distance. Specifically， when compared to the situation in which a deflector is not applied， the effective propulsive distance increases by 4.8%， 28.4%， and 30.8% at velocities of 0.3， 0.4， and 0.6 m/s， respectively.

Riverbed roughness is an important aspect of river dynamics research， and its quantitative measurement has always been challenging. In this experiment， regardless of the macroscopic undulation of the riverbed， we laid the initial bed surface in a straight channel with non-uniform natural sand and thickened it by gradually increasing the flow rate of water. Laser scanning technology was used to obtain high-resolution bed elevation data after each bed roughening. Statistical theory and variogram function were used to investigate the surface roughness characteristics of riverbeds with varying degrees of coarseness， and to deepen the understanding of their statistical characteristics. The study's main finding is that artificially laid non-uniform sand presents an elevation frequency distribution that approximates a normal distribution. On the contrary， the frequency distribution of the stable rough bed surface formed by the continuous cumulative erosion of different flow intensities shows a slight positive deviation trend. The study found evidence that the increase of flow intensity leads strictly to an associated elevation standard deviation increase. This finding indicates that elevation standard deviation can be used as a reliable indicator to measure the characteristics of riverbed roughness in future studies. The study revealed that the kurtosis of the bed elevation data precisely decreased monotonically with the flow intensity， and that skewness and kurtosis showed a monotonic trend， indicating that they can be used to measure bed roughness. Additionally， the study found that the flow forming the bed surface is anisotropic， and as flow intensity increases， the rough layer breaks and becomes rougher again. This leads to an increase in the base value of the two-dimensional bed elevation variogram. Therefore， the study suggests that the base value of the two-dimensional variogram could serve as another reliable index for evaluating bed surface roughness. In addition， due to the influence of water flow， although the change trend of the characteristic parameters of the average variogram along the flow direction， vertical flow direction and radial profile elevation is consistent， the average variogram base value and golden point value of the transverse section are slightly larger than those of the transverse and longitudinal sections. Conversely， in the radial section， the abutment value and nugget value are considerably smaller than the values in transverse and longitudinal sections. In conclusion， this study enhances our understanding of the characteristics of bed roughness and provides crucial insights into statistical expressions for quantifying it.