Tire wear particles （TWPs） generated during vehicle driving can easily enter the bioretention system （BRS） through road stormwater runoff and continuously accumulate， posing a potential threat to the nitrogen removal function of the system. Therefore， the impact of different durations of TWPs stress on the nitrogen removal efficiency of BRS was investigated in this study. By considering the influence of TWPs on soil physicochemical properties and microbial metabolic activity， the primary driving factors of TWPs stress on nitrogen removal performance were identified. The results showed that short-term stress （0~30 d） from TWPs exerted no significant influence on the pollution removal performance of BRS. However， long-term TWPs stress （31~101 d） weakened the removal capability of BRS for NH₄ ⁺-N and TN， resulting in a decrease in removal rates by 16.90% and 6.89%， respectively （P<0.001）. The nitrification rate （NR） of the planting layer was significantly inhibited （P<0.05）， while it significantly enhanced the substrate-induced respiratory rate （SIR） and dehydrogenase activity （DHA） （P<0.01）. Moreover， long-term TWPs stress significantly increased soil organic matter （SOM） and TN content， but decreased soil NH₄ ⁺-N content significantly as well （P<0.01）； however， it had no impact on the soil properties of the submerged layer. Additionally， partial least squares path model analysis revealed a significant positive correlation between soil health chemical indexes （SOM and TN） and biological indexes （DHA and SIR） in planting layer （0.001<P<0.01）. Furthermore， an increase in SIR and DHA played a pivotal role in reducing the removal performance of NH₄ ⁺-N by BRS. The findings confirmed that the long-term accumulation of TWPs in BRS would adversely affect microbial metabolic activity within the planting layer， thereby directly impacting nitrogen removal processes.

It is of great significance to study the influence of landscape pattern on the water quality of urban rivers at different spatial and temporal scales for the protection of ecological environment in river basins. Taking Yangtze River Basin water system within Wuhan， Hubei Province as the research object， based on the statistical results of water quality indexes （including pH， DO， COD_Mn， NH₃-N， TP and TN） of 7 local monitoring sections in 2021， and extracting their land use situation in circular buffer zones with 5 radii （200， 500， 1 000， 1 500 and 2 000 m） from GlobeLand30 dataset， the relationship between landscape pattern and surface water quality at different spatial and temporal scales was studied with redundancy analysis method. The results showed that： ① The interpretation rates of water quality indexes by land use composition were the largest in the 200 m buffer zone in both wet and dry seasons （84.1% and 97.2% respectively）， and the interpretation rates by landscape pattern indexes were the largest in the 200 m buffer zone in the wet season （90.9%） and 500 m buffer zone in the dry season （88.2%）. ② The main explanatory variables of land use composition and landscape pattern indexes affecting the water quality were different at different spatial and temporal scales. For land use composition， water area and grass land had the highest contribution rate under small spatial scale in the wet season， and construction land had the highest contribution rate under large spatial scale， while in the dry season， forest land was the main explanatory variable. When it comes to landscape pattern indexes， patch density （PD）， largest patch index （LPI） and Shannon’s diversity index （SHDI） contributed the most to explaining water quality indexes in most cases. ③ Under most situations， cultivated land， grassland and PD， landscape shape index （LSI） and SHDI in the study area were significantly positively correlated with COD_Mn and NH₃-N， while forest land， unused land and LPI were positively correlated with DO， and water area was negatively correlated with COD_Mn， NH₃-N and TP. However， there was a significant positive correlation between construction land and polluted water quality indexes only in the 200 m buffer zone. Therefore， it is vital to focus on the prevention and control of pollutant discharge within the 200 m buffer zone， strengthen the management of cultivated land， forest land and construction land， and optimize the spatial pattern of urban landscape for the pollution control of urban water system in Wuhan.

As an important indicator for evaluating the water treatment process in sewage treatment plants， the accurate prediction of ammonia nitrogen content in the effluent is crucial for timely adjustment of the treatment process and ensuring the safety of the water environment. This paper proposes a water quality prediction model based on the Bi-directional Long Short-Term Memory （BiLSTM） network， improved by Multi-spatial Dimensional Cooperative Attention （MDCA）. Initially， the Pearson correlation method was used to select three indicators highly correlated with effluent ammonia nitrogen—total nitrogen， sludge settling ratio， and temperature—as inputs to the model. Then， the model combined strong correlation information across three dimensions to predict the effluent ammonia nitrogen for the next six hours. Results show that the MDCA-BiLSTM model， after integrating residual sequences， achieved an R2 prediction accuracy of 0.979 for effluent ammonia nitrogen. Additionally， the root mean square errors （RMSE） for total nitrogen， total phosphorus， and dissolved oxygen were 0.002， 0.003， 0.001， and 0.004， 0.003， 0.002 at Taiping and Wenchang sewage treatment plants， respectively. Prediction accuracies were 0.959， 0.947， 0.971， and 0.962， 0.951， 0.983， respectively. Compared to BiLSTM， the RMSEs decreased by 0.007， 0.007， 0.007， and 0.017， 0.006， 0.005， while prediction accuracies increased by 0.176， 0.183， 0.258， and 0.098， 0.109， 0.11. Moreover， the model demonstrated robust performance with prediction accuracies of 0.956， 0.933， and 0.917 for future time steps of 6， 12， and 24 hours. The improved model exhibits significant advantages in prediction accuracy and robustness. This method effectively enhances the prediction accuracy of effluent ammonia nitrogen and other indicators in sewage treatment plants， serving as a valuable reference for water resource management and decision-making， with strong practical application value.

Fishways can help migratory fish go up and down through dams and other obstacles to reach breeding grounds and feeding grounds， and complete the process of fish reproduction and migration.The vertical slot fishway is a common form of fishway in China. In this paper， the model test of the flow condition of the vertical slot fishway in a navigation hub project is carried out. The maximum design operating water head of the fishway is 6.8 m， and the main fishes passing through the fishway are the four major Chinese carps. The design flow rate of the fishway is 0.8~1.0 m/s. The fishway common pool features 3.6 m length， 3.0 m width and 0.45 m vertical slot width， and adopts “L” type partition board. The length of the straight resting pool is increased to 9.0 m， and the fishway width and partition type remain unchanged.The longitudinal slope of common fishway pool is 1∶80， and the longitudinal slope of resting pool is 0. The design water depth of the fishway pool is 2.0 m. This paper establishes a 1∶5 large-scale hydraulic physical model of fishway. It adopts suspended propeller current meter to measure the water flow velocity of vertical slot in fishway pool under different layout schemes， and adopts ADV（Acoustic Doppler Velocimeter） and PIV（Particle Image Velocimetry） technologies to analyze the water flow velocity and flow pattern in fishway pool under different layout schemes. In the physical model， the maximum vertical slot flow velocity measured by the suspended propeller current meter is 0.95 m/s， which is lower than the maximum designed flow velocity of the fishway， and the measured maximum flow rate of the fishway is 0.69 m³/s. The measured maximum flow velocity values of ADV and PIV were both located in the mainstream center of the vertical slot of the fish pool， and were about 0.94 m/s. The flow direction from the vertical slot of the fish pool is clear， and the maximum flow velocity near the vertical slot outlet is about 0.6 m/s， and the main flow stream in the fish pool forms a relatively slow “S” shape flow line. After the fish enter the pool through the vertical slot， there is a clear current to guide the fish along the main stream into the left side of the pool for rest and adjustment. The fish release test was carried out on the established 1∶5 local physical model of fishway， and it was observed that the fish swam upward through the vertical slot in fishway pool with the direction of the main stream. When the fish are tired， they can stay in the low-flow return area on the downstream side of the resting pool for rest. In general， the upstream process is smooth， indicating that under the current fishway layout conditions， the fishway pools dimensions and vertical slot size， the bottom slope of the common fish pool and the bottom slope of the resting pool are reasonable.

In order to construct a river health evaluation system suitable for Northeast China， and in response to the distinct seasonal ice-cover hydrological attributes of rivers in Northeast China and the application limitations of conventional assessment frameworks—such as suboptimal weighing and interpretation of hydrological measures—that hinder an accurate reflection of river health trajectories， this study endeavors to enhance the “Technical Guidelines for River and Lake Health Assessment （SL/T793-2020）.” Through a comprehensive incorporation of multidimensional characteristics including hydrology， ecology， and societal service functionalities inherent to northeastern river systems， we have refined both the evaluation criteria and methodologies. Employing a strategic selection of typical northeastern rivers， namely the Lalin River， the Second Songhua River， the Yitong River， the Yinma River， and the Dongliao River， as investigative subjects， we have applied the combined weighting techniques and the matter-element extension model to execute a quantitative appraisal of their respective health statuses. Additionally， leveraging the analytical mechanism of comprehensive correlation distance， we have forecasted prospective health trajectories for these riverine ecosystems. Our findings underscore that the improved health assessment paradigm for rivers in Northeast China encompasses five cardinal dimensions： hydrology， aquatic environment， riverbank structural integrity， aquatic biodiversity， and societal service provision， encapsulating a total of 16 evaluative metrics. This novel system demonstrates a heightened adeptness at addressing the unique evaluative exigencies of northeastern rivers. Notably， the health evaluation reveals the Yitong River as exhibiting a peak comprehensive correlation degree of 0.014 6， thereby categorizing its health into Grade IV， indicative of a concerning “unhealthy” condition. Conversely， the remaining rivers examined manifest a “sub-healthy” Grade III status， yet they simultaneously showcase promising trends of remediation and rejuvenation. Rigorous validation through comprehensive index methodologies and fuzzy evaluation models further solidifies the veracity of our conclusions， offering a robust scientific foundation for the perpetual health management of northeastern riverine networks. Moreover， this research proffers invaluable insights and methodological blueprints for analogous river health assessment initiatives in other cold region contexts.

Sudden increase or depletion of dissolved oxygen in river water bodies can cause a series of environmental pollution， species diversity destruction and other problems， and accurate prediction of dissolved oxygen （DO） values in rivers is of great significance to the management of river water environment. In order to improve the interpretability of model input features and model accuracy， and to obtain the optimal prediction model of river DO values， this study utilized the data from water quality monitoring stations in the Yellow River Basin in Shanxi， and used the bidirectional long and short-term memory network （BiLSTM） as the basis， combined with the convolutional neural network model （CNN） and the Attention Mechanism to optimize the feature selection， and conducted feature optimization based on the Random Forest Model （RF）， established RF-CNN-BiLSTM-Attention （RF-CBA） model， and further utilized the bionic optimization algorithms such as Bloodsucking Leech Optimization Algorithm （BSLO）， Black-winged Kite Optimization Algorithm （BKA）， and White Shark Optimization Algorithm （WSO）. A total of five optimization models， BSLO-RF-CBA，， BKA-RF-CBA， and WSO-RF-CBA， were constructed and compared with the CNN-A， LSTM-A， BiLSTM-A， CBA， and RF-CBA models of deep learning， and analyzed to obtain the prediction results of the river dissolved oxygen with the Mean Absolute Error （MAE）， Root Mean Square Error （RMSE）， Mean Square Error （MSE）， Coefficient of Determination （R ²）， Global Performance Indicator （GPI）， and Relative Error （MAPE） to evaluate the different model accuracies， and the results showed that： ① The RF model can eliminate the effect of redundant features on the water quality prediction model and improve prediction accuracies by sorting and filtering the influence of the feature values affecting the DO in rivers. ② Using the bionic algorithm to optimize the number of neurons， learning rate， regularization coefficient and other parameters of the RF-CBA model， the model simulation accuracy was further improved， the overall time series characteristics of DO fluctuations were captured， and the model showed strong stability and generalization ability. ③ The BSLO-RF-CBA model had the highest simulation accuracy， outstanding ability to capture DO changes， and stronger ability to capture global dependence relationships and is recommended for river DO prediction modeling. The model has the ability to be extended to predict the concentration of dissolved oxygen and other pollutants in different rivers， providing technical support for early warning and systematic management of river water pollution.

Considering the non-renewable and indispensable nature in crop production of phosphorus resources， the treatment and recovery of phosphorus from wastewater based on struvite crystallization method can achieve resource reuse and ensure longer life span of phosphorus rock. This manuscript systematically studied the response optimization of phosphorus recovery from phosphogypsum landfill leachate under varying temperature （5~30 °C）， providing theoretical and data support for treating leachate wastewater and obtaining high quality of struvite fertilizer. The results demonstrated that under the premise of Mg/N/P molar ratio of 1/1/1， the highest phosphorus recovery rate in the leachate wastewater was achieved at a temperature of 10 °C， being 77.5%， experiencing the largest solution pH reduction from 8.8 to 5.86 after struvite crystallization. As the crystallization temperature continued to increase， the phosphorus recovery rate showed a significant decreasing trend， and the phosphorus recovery rate was only 35.6% at 30 ℃. Meanwhile， the crystal particle size basically presented an increasing trend with the increase of temperature， and the largest size of crystal particle （68.2 μm） were formed at 30 ℃. Through X-ray diffraction and Micro-Fourier transform infrared spectroscopy analysis， the purity of struvite in the crystals formed under different temperature conditions is high， and there is basically a small amount of calcium phosphate. However， the ammonium content in crystals precipitated in the leachate was reduced with an increasing temperature， resulting in a lower purity of struvite. The suggestion is given that phosphorus recovery from leachate should be processed under lower temperature conditions （10~15 ℃）， and the phosphorus resource can be recovered from the wastewater after adding alkaline， achieving an optimal balance among phosphorus recovery rate， struvite particle size and purity.

Bacteria are an important part of the aquatic ecological environment， and the bacterial community structure will change with the change of environmental factors， and it also has a counter-effect on the aquatic environment. In order to explore the impact of ecological restoration project on the microbial community structure of drainage ditch water and sediments， water and sediment samples were collected and investigated in August 2021 （before the implementation of the project）， December 2021， May 2022 and July 2022， and the contents of eight physical and chemical factors （pH， conductivity， salinity， total nitrogen， CODMn， CODCr， total phosphorus and F-） in the water were determined. The characteristics of bacterial community structure in the water and sediments of the fifth drainage ditch were analyzed by 16S rDNA high-throughput sequencing， and the response relationship between the bacteria community and the physical and chemical factors of the water body was explored. The results showed that the conductivity， salinity， total nitrogen， total phosphorus， CODMn and CODCr of the water body were significantly reduced and the water quality was improved after the implementation of the ecological restoration project. The species diversity index of bacteria in the water body increased， the community abundance increased， the uniformity increased， the dominant bacteria phyla and bacterial community structure and composition changed significantly， the relative abundance of the dominant bacteria phyla Proteobacteria and Bacteroides increased， and the relative abundance of Cyanobacteria decreased. There was no significant change in the dominant phylum of sediment bacteria before and after the implementation of the ecological restoration project， which were Proteobacteria， Bacteroides and Desulfobacterota， and the relative abundance of Proteobacteria increased after the implementation of the ecological restoration project， while the relative abundance of Bacteroidetes and Desulfobacterota decreased. The diversity， richness and uniformity of bacterial species in sediment were higher than those in water， and there were great differences in spatial and temporal distribution. TP and F- were the main influencing factors of the bacterial community structure in water， and salinity， TP and CODMn were the main influencing factors of the bacterial community structure in sediments.

In order to solve the problem that the traditional water index easily identifies non-water bodies such as shadows， buildings and ice and snow as water bodies when extracting water bodies， a new multi-band combined water index model （NMCWI） of blue light band （490 nm）， green light band （560 nm）， red light band （665 nm）， vegetation red edge band （783 nm） and near infrared band （842 nm） is proposed by analyzing different spectral characteristics of Sentinel-2 data. In order to verify the water extraction performance of NMCWI， this paper compared several commonly used water extraction algorithms， including the improved normalized difference water index MNDWI， the revised normalized water index （RNDWI）， the normalized multiband water index （NDMBWI）， the vegetation red edge water index （RWI） and the triangle water index （TWI）， and selected six experimental areas for water extraction experiment. The results show that the overall accuracy and Kappa coefficient of NMCWI in each test area are higher than 95%， which is higher in index extraction accuracy compared with other water bodies， which can reduce the interference of most non-water bodies， such as shadows， buildings and ice. Meanwhile， NMCWI is applicable to most areas and can effectively extract water bodies.

The coordinated development of urbanization development with water security and food security is an inevitable requirement for achieving high-quality development. Metropolitan areas are key drivers for optimizing resource allocation and promoting coordinated regional development. Taking Nanjing metropolitan area as an example， we deeply analyze the evolution pattern of water security， food security， and urban development from 1990 to 2020. We use the coupling coordination degree model to reveal the spatio-temporal characteristics of the coordinated development of water security- food security- urbanization development， and identify the relevant influencing factors through the gray correlation analysis method. The results show that： ① The water security level and urban development index showed a steady increase from 1990 to 2020， while the food security level first declined and then rebounded. Overall， both water security and food security performed well， and by 2020， all cities in the metropolitan area had reached a relatively safe level. Nanjing， as the central city of the metropolitan area， had an urban development index significantly higher than other cities. ② The coupling coordination degree of water security-food security-urbanization development continues to improve， gradually entering the stage of intermediate or good coordination from a state of dissonance in 1990. The development of water security in each city is particularly prominent and prioritized. Among them， Yangzhou has the highest level of coordinated development， while for cities like Xuancheng， Wuhu and Ma'anshan their water safety and food security are far better than the urban development index， and have great potential for development. ③ Expansion of built-up land area， urbanization rate， and increase in population density are the most important factors affecting the degree of coordination of the three couplings. In addition， factors such as green coverage of built-up areas， food prices， the proportion of tertiary industry and the use of fertilizer per unit area also play an important role. The study provides a reference for coordinating the relationship between water， food， and urban development， optimizing the allocation of regional resources， and promoting the high-quality development of the metropolitan area.

Drought is one of the most frequent and destructive natural disasters that occur in arid and semi-arid regions. Its high frequency， long duration， and wide impact have had a serious impact on ecological， economic， and agricultural systems worldwide， and its spatiotemporal characteristics have become increasingly complex under the background of climate change. The research on multi-scale meteorological drought characteristics and potential influencing factors in different climate zones aims to reduce drought losses and promote sustainable development of the watershed. Therefore， this article takes the Yellow River Basin as the research object， fully considering the spatial heterogeneity of meteorological and underlying surface elements in the basin. The Mann-Kendall trend test， run theory， and multi-time scale SPEI index method are used to reveal the temporal and spatial evolution characteristics of multi-scale meteorological drought in different climate zones， and quantitatively evaluate the relative contribution of extreme climate variables to multi-scale meteorological drought in different climate zones. The results show that， in terms of time course， the overall trend of meteorological drought in the watershed at multiple time scales follows a pattern of "humid-arid-humid"， with the highest degree of drought mainly occurring around 90 years； In terms of space， the degree of drought in the middle reaches of the watershed （D and E zones） is the highest， followed by the downstream， and the upstream is the smallest. Drought characteristic variables and extreme climate indicators （precipitation and temperature） exhibit strong spatial heterogeneity in different climate regions. SDII， R10， R20， CDD， R99pToT， R95pTOT， R95p， Rx5day， Rx1day， TX90p， and SU variables explain more than 80% of the evolution characteristics of meteorological drought， and Rx5day variables dominate the contribution of meteorological drought at different time scales， with a contribution rate of about 65%. The research results provide important information for watershed ecological protection， water resource management， and climate change adaptation， and effectively improve the predictability of drought processes.

The drawdown water levels of cascade reservoirs is one of the important factor affecting hydropower generation. With the construction of the Lianghekou Reservoir with multi-year regulation capacity， determining the drawdown water levels of the Lianghekou Reservoir， and the downstream Jinping Ⅰ and Ertan hydropower stations to increase hydropower generation of cascade hydropower plants in the Yalong River needs further research. This study established a joint optimal operation model for the maximum hydropower generation of the cascade hydropower plants in the middle and lower reaches of the Yalong River. The dynamic programming- progressive optimality algorithm （DP-POA） was implemented to obtain the drawdown water levels， annual runoff and hydropower generation. The K-means clustering algorithm was employed to analyze the relationships between drawdown water levels， and annual runoff and hydropower generation under the medium and long-term optimal operation of cascade hydropower plants. The results showed that： ①Compared with the average power generation of cascade hydropower plants under conventional dispatching， joint optimal operation could improve hydropower generation by 9.108 billion kWh （the corresponding increase rate of 9.36%） with more significant increase in hydropower generation of the Jinping hydropower plant Ⅱ； ② For most inflow scenarios， the drawdown water levels of the Lianghekou， Jinping Ⅰ and Ertan hydropower plants under joint optimal operation are 2 800， 1 825 and 1 175 m， respectively， which could increase the hydropower generation by 4.043 billion kWh with corresponding increase rate of 4.16%， as compared to the conventional operation； ③ There is a positive correlation between the hydropower generation and the annual runoff and a negative correlation between the drawdown water level and the inflow runoff was found in the Lianghekou hydropower plant. While due to the influence of the runoff regulation of the upstream hydropower plants， the correlation between the drawdown water level and the runoff of the downstream Ertan hydropower plant was not strong. The research results can provide references for the joint operation and drawdown water level controlling of the cascade hydropower plants in the middle and lower reaches of the Yalong River.

The issue of saltwater tide has become a major hydrological factor restricting water supply safety in coastal cities. Understanding the patterns and evolutionary trends of saltwater tide is crucial for effective water resource management and securing water supply safety. This study， based on statistical analysis and numerical simulation methods， analyzed the evolution of saltwater tide in Modaomen Waterway at the Pearl River Estuary at different time scales since 2005 and the development trends of the saltwater interface during the dry season. An innovative trend analysis method was employed to examine the developmental trends of major influencing factors of saltwater interface changes. The results indicate that the frequency of saltwater tide in Modaomen Waterway has increased in recent years， with the number of severe saltwater intrusions shortened from approximately once every seven years to once every three years. The occurrence time has also advanced to the post-flood season， resulting in a lower chance of freshwater extraction at major intakes. A upward trend in the saltwater interface is evident， and its impact area is gradually expanding. Among the major influencing factors of the saltwater interface change， the Makou diversion ratio and sea level change exhibit a significant downward and upward trend of over 10%， respectively. Under the same inflow conditions in Sixian Jiao， the variation trend of the Makou diversion ratio is greatly influenced by the dry season. To enrich the research on the relationship between the impacts of different factors on saltwater intrusion in Modaomen Waterway and the actual water intake for water supply plants， this study predicted the changes in the saltwater interface in Modaomen Waterway under the individual and combined effects of sea level rise， river channel topographic changes， and upstream water resource allocation projects， given a flow rate of 2 500 m3/s in Sixian Jiao. The results show that under the individual effects of each factor， the upward movement distances of the saltwater interface in Modaomen Waterway are 1.2， 1.4， and 0.1 km， respectively. Under the combined effects， the upward movement distance is 2.5 km. The upward movement of the saltwater interface is significantly influenced by sea level rise and river channel topographic changes， and the combined effects have a greater impact than the individual effects.

Reanalysis datasets are attractive for hydrological modeling and reliable water resource management， especially for areas where meteorological information is scarce. This study takes the Yellow River source as the research area， uses the China Meteorological Assimilation Driving Dataset for the SWAT Model （CMADS） to drive the Soil and Water Assessment Tool （SWAT） model for daily scale runoff simulation， and uses the SWAT-CUP （SWAT Calibration and Uncertainty Program） and SUFI-2 （Sequential Uncertainty Fitting-2） algorithms for calibration and validation to evaluate the accuracy of CMADS and its applicability for hydrological simulation in the Yellow River source area. The results show that： ① The accuracy of CMADS for daily scale temperature in the Yellow River source area is very high， and the correlation coefficients with the measured data from eight meteorological stations in the basin are all above 0.95. The accuracy of daily precipitation during the flood season is satisfactory， with the relative error basically between ±10%， and the accuracy of daily precipitation during the non-flood season is poor， with the relative error basically between -30% and -50%. ②The applicability of SWAT model for hydrological simulation over the Yellow River source area is very strong. The NSE， R2， PBIAS， RSR and KGE evaluation indicators of the calibration period and the validation period obtained by driving the SWAT model using meteorological station observation data are all very good.. ③ Two methods are used to evaluate the applicability of CMADS hydrologic simulation. Method 1 is to use CMADS to drive the SWAT model to rate and validate and perform hydrological simulation； method 2 is to use CMADS to drive the SWAT model， which has already been rate-validated with the best parameters using measured meteorological data， to perform hydrological simulation. It was found that the hydrological simulation of CMADS in the source area of the Yellow River had a high correlation with the measured flow， but it was easy to underestimate the flow. Overall， the applicability of CMADS in the hydrological simulation of the source area of the Yellow River was good， in which Method I was better than Method II in the hydrological simulation. The results of this study proved that CMADS can provide a relatively reliable data source for the alpine mountainous areas where meteorological data are scarce， and provide a possibility to expand the temporal and spatial scales of hydrological simulation.

The results of multi-objective optimal scheduling of reservoirs in stochastic environment is a set of non-inferior solution sets with uncertainty， and the traditional deterministic decision-making methods are difficult to quantitatively consider the synthetical uncertainties and risks in the decision-making process of reservoir scheduling. If scheduling is based on deterministic decision results， it will create risks for the actual scheduling of the reservoir. In order to cope with the influence of uncertainty factors on the reservoir scheduling decision-making problem and to reduce the risk of reservoir scheduling decision-making in a stochastic environment， this paper proposes a risk-based group decision-making model for reservoir scheduling considering multiple uncertainties. Firstly， we quantify the uncertainties of the decision indicatorsindexes by probability distribution. Then， we propose the resolution of decision-maker group preference conflict and empowerment method based on entropy weight method， fuzzy analytic hierarchy process， game theory and the principle of minimum deviation， and then adopt the feasible weight space to derive the feasible domain of the index weights. Finally， we establish a risk-based group decision-making model based on SMAA-GTDM for reservoir scheduling， and then propose a two-stage process for risk-based group decision-making.The decision risk degree index is defined to assess the reliability of the decision results. Applying the proposed methodology to a flood control system in the Pubugou Reservoir of Dadu River basin in China， the feasibility and superiority of the risk-based group decision-making model based on SMAA-GTDM and its two-phase decision-making process in dealing with decision-making risky problems are demonstrated by a comparative analysis with the deterministic GTDM model. Further onwards， we conduct comparative verification experiments with the original SMAA-2 model. The results show that the probability of obtaining the optimal rank for the scheme with the best comprehensive rank in the two model results is 68.95% and 45.61%， respectively， with confidence factors of 69.1% and 46.22%， respectively， and decision risk degrees of 0.04% and 1.55%， respectively. This indicates that the SMAA-GTDM model can provide more explicit ranking results in the stochastic environments， significantly reduce the decision-making risk degree， and provide more robust decision-making support for reservoir scheduling.

Long short-term memory network （LSTM） model can effectively simulate the nonlinear response between rainfall and runoff， and has been widely used in flood simulation and forecast. In order to improve the applicability and simulation accuracy of the model in different application scenarios， this paper， based on the LSTM model and its five variants， carried out a case study on the rainfall and runoff time series of 30 floods from 1986 to 2000 in the Shujia watershed in the southern mountainous area of Anhui Province. The flood simulation effects of LSTM and its variant models under different loss functions， different forecast periods and different training scales are discussed. The ensemble simulation of LSTM model， its variant model and extreme Gradient Rise （XGBoost） model is also studied. The results show that： ① The four loss functions can well simulate the flood process of the outlet section of Shujia Basin， and the simulation accuracy is as follows： relative root mean square error （RSR）> Nash efficiency coefficient （NSE）> mean square error （MSE）> Klin-Gupta efficiency coefficient （KGE）. The Nash efficiency coefficient （NSE） of each RSR test set under LSTM and its variant model can reach more than 0.7. ② With the extension of the prediction period， the model faces problems such as information forgetting or error accumulation when dealing with long time series， and the accuracy of flood simulation using LSTM and its variant models generally shows a downward trend； Under the same forecast scenario， with the increase of training scale， the simulation accuracy of the model first increases to the best and then tends to be stable. ③LSTM model and its variant model are combined with XGBoost model， which reduces the simulation deviation of a single model and makes the overall prediction more accurate and reliable； Moreover， the residual simulation is introduced to make up for the characteristics of compound flood which can not be captured by the single model， and the simulation accuracy of compound flood is further improved.

This study proposes a fault diagnosis method for hydroelectric units based on signal processing and deep learning technology. Firstly， the original signals of the hydroelectric units are decomposed and reconstructed using Variational Mode Decomposition （VMD） to achieve signal denoising and obtain the Intrinsic Mode Functions （IMFs）. Subsequently， the IMFs are transformed into Gramian Angular Field （GAF） and Gramian Angular Difference Field （GADF） images through Gramian Angular Field transformation. Then， all the image data is input into a Parallel Dual-Channel Two-Dimensional Convolutional Neural Network with Bidirectional Gated Recurrent Unit （PCNN-BiGRU） model for training. This model uses CNN to extract feature maps， which are then input into BiGRU to maintain sensitivity to temporal features and eliminate redundant information. Finally， to validate the effectiveness of this method， comparative experiments are conducted using actual samples from power plant units， confirming that the proposed method provides an efficient and accurate solution for fault diagnosis of hydroelectric units.

In the outflow condition， the water flow in the diffusion section of the side inlet/outlet is prone to be separated from the side wall， resulting in local unsteady state phenomenon， which leads to flow disorder at the inlet/outlet. In this paper， the RNG k-ε turbulence model is used to conduct three-dimensional numerical simulation of the flow field at the inlet/outlet of a pumped storage power station， and the rationality of the numerical simulation is verified by model test. The results show that： when the side inlet/outlet diffuses the flow， there is a flow separation area near the mainstream due to the influence of the diffusion angle. The turbulence intensity in this area is large， resulting in local unsteady state phenomenon. The relationship between turbulence intensity and velocity square is linear. The flow velocity in the channel shows periodic fluctuations， and the fluctuation period is approximately in a power function relationship with the Reynolds number. The fluctuation period of the middle channel is smaller than that of the side channel， and the difference in the fluctuation period of the side channel and the middle channel is positively correlated with the Reynolds number. The monitoring time of the flow velocity in the model test should cover several fluctuation periods， so as to accurately measure the hydraulic characteristics of the inlet/outlet.

The layout design and hydraulic optimization of the upper flat segment of the water diversion section in pumped storage power stations are one of the important contents for ensuring the safe and stable operation of the units. This paper establishes a mathematical model of the hydraulic transition process of the pumped storage system， and systematically investigates the comprehensive effects of the diameter， length， and slope of the upper flat segment of the water diversion on the minimum pressure at the end of the upper flat segment. The regression equation for the minimum pressure at the end of the upper flat segment of the water diversion is fitted. The research results indicate that considering the layout characteristics of the water conveyance system in pumped storage power stations， appropriately increasing the diameter of the upper flat segment， shortening the length of the upper flat segment， and increasing the slope of the upper flat segment are beneficial for improving the minimum internal water pressure at the end of the upper flat segment， and the corresponding influencing mechanism is elucidated； Based on the multi-parameter regression fitting analysis， it was found that the three parameters of the upper flat segment of the diverted water were approximately linearly related to the extreme pressure at the end of the upper flat segment. Among them， the extreme value of the minimum pressure value at the end of the upper flat segment is greatly influenced by the slope， followed by the influence of diameter and length. The relevant conclusions can provide reliable technical support for the hydraulic optimization of the upper flat segment of water diversion in pumped storage power plants.

Hydraulic machinery and equipment play an important role in the current national production， and its safe and stable operation is very important. In order to solve the problem that it is difficult for a single depth feature to effectively reflect the fault information of the unit， a fault diagnosis model for hydraulic machinery and equipment based on the fusion of convolutional neural network and graph convolutional network features is proposed. Firstly， the convolutional neural network is used to obtain the convolution depth characteristics of the monitoring signal of hydraulic mechanical equipment， and the fast Fourier transform is used to obtain the spectral value of the monitoring signal， and the monitoring signal graph data is constructed， and the graph convolutional network is constructed to extract the sample association features. Then， the attention mechanism is used to perform weighted summation of different types of features to achieve multimodal feature fusion. Finally， the full connectivity layer is used to realize the fault diagnosis of the equipment. The model is validated by the measured data of the hydroelectric unit and main pump unit faults as well as the bearing fault data. The results show that the proposed model can effectively realize the fault diagnosis of hydromechanical equipment.

To explore the influence of blade angle difference on the pressure pulsation characteristics of axial flow pump， four calculation schemes based on RNG k-ε turbulence model were designed by changing the single blade placement angle of axial flow pump. And numerical simulation with CFX software were conducted to compare and analyze the pressure pulsation characteristics at the monitoring points at the impeller inlet and outlet and the guide vane outlet of an axial flow pump for each scheme. The results show that： the harmonic frequency induced at the impeller inlet and outlet monitoring points changes from high frequency to low frequency as the angle increases， and this low frequency pulsation would have a greater impact on the stable operation of the pump； at the same time， the change of the blade angle would lead to an increase in the backflow area at the guide vane outlet， which can easily form a whirlpool， then affecting the safety of pump operation.

With the grid-connected operation of large-capacity hydropower units and large-scale new energy sources， the hydro-turbine speed governing system， as the main control equipment of peak regulation and frequency regulation in the power grid， has taken on more and more heavy tasks of regulation and control. Its health status is directly related to the safety and stability of hydropower stations and power generation efficiency. Based on the digital twin five-dimensional model， according to the operating principle and spatial structure of the governing system， a new state evaluation framework for the speed regulation system of hydraulic turbine is proposed in this paper. A digital twin model of hydraulic turbine speed regulation system is established， including a three-dimensional physical model， an Operation Mechanism model and a data-driven model.The digital twin model is used as a support to complete the construction of a state assessment system for the turbine speed regulation system for digital twins. It can provide reference for the digital twin modeling of hydropower unit.

In order to further improve the efficiency of resistance-type turbines and reduce their influence on the liquid conveying capacity of pipelines， the influence of impeller design parameters on their performance was analyzed by single factor test method， and four key parameters and their value ranges were selected. Then， the response surface method was used to design the test scheme， and the function relationship between the key design parameters and the efficiency and head loss was fitted by the least square method， and the regression model is obtained. Finally， MOPSO algorithm was used to optimize the regression model， and the optimal impeller parameters was obtained. The results show that the average efficiency of the optimized resistance-type turbine is increased by 4.053%， and the average head loss is reduced by 0.679%.

In order to study the influence of guide vanes on the flow state and evolution law of the vortex structure of pump as turbine， the evolution process of vortex structure under the optimal working condition of pump as turbine is analyzed based on Omega method. Firstly， the turbine with guide vane number Z ₀=0 is taken as the base， and then guide vanes are added to it. The results show that when the number of guide vanes is Z ₀=0， the vortex structure is relatively disordered， especially the outlet vortex interaction makes its morphological structure disorderly. When the number of guide vanes is Z ₀=9， the outlet vortex range is small， and the vortex shape and evolution are clear. The vortex structure in the guide vanes is simple and the evolution process is very clear. When the number of guide vanes Z ₀=0， the vortex structure and direction are not obvious due to the interaction between the main vortex and small vortex in the tailwater pipe. The spiral main vortex in the tailwater pipe with guide vane number Z ₀=9 has the same rotation direction as the impeller， and the opposite rotation direction of the small vortex. The tangential and axial velocities of the three sections in the tailwater tube of the turbine with Z ₀=9 have been improved compared to Z ₀=0， and the maximum tangential kinetic energy of the P1 section in the turbine with Z ₀=9 is 96.40% higher than that of Z ₀=0. In summary， guide vanes can reduce the generation of vortices， increase the flow velocity of the turbine and reduce the hydraulic loss. The research results can provide a theoretical basis for the design and operation of the guide vane turbine.

In order to investigate the effect of air-entraining agent dosage on the capillary water absorption saturation of unsaturated hydraulic concrete， the capillary water absorption test of unsaturated hydraulic concrete with different air-entraining agent dosage （mass fractions of 0?， 2.50?， 3.75?， 5.00?， 6.25?， respectively） was carried out in a low-temperature environment， by using infiltration absorption and horizontal absorption methods. Then the effect of air-entraining agent dosage on concrete porosity， compressive strength， and capillary water absorption saturation was studied. And finally a prediction model for the capillary water absorption saturation of unsaturated hydraulic concrete coupled with different depths of water absorption and different air-entraining agent contents was established. The results show that： with the increase of air-entraining agent dosage， the porosity of hydraulic concrete increases linearly， the compressive strength decreases slightly with an upward convex curve， and the saturation degree decreases gradually. The saturation degree and water absorption depth were exponentially related. The saturation degree of concrete and air-entraining agent admixture of the infiltration absorption and horizontal absorption methods were linearly and quadratically related， respectively. Then a prediction model of capillary water absorption saturation of unsaturated air-entraining hydraulic concrete was established based on the separation of variables model. And the analysis shows that the new model can better predict the distribution law of capillary water absorption saturation of unsaturated hydraulic concrete under the joint action of different water absorption depths and different air-entraining agent mixing amounts.

One-dimensional consolidation tests， scanning electron microscope tests， and mercury intrusion porosimetry tests were conducted to explore the compression properties and microstructure of compacted clay under dry-wet and freeze-thaw environments with different initial moisture contents. A compression model considering the influence of environmental factors was proposed to describe the relationship between compacted clay void ratio and compression stress. The results indicate that the optimally compacted clay samples with optimal moisture content on the dry side exhibit an aggregate structure with a bimodal pore size distribution curve， while those on the wet side show a dispersed structure with a unimodal pore size distribution curve. Both dry-wet and freeze-thaw environmental effects can promote the transformation of small pores to medium and large pores within the two types of samples， leading to improved compressibility characterized by a reduction in initial consolidation stress and an increase in recompression index， with limited variation in compression index. The influence of dry-wet cycles on the pore system and compressibility of samples is greater than that of freeze-thaw cycles， with a significantly higher enhancement in compressibility observed in wet-side samples compared to dry-side samples under environmental effects. The proposed compression model considering environmental influences effectively describes the compression properties of compacted clay.

In order to make a network plan better resource leveling for roller compacted concrete（RCC） dams，so as to improve resource utilization and reduce engineering investment， the paper applies the hybrid particle swarm（Hybrid PSO） to the optimization of “Construction Period Fixed and Resource Leveling”of RCC Dam. A resource intensity variance evaluation function with process start time as independent variable is constructed， and simulated annealing（SA） algorithm is used as the convergence criterion of particle swarm optimization（PSO） algorithm to improve its global convergence ability and convergence accuracy. A speed detection method based on dynamic time difference is introduced，the Hybrid PSO is effectively integrated with“Construction Period Fixed and Resource Leveling”of RCC Dam，and an optimization mathematical model under multiple constraints is established.In the application of engineering case study， we calculate and obtain network plan with better resource leveling，and verify that the application of the Hybrid PSO in “construction period fixed and resource leveling” of RCC Dam is reasonable and effective，the convergence speed is faster and the results are better than traditional methods.

Microcapsule self-repairing technology is an effective method to solve the deterioration of concrete and improve its durability. In recent years， researchers have revealed the self-repairing function of microcapsule by studying the self-repairing microcapsule on millimeter-scale and hundred-micrometer-scale. However， the mechanical properties of matrix concrete are reduced. The microcapsules with particle size of 40 μm and 8 μm were synthesized by controlling the mixing rate and other measures， and the effects of the microcapsules on the mechanical properties of concrete were studied. Results show that： ① The thermal stability of the two particle sizes of microcapsules is good， the large-size microcapsules are broken after adding concrete， and the surface of small-size microcapsules becomes rough； When concrete is damaged and cracks appear， microcapsules will rupture in a timely manner， and the core material will flow out to repair the concrete. ② The compressive strength and flexural strength of concrete decrease with the addition of 40 μm microcapsules， while the compressive strength and flexural strength of concrete increase with the addition of 8 μm microcapsules when the content is less than 4% and begin to decrease after exceeding 4%； ③ Both 40 μm and 8 μm microcapsules can repair the damage caused by compression and bending of concrete.The maximum recovery rates of compressive and flexural strength of 40μm microencapsulated concrete are 110.2% and 113.0% respectively， and those of 8μm microencapsulated concrete are 115.6% and 114.7% ，respectively. However， when the amount of microcapsules is increased to a certain extent， it will reach an inflection point， and then the repair effect begins to decrease.The application of hydraulic concrete can effectively improve the mechanical properties of concrete by controlling the particle size of microcapsules， and timely self repair when the concrete is damaged.

In the actual construction process of water conveyance tunnel， the construction environment is complicated， and the personnel and equipment are limited by many factors. Therefore， it is necessary for improving the construction efficiency level of water conveyance tunnel to accurately identify the main factors affecting the construction efficiency and take corresponding measures. Based on AHP-grey cluster analysis method， a set of construction efficiency evaluation system is established. Firstly， the evaluation index of construction efficiency is determined， and the weight of index at each level is determined by sum-product method. Secondly， the qualitative and quantitative analysis method is used to divide the number of gray classes of construction efficiency level， and the gray class of each index is determined by expert scoring. Finally， according to the clustering results of construction efficiency level， the corresponding improvement suggestions are put forward. Taking the Pearl River Delta water resources allocation project as an example， a representative construction process of lined steel pipe assembly was selected for empirical study. The results show that the evaluation system established by this evaluation method can easily and quickly identify the influencing factors of construction efficiency and judge the level of construction efficiency scientifically， and put forward targeted suggestions for improving the level of construction efficiency.

In order to solve the problem of information loss that may be brought by a single model in dam deformation prediction， differential evolutionary algorithm （DE） is used for the parameter optimisation of the long- and short-term memory neural network （LSTM） model and combined with the multiple linear regression （MLR） model to establish a tandem combination model of MLR-DE-LSTM. The model is validated based on the horizontal displacement prototype monitoring data of a gravity dam. The results show that the DE algorithm can effectively improve prediction accuracy of the LSTM model， and the LSTM model can effectively mine the information that has not been fully explained by the MLR model. Compared with a single model， the combined model has higher accuracy and stability in predicting displacement data， and the combined model has greater advantages in making full use of the data information. The results provide reference value for improving the accuracy of dam deformation prediction.

In response to the low efficiency and high cost of abnormal personnel and seepage safety detection for small earth and rock dams， an innovative intelligent inspection method is proposed. This method is based on UAV images and deep learning object detection technology， improves the YOLOv8 network structure， and introduces fine-grained convolution and a new MPDIoU loss function. Fine-grained convolution can capture richer image details， while the new MPDIoU loss function helps to improve the positioning accuracy of the model. Experiments were conducted on a self-made dam abnormal personnel and seepage dataset. The experimental results prove that the proposed intelligent recognition algorithm has significant advantages in the comprehensive recognition of personnel and seepage， improves the detection precision of intruders in the reservoir area of the earth and rock dam project and dam seepage， and can adapt to the actual application needs in different environments.

The material of siltstone is relatively weak， which can easily lead to damage to the double line pipeline during operation. To investigate the influence of filling thickness and internal water pressure on the stability of water supply pipelines， a typical pipeline section of the Taiyangshan Water Supply Project was selected as the research object. Using the numerical simulation method， a three-dimensional finite element model of "double line pipeline—artificial filling—foundation" was established to study the influence of different single-layer filling thicknesses and internal water pressure on the mechanical properties of double-line pipelines. The research results indicate that with the increase of soil filling frequency and the thickness of single layer filling， the vertical displacement of the double line pipeline gradually increases， with a maximum value of 12.24 mm； The increase in thickness of single layer filling results in a faster increase in vertical displacement of double line pipelines； As the number of soil filling increases， the tensile and compressive stresses of the double line pipeline gradually increase， with maximum values of 0.148 MPa and 0.568 MPa， respectively. When the number of soil filling is the same， the tensile and compressive stresses of the pipe segment increase with the increase of the thickness of the single layer filling； When the internal water pressure is 0.6 MPa， the trend of circumferential deformation， tensile and compressive stresses on the inner and outer sides of the left and right pipelines are basically the same. The outer stress is smaller than the inner stress， with tensile stress decreasing by 25% and 20.1% respectively， and compressive stress decreasing by 16% and 18.2% respectively； Under the combined action of soil pressure and internal water pressure， the internal water pressure increases， and the deformation and compressive stress of the double line pipeline show a trend of first decreasing and then increasing， while the tensile stress gradually increases. The research results can provide a theoretical reference and basis for similar water supply pipeline projects.