Groundwater depth prediction plays an important role in agricultural production, rational utilization of land and water resources, and ecological environment protection. Groundwater depth is a multi-level complex system, which is influenced by many factors, further more, the groundwater’s evolution has the characteristics of uncertain, random, fuzzy and unstable. Based on the strong ability of EEMD to deal with nonlinear problem and the Elman network has the advantages of adaptive time and dynamic memory, a groundwater prediction coupling model based on EEMD and the Elman neural network is proposed, and applied to groundwater depth prediction in people’s victory canal irrigation district. The results show that the maximum relative error, the minimum relative error and the qualified rate are 2.91%, 0.04% and 100%, respectively, and the prediction accuracy of the coupling model is much higher than single BP model and Elman model. In addition, to some extent, the model can reveal the evolution mechanism and factors of groundwater time series, further more, the model is simple in calculation and clear in thinking, which provides a new way for groundwater depth prediction.
Key words
EEMD /
Elman network /
Groundwater depth /
Prediction /
people＇s victory canal irrigation district
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