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当前位置: 首页 > 文章 > 基于EEMD与Elman网络的灌区地下水埋深预测模型 节水灌溉 2018 (12) 86-91
Position: Home > Articles > Research on Groundwater Depth Prediction Model of Irrigation District Based on EEMD and Elman Neural Network Water Saving Irrigation 2018 (12) 86-91

基于EEMD与Elman网络的灌区地下水埋深预测模型

作  者：
张先起;宋超;胡登奎
单  位：
华北水利水电大学水利学院;水资源高效利用与保障工程河南省协同创新中心
关键词：
EEMD;Elman网络;地下水埋深;预测;人民胜利渠灌区
摘  要：
地下水埋深预测对于灌区农业生产、水土资源合理利用和生态环境保护等具有重要指导价值与作用。地下水埋深是一个受多种因素影响的多层次复杂系统,其演变具有不确定性、随机性、模糊性和非平稳性。基于EEMD较强的处理非线性问题能力和Elman网络具有适应时变和动态记忆的优点,构建了基于EEMD与Elman神经网络的地下水预测耦合模型,并将其应用于人民胜利渠灌区地下水埋深预测中。研究结果表明:基于EEMD和Elman神经网络耦合模型预测结果的最大相对误差为2.91%,最小相对误差为0.04%,预测合格率为100%,该耦合模型对人民胜利渠灌区地下水埋深的预测精度要高于单一的Elman模型和BP模型。另外,该模型在某种程度上可揭示灌区地下水时间序列的演变机制与影响因素,且计算简单、思路清晰,为地下水埋深预测提供了一种新的途径。
译  名：
Research on Groundwater Depth Prediction Model of Irrigation District Based on EEMD and Elman Neural Network
作  者：
ZHANG Xian-qi;SONG Chao;HU Deng-kui;North China University of Water Resources and Electric Power;Collaborative Innovation Center of Water Resources Efficient Utilization and Protection Engineering;
单  位：
ZHANG Xian-qi%SONG Chao%HU Deng-kui%North China University of Water Resources and Electric Power%Collaborative Innovation Center of Water Resources Efficient Utilization and Protection Engineering
关键词：
EEMD;;Elman network;;Groundwater depth;;Prediction;;people's victory canal irrigation district
摘  要：
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.Furthermore,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 ' s 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 are2.91%,0.04% and 100%,respectively,and the prediction accuracy of the coupling model is much higher than that of single BP model and Elman model. In addition,to some extent,the model can reveal the evolution mechanism and factors of groundwater time series. Moreover,the model is simple in calculation and clear in thinking,which provides a new way for groundwater depth prediction.

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