长江科学院院报2016，Vol.33Issue(8)：18-21,4.DOI:10.11988/ckyyb.20150474
小波－神经网络混合模型预测地下水水位
A Wvaeel t-ANN Hybrid Model for Groundwaet r Level Forecasting
摘要
Abstract
Due to over-exploitation of groundwater in many cities of North China Plain , there is a tendency of lasting decrease in groundwater level , which results in serious problems , such as groundwater exhaustion , land subsidence and seawater intrusion .In order to accurately predict changes of urban groundwater level , based on artificial neural network (ANN) and analysis of multi-scale of wavelet transform (WT), we established a wavelet-ANN conjugate model and test its accuracy to predict groundwater level .Measured data of groundwater level at Pinggu district of Beijing were taken as research objects .We predicted groundwater levels at the district by back propagation ( BP ) model and hybrid model .Then , we calculated the prediction accuracy by using statistical parameters including root mean square error ( RMSE) , mean absolute error ( MAE) and correlation coefficient ( R) .Results showed that the MAE of the hybrid model from the first month to the third month was 0.535, 0.598 and 0.634 m, respectively, whereas 0.566, 0.824 and 0.940 m for BP model.The MAE of hybrid model from the first month to the third month was 95%, 73% and 67%of that of BP model , respectively .Comparison of results reveals that the hybrid model has advantages of better prediction accuracy and longer effective prediction duration .关键词
华北平原/过量开采/地下水水位/离散小波变换/人工神经网络/预测Key words
North China Plain/over-exploitation/groundwater level/discrete wavelet transform/artificial neural network/forecasting分类
天文与地球科学引用本文复制引用
张建锋,刘见宝,崔树军,谢玉华..小波－神经网络混合模型预测地下水水位[J].长江科学院院报,2016,33(8):18-21,4.基金项目
国家自然基金青年基金项目（41206037）；河南省教育厅科技攻关项目（14B170011）；郑州市科技发展计划项目（131PPTGG414－7）；河南工程学院博士基金项目 （）
