关键词: 小波转换, BP神经网络模型, 小波神经网络模型, 自回归移动平均模型, 人工神经网络, 地下水埋深, 预测, 吉林西部

Abstract:

Accurate and reliable groundwater depth forecasting model is important to ecological environment protection and water resource planning and management. To minimize the interference of the nonlinear and complicated kinetic changes in the shallow water depth forecasting in western Jilin, a model for prediction is established based on the combination of wavelet analysis and artificial neural network,the wavelet network (WA-ANN) model: The parameters inputted in the models are monthly precipitation, evaporation, labor exploitation, and pre-monthly groundwater depth recorded from January 2002 to December 2009; and output was monthly groundwater depth in the study area. A comparison is made to BP artificial neural network (BP-ANN) model and autoregressive integrated moving average (ARIMA) model. The results show that ARIMA model processes more simple, but WA-ANN model predicted more accurate than both the BP-ANN and ARIMA models. In conclusion, the wavelet neural network model is more applicable for monthly average shallow groundwater depth forecasting.

Key words: wavelet transforms, BP-ANN model, WA-ANN model, ARIMA model, artificial neural networks, groundwater depth, forecasting, westorn Jilin