分析了塔里木盆地南缘和田绿洲洛浦灌区地下水位较高的成因,研究了灌区地下水位年内的动态变化规律,利用小波分析的多分辨率功能和人工神经网络的非线性逼近功能,建立了基于小波变换和BP神经网络的地下水埋深动态预测小波神经网络模型。结果表明:灌区地下水位变化具有周期性、季节性的特点,并且可以被小波神经网络模型准确地模拟,模型预测洛浦灌区在未来几年内地下水位会持续上升,年平均升幅为0.1 m左右,因此当地应加强地下水的科学管理。 The causes of the high groundwater level in the Hepu Oasis Irrigation District in the south of the Tarim Basin were analyzed. The dynamic variation of the groundwater level in the irrigation area during the year was studied. Based on the multi-resolution function of the wavelet analysis and the nonlinear approximation function of the artificial neural network, Wavelet transform and BP neural network dynamic prediction of groundwater depth using wavelet neural network model. The results show that the variation of groundwater level in irrigation area is cyclical and seasonal, and can be accurately simulated by wavelet neural network model. The model predicts groundwater level will continue to rise in the next few years in Luopu Irrigation District with annual average increase of about 0.1 m, Therefore, the local should strengthen the scientific management of groundwater.