由于过量开采地下水，华北平原的许多城市出现地下水水位持续下降趋势，由此导致了许多严重的环境问题，如地下水枯竭、地面沉降和海水入侵等。为了准确预测城市地下水水位变化，利用小波变换的多尺度分析特征，建立了小波-神经网络混合模型(以下简称“混合模型”)，并研究了其在地下水水位预测中的精度。利用北京市平谷区地下水水位观测资料，分别用BP网络和混合模型对该区地下水水位进行了预测。采用均方根误差(RMSE)、平均绝对误差(MAE)和线性相关系数(R)对模型预测的精度进行度量。预测结果表明混合模型第1至第3个月的地下水水位平均绝对误差分别是0.535，0.598和0.634 m;而BP模型的平均绝对误差分别为0.566，0.824和0.940 m。混合模型的预测误差分别为BP模型的95%，73%和67%。使用混合模型能明显提高预测的精度，显著增加有效预测时段长度。

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.