Groundwater level forecasting is a paramount necessity for integrated management of a basin. Development of suitable models is an essential step in determining groundwater level fluctuations in the future. The main objective of this study was to provide a powerful hybrid model by combining the group method of data handling (GMDH) and certain signal processing techniques, i.e., ensemble empirical mode decomposition (EEMD), wavelet transform (WT) and wavelet packet transform (WPT) for groundwater level forecasting in monthly time steps. Two different plains were selected to assess the performance of the afore-mentioned methods. The results showed that all of these preprocessing methods improved the capability of the group method of data handling model. The EEMD–GMDH model outperformed the WT–GMDH model. The WPT–GMDH model had a superior performance compared to both of the afore-mentioned hybrid models. However, it was shown that WPT–GMDH model had more computational cost that may affect the feasibility of this modeling approach in some cases. Finally, the EEMD–GMDH model can be introduced as a suitable modeling approach for groundwater level forecasting.

地下水位预报对于流域综合管理至关重要。开发合适的模型是确定未来地下水位波动的重要步骤。这项研究的主要目的是通过结合数据处理的分组方法（GMDH）和某些信号处理技术（即集成经验模式分解（EEMD），小波变换（WT）和小波包变换（ WPT），以每月时间间隔进行地下水位预测。选择了两个不同的平原来评估上述方法的性能。结果表明，所有这些预处理方法均提高了数据处理模型分组方法的能力。EEMD–GMDH模型优于WT–GMDH模型。与上述两种混合模型相比，WPT-GMDH模型具有更好的性能。但是，结果表明，WPT-GMDH模型的计算成本更高，在某些情况下可能会影响该建模方法的可行性。最后，可以引入EEMD–GMDH模型作为地下水位预测的合适建模方法。