Combining Group Method of Data Handling with Signal Processing Approaches to Improve Accuracy of Groundwater Level Modeling

Abstract

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.