Water Supply (Apr 2024)
Improved prediction of groundwater depth in urban areas using the EWT-S–G-GRU and LSSVM model: a case study of Xinxiang City
Abstract
The improvement in predicting groundwater depth accuracy has significant implications for the management, ecological environment protection, and economic and social development of regional water resources. Employing empirical wavelet transform (EWT) for nonlinear processing, Savitzky–Golay (S–G) filtering to reduce high-frequency noise, gate recurrent unit (GRU) neural network for linear feature signal processing, and least squares support vector machine (LSSVM) for nonlinear signal handling, we established a comprehensive model combining EWT-S–G-GRU and LSSVM. The results demonstrate that the proposed model exhibits superior accuracy in groundwater depth prediction, with an average relative error of only 2.14% and Nash–Sutcliffe efficiency (NSE) of 0.93. This outperforms the other four models, with average relative errors of 12.88%, 11.90%, 7.07%, and 11.10%, and NSE values of 0.58, 0.56, 0.71, and 0.73, respectively. The superiority of the model established in this study is attributed to its effective handling of both nonlinear and linear features of groundwater, thereby enhancing predictive accuracy. The EWT-S–G-GRU and LSSVM model proves to be more reliable in revealing the spatial distribution and dynamic changes of future groundwater, providing a robust reference for the rational development and utilization of groundwater in the urban area of Xinxiang City. HIGHLIGHTS Through empirical wavelet transform decomposition, Savitzky–Golay filtering and noise reduction process reduces signal fluctuation.; Separate the high-frequency and low-frequency components.; The spatial distribution of groundwater in the future and its dynamic change with time are revealed.; Compared with other models, the advantages of this model are clarified.; The coupling of multiple models improves the prediction accuracy.;
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