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Prediction of Groundwater Level Using the Hybrid Model Combining Wavelet Transform and Machine Learning Algorithms by Aihua Wei; Yuanyao Chen; Duo Li; Xianfu Zhang; Tao Wu; Hui Li is a Environmental Science article available to read on EtoBox.

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The main objective of this study is to predict groundwater levels (GWLs) using modified hybrid algorithms with two levels of improvement. The observed GWLs, precipitation, and temperature were used as input variables in the prediction algorithms. Two widely used machine learning algorithms, namely support vector machine (SVM) and random forest (RF), were used first as the base algorithm, then the wavelet transform (WT), with seven wavelet types, was employed as a preprocessing method. SVM and RF combined WT, namely W-SVM and W-RF, respectively, constructed the first-level hybrid algorithm, taking all of the components together. In addition, the approximation and detail components were separately used to construct the second-level hybrid algorithm, coupling SVM and RF with WT, namely W-SVM-D and W-RF-D, respectively. Four statistical metrics were used to evaluate and validate the predictive accuracies of algorithms. According to the obtained results, The W-SVM-D and W-RF-D hybrid algorithms demonstrated the highest predictive accuracies of GWLs, followed by the first-level hybrid and single algorithms. In addition, the mother wavelet types affected the prediction accuracy of W-SVM a

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Author

Aihua Wei; Yuanyao Chen; Duo Li; Xianfu Zhang; Tao Wu; Hui Li

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Environmental Science (Physical Sciences)