Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms.

• Machine learning models coupled with wavelet transforms for GWL forecasting. • eXtreme Gradient Boosting, Random Forests, and Support Vector Regression explored. • Bayesian optimization automatically selects XGB, RF, and SVR hyper-parameters. • XGB and RF inherently perform high dimensional input variable selection. • New hybrid approaches (WT-XGB and WT-RF) are more accurate than standalone models. Groundwater level (GWL) forecasting is crucial for irrigation scheduling, water supply and land development. Machine learning (ML) (e.g. , artificial neural networks) has been increasingly adopted to forecast GWL due to its ability to model nonlinearities between GWL and its drivers (e.g. , rainfall). Although ML approaches have been successful at forecasting GWL, they are often inaccurate when GWL exhibits multiscale changes (e.g. , due to urbanization). To address this shortcoming, wavelet transforms (WT) are routinely coupled with ML methods. Unfortunately, researchers frequently neglect key issues associated with WT that render such forecasts useless for real-world scenarios. This study demonstrates how new ML methods, such as eXtreme Gradient Boosting and Random Forests, can be properly coupled with WT to generate accurate GWL forecasts (1-–3 months ahead) for 7 wells in Kumamoto City in Southern Japan that can be used to help address current pressing issues such as groundwater quality and land subsidence. [ABSTRACT FROM AUTHOR]