Abstract 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.

中文翻译：

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多尺度地下水位预测：将新机器学习方法与小波变换相结合

摘要 地下水位 (GWL) 预测对于灌溉调度、供水和土地开发至关重要。由于机器学习 (ML)（例如人工神经网络）能够对 GWL 与其驱动因素（例如降雨）之间的非线性进行建模，因此它越来越多地用于预测 GWL。尽管 ML 方法在预测 GWL 方面取得了成功，但当 GWL 表现出多尺度变化（例如，由于城市化）时，它们往往不准确。为了解决这个缺点，小波变换 (WT) 通常与 ML 方法结合使用。不幸的是，研究人员经常忽视与 WT 相关的关键问题，这些问题使此类预测对现实世界的场景无用。这项研究展示了新的 ML 方法，例如 eXtreme Gradient Boosting 和 Random Forests，