Abstract：
Groundwater, the world\'s most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Accurate forecasting of groundwater level (GWL) is essential for effective management of this vital resource, but it remains a complex and challenging task. In recent years, there has been a notable increase in the use of machine learning (ML) techniques to model GWL, with many studies reporting exceptional results. In this paper, we present a comprehensive review of 142 relevant articles indexed by the Web of Science from 2017 to 2023, focusing on key ML models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), evolutionary computing (EC), deep learning (DL), ensemble learning (EN), and hybrid-modeling (HM). We also discussed key modeling concepts such as dataset size, data splitting, input variable selection, forecasting time-step, performance metrics (PM), study zones, and aquifers, highlighting best practices for optimal GWL forecasting with ML. This review provides valuable insights and recommendations for researchers and water management agencies working in the field of groundwater management and hydrology.
摘要：
地下水,世界上最丰富的淡水来源，由于各种因素，许多地区正在迅速枯竭。准确预测地下水位（GWL）对于有效管理这一重要资源至关重要，但这仍然是一项复杂而具有挑战性的任务。近年来,使用机器学习（ML）技术对GWL进行建模的情况显着增加，许多研究报告了异常的结果。在本文中,我们对2017年至2023年由WebofScience索引的142篇相关文章进行了全面回顾，重点关注关键的ML模型，包括人工神经网络(ANN)，自适应神经模糊推理系统(ANFIS)，支持向量回归(SVR)，进化计算(EC)，深度学习(DL)，合奏学习（EN），和混合建模(HM)。我们还讨论了关键的建模概念，如数据集大小、数据拆分,输入变量选择，预测时间步长，性能指标(PM)，研究区,和含水层，突出使用ML进行最佳GWL预测的最佳实践。这篇评论为地下水管理和水文学领域的研究人员和水管理机构提供了宝贵的见解和建议。
