Abstract This study is a review to the special issue on artificial intelligence (AI) methods for groundwater level (GWL) modeling and forecasting, and presents a brief overview of the most popular AI techniques, along with the bibliographic reviews of the experiences of the authors over past years, and the reviewing and comparison of the obtained results. Accordingly, 67 journal papers published from 2001 to 2018 were reviewed in the terms of the features and abilities of the modeling approaches, input data consideration, prediction time steps, data division, etc. From the reviewed papers it can be concluded that despite some weaknesses, if the AI methods properly be developed, they can successfully be used to simulate and forecast the GWL time series in different aquifers. Since some of the stages of the AI modeling are based on the experience or trial-and-error procedures, it is useful to review them in the special application on GWL modeling. Many partial and general results were achieved from the reviewed papers, which can provide applicable guidelines for researchers who want to perform similar works in this field. Several new ideas in the related area of research are also presented in this study for developing innovative methods and for improving the quality of the modeling.

中文翻译：

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地下水位建模中的人工智能方法综述

摘要 本研究是对地下水位 (GWL) 建模和预测的人工智能 (AI) 方法特刊的回顾，并简要概述了最流行的人工智能技术，以及作者经验的书目评论。过去几年，以及对所获得结果的回顾和比较。相应地，从建模方法的特点和能力、输入数据考虑、预测时间步长、数据划分等方面审查了 2001 年至 2018 年发表的 67 篇期刊论文。 从审查的论文中可以得出结论，尽管存在一些弱点, 如果 AI 方法开发得当，它们可以成功地用于模拟和预测不同含水层的 GWL 时间序列。由于 AI 建模的某些阶段基于经验或试错程序，因此在 GWL 建模的特殊应用程序中查看它们是有用的。从审查的论文中取得了许多部分和一般的结果，可以为希望在该领域进行类似工作的研究人员提供适用的指导。本研究还提出了相关研究领域的一些新想法，用于开发创新方法和提高建模质量。