Purpose of Review

This review aims to examine the methods used to date in assessing aquifer vulnerability over the last three decades (1993-2023). In addition to a comprehensive review of prior AVA research, the novelty of this study lies in its specific focus on these methods and their application to the widely used DRASTIC and GALDIT models. We particularly emphasize statistical analysis, multicriteria decision-making, optimization techniques, machine learning algorithms, and deep learning (DL) models.

Recent findings

The most widely used modification, optimization, and improvement-based methods for DRASTIC indices are the analytic hierarchy process, genetic algorithm, and fuzzy logic. In contrast, single-parameter sensitivity analysis, genetic algorithm, and support vector machine are commonly applied to modify, optimize, and improve GALDIT indices.

Summary

The results of this study are important especially in the era of global warming and climate change/variability when the need and demand for aquifers and groundwater resources is increasing.

中文翻译：

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气候变化时代含水层脆弱性评估修改、优化和改进模型的全球审查

审查目的

本综述旨在研究过去三十年（1993-2023）迄今为止用于评估含水层脆弱性的方法。除了对之前的 AVA 研究进行全面回顾之外，本研究的新颖性还在于它特别关注这些方法及其在广泛使用的 DRASTIC 和 GALDIT 模型中的应用。我们特别强调统计分析、多标准决策、优化技术、机器学习算法和深度学习（DL）模型。

最近的发现

DRASTIC 指数最广泛使用的修改、优化和改进方法是层次分析法、遗传算法和模糊逻辑。相比之下，单参数敏感性分析、遗传算法和支持向量机通常用于修改、优化和改进 GALDIT 指数。

概括

这项研究的结果非常重要，尤其是在全球变暖和气候变化/变化的时代，对含水层和地下水资源的需求不断增加。