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Global Review of Modification, Optimization, and Improvement Models for Aquifer Vulnerability Assessment in the Era of Climate Change
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DOI：10.1007/s40641-023-00192-2.png)
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Abstract
En 中文
Purpose of ReviewThis 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 findingsThe 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.SummaryThe 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.
Keywords:
Aquifer vulnerability assessment
Index-overlay methods
Machine learning
Deep learning
Optimization
Climate change
Journal
IF:
10.3
Papers:
202
Citations:
2.5K
Organization
Cited Papers