{"title":"Global Review of Modification, Optimization, and Improvement Models for Aquifer Vulnerability Assessment in the Era of Climate Change","authors":"","doi":"10.1007/s40641-023-00192-2","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <span> <h3>Purpose of Review</h3> <p>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.</p> </span> <span> <h3>Recent findings</h3> <p>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.</p> </span> <span> <h3>Summary</h3> <p>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.</p> </span>","PeriodicalId":54235,"journal":{"name":"Current climate change reports","volume":"53 1","pages":""},"PeriodicalIF":9.3000,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current climate change reports","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s40641-023-00192-2","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍：
Current Climate Change Reports is dedicated to exploring the most recent research and policy issues in the dynamically evolving field of Climate Change. The journal covers a broad spectrum of topics, encompassing Ecological Impacts, Advances in Modeling, Sea Level Projections, Extreme Events, Climate Feedback and Sensitivity, Hydrologic Impact, Effects on Human Health, and Economics and Policy Issues. Expert contributors provide reviews on the latest research, assess the effectiveness of available options, and engage in discussions about special considerations. All articles undergo a thorough peer-review process by specialists in the field to ensure accuracy and objectivity.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
