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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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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.

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Data Availability

The data is available from the corresponding author upon reasonable request.

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Acknowledgements

This research was supported by the Basic Research Laboratory Program (Grant Number: 2022R1A4A3032838) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT.

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Authors and Affiliations

  1. Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania “Luigi Vanvitelli”, Caserta, Italy

    Mojgan Bordbar & Gianluigi Busico

  2. Geoscience Data Center, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-Ro, Yuseong-Gu, Daejeon, 34132, Republic of Korea

    Fatemeh Rezaie

  3. Department of Geophysical Exploration, Korea University of Science and Technology, 217, Gajeong-Ro, Yuseong-Gu, Daejeon, 34113, Republic of Korea

    Fatemeh Rezaie

  4. Department of Civil, Environmental, and Construction Engineering and Water Resources Research Center, University of Hawai‘I at Manoa, Honolulu, HI, 96822, USA

    Fatemeh Rezaie, Sayed M. Bateni & Mahdi Panahi

  5. Department of Civil and Environmental Engineering, College of Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea

    Changhyun Jun

  6. Department of Civil and Environmental Engineering, Hongik University, Mapo-Gu, Seoul, Republic of Korea

    Dongkyun Kim

  7. Department of Water Resources Research, Water Research Institute, Tehran, Iran

    Hamid Kardan Moghaddam

  8. Department of GIS/RS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Sina Paryani

  9. Department of Engineering and Engineering Technology, Metropolitan State University of Denver, Denver, CO, 80217, USA

    Mohammad Valipour

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Bordbar, M., Rezaie, F., Bateni, S.M. et al. Global Review of Modification, Optimization, and Improvement Models for Aquifer Vulnerability Assessment in the Era of Climate Change. Curr Clim Change Rep 9, 45–67 (2023). https://doi.org/10.1007/s40641-023-00192-2

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