Assessment of the vulnerability of hybrid coastal aquifers: application of multi-attribute decision-making and optimization models-Reference-Cited by-同舟云学术

Assessment of the vulnerability of hybrid coastal aquifers: application of multi-attribute decision-making and optimization models

Published:2023-05-12 Issue:8 Volume:68 Page:1095-1108
ISSN:0262-6667
Container-title:Hydrological Sciences Journal
language:en
Short-container-title:Hydrological Sciences Journal

Author:

Bordbar Mojgan¹²^ORCID,Nikoo Mohammad Reza³^ORCID,Sana Ahmad³^ORCID,Nematollahi Banafsheh⁴^ORCID,Al-Rawas Ghazi³^ORCID,Gandomi Amir H.⁵⁶^ORCID

Affiliation:

1. Department of Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran

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

3. Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman

4. Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran

5. Faculty of Engineering & Information Technology, University of Technology Sydney, Ultimo, Australia

6. University Research and Innovation Center (EKIK), Óbuda University, Budapest, Hungary

Publisher

Informa UK Limited

Subject

Water Science and Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/02626667.2023.2203825

Reference49 articles.

1. Groundwater recharge dams in arid areas as tools for aquifer replenishment and mitigating seawater intrusion: example of AlKhod, Oman

2. Investigating effects of climate change, urbanization, and sea level changes on groundwater resources in a coastal aquifer: an integrated assessment

3. Application of groundwater flow model in assessing aquifer layers interaction in arid catchment area

4. Mapping of the vulnerability to marine intrusion “in coastal Cherchell aquifer, Central Algeria” using the GALDIT method

5. Improving GALDIT-based groundwater vulnerability predictive mapping using coupled resampling algorithms and machine learning models

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1. Comparative study for coastal aquifer vulnerability assessment using deep learning and metaheuristic algorithms;Environmental Science and Pollution Research;2024-03-04

2. Global Review of Modification, Optimization, and Improvement Models for Aquifer Vulnerability Assessment in the Era of Climate Change;Current Climate Change Reports;2024-01-06

3. A new approach for assessing the assembled vulnerability of coastal aquifers based on optimization models;Journal of Hydrology;2023-10