Comparative study for coastal aquifer vulnerability assessment using deep learning and metaheuristic algorithms
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bordbar, Mojgan | - |
dc.contributor.author | Heggy, Essam | - |
dc.contributor.author | Jun, Changhyun | - |
dc.contributor.author | Bateni, Sayed M. | - |
dc.contributor.author | Kim, Dongkyun | - |
dc.contributor.author | Moghaddam, Hamid Kardan | - |
dc.contributor.author | Rezaie, Fatemeh | - |
dc.date.accessioned | 2024-04-16T05:00:33Z | - |
dc.date.available | 2024-04-16T05:00:33Z | - |
dc.date.issued | 2024-03-04 | - |
dc.identifier.issn | 0944-1344 | - |
dc.identifier.issn | 1614-7499 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/32998 | - |
dc.description.abstract | Coastal aquifer vulnerability assessment (CAVA) studies are essential for mitigating the effects of seawater intrusion (SWI) worldwide. In this research, the vulnerability of the coastal aquifer in the Lahijan region of northwest Iran was investigated. A vulnerability map (VM) was created applying hydrogeological parameters derived from the original GALDIT model (OGM). The significance of OGM parameters was assessed using the mean decrease accuracy (MDA) method, with the current state of SWI emerging as the most crucial factor for evaluating vulnerability. To optimize GALDIT weights, we introduced the biogeography-based optimization (BBO) and gray wolf optimization (GWO) techniques to obtain to hybrid OGM-BBO and OGM-GWO models, respectively. Despite considerable research focused on enhancing CAVA models, efforts to modify the weights and rates of OGM parameters by incorporating deep learning algorithms remain scarce. Hence, a convolutional neural network (CNN) algorithm was applied to produce the VM. The area under the receiver-operating characteristic curves for OGM-BBO, OGM-GWO, and VMCNN were 0.794, 0.835, and 0.982, respectively. According to the CNN-based VM, 41% of the aquifer displayed very high and high vulnerability to SWI, concentrated primarily along the coastline. Additionally, 32% of the aquifer exhibited very low and low vulnerability to SWI, predominantly in the southern and southwestern regions. The proposed model can be extended to evaluate the vulnerability of various coastal aquifers to SWI, thereby assisting land use planers and policymakers in identifying at-risk areas. Moreover, deep-learning-based approaches can help clarify the associations between aquifer vulnerability and contamination resulting from SWI. | - |
dc.format.extent | 15 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | SPRINGER HEIDELBERG | - |
dc.title | Comparative study for coastal aquifer vulnerability assessment using deep learning and metaheuristic algorithms | - |
dc.type | Article | - |
dc.publisher.location | 독일 | - |
dc.identifier.doi | 10.1007/s11356-024-32706-2 | - |
dc.identifier.scopusid | 2-s2.0-85186552857 | - |
dc.identifier.wosid | 001176026000021 | - |
dc.identifier.bibliographicCitation | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, v.31, no.16, pp 24235 - 24249 | - |
dc.citation.title | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH | - |
dc.citation.volume | 31 | - |
dc.citation.number | 16 | - |
dc.citation.startPage | 24235 | - |
dc.citation.endPage | 24249 | - |
dc.type.docType | Article; Early Access | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.subject.keywordPlus | GREY WOLF OPTIMIZER | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordAuthor | Seawater intrusion | - |
dc.subject.keywordAuthor | Vulnerability | - |
dc.subject.keywordAuthor | Convolutional neural network | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | GALDIT | - |
dc.subject.keywordAuthor | Optimize weights | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
94, Wausan-ro, Mapo-gu, Seoul, 04066, Korea02-320-1314
COPYRIGHT 2020 HONGIK UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.