TY - JOUR
T1 - Assessing vulnerability of coastal aquifer to seawater intrusion using Convolutional Neural Network
AU - Nadiri, Ata Allah
AU - Bordbar, Mojgan
AU - Nikoo, Mohammad Reza
AU - Silabi, Leila Sadat Seyyed
AU - Senapathi, Venkatramanan
AU - Xiao, Yong
N1 - Copyright © 2023. Published by Elsevier Ltd.
PY - 2023/12/1
Y1 - 2023/12/1
N2 - This study examined coastal aquifer vulnerability to seawater intrusion (SWI) in the Shiramin area in northwest Iran. Here, six types of hydrogeological data layers existing in the traditional GALDIT framework (TGF) were used to build one vulnerability map. Moreover, a modified traditional GALDIT framework (mod-TGF) was prepared by eliminating the data layer of aquifer type from the GALDIT model and adding the data layers of aquifer media and well density. To the best of our knowledge, there is a research gap to improve the TGF using deep learning algorithms. Therefore, this research adopted the Convolutional Neural Network (CNN) as a new deep learning algorithm to improve the mod-TGF framework for assessing the coastal aquifer vulnerability. Based on the findings, the CNN model could increase the performance of the mod-TGF by >30 %. This research can be a reference for further aquifer vulnerability studies.
AB - This study examined coastal aquifer vulnerability to seawater intrusion (SWI) in the Shiramin area in northwest Iran. Here, six types of hydrogeological data layers existing in the traditional GALDIT framework (TGF) were used to build one vulnerability map. Moreover, a modified traditional GALDIT framework (mod-TGF) was prepared by eliminating the data layer of aquifer type from the GALDIT model and adding the data layers of aquifer media and well density. To the best of our knowledge, there is a research gap to improve the TGF using deep learning algorithms. Therefore, this research adopted the Convolutional Neural Network (CNN) as a new deep learning algorithm to improve the mod-TGF framework for assessing the coastal aquifer vulnerability. Based on the findings, the CNN model could increase the performance of the mod-TGF by >30 %. This research can be a reference for further aquifer vulnerability studies.
KW - Coastal aquifer
KW - Convolutional Neural Network (CNN)
KW - Deep learning
KW - GALDIT
KW - Seawater intrusion (SWI)
KW - Vulnerability
UR - http://www.scopus.com/inward/record.url?scp=85175257480&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85175257480&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/efcd8197-baba-39f9-884e-6c475e4358d7/
U2 - 10.1016/j.marpolbul.2023.115669
DO - 10.1016/j.marpolbul.2023.115669
M3 - Article
C2 - 37922752
AN - SCOPUS:85175257480
SN - 0025-326X
VL - 197
SP - 115669
JO - Marine Pollution Bulletin
JF - Marine Pollution Bulletin
M1 - 115669
ER -