Nadiri Ata Allah, Bordbar Mojgan, Nikoo Mohammad Reza, Silabi Leila Sadat Seyyed, Senapathi Venkatramanan, Xiao Yong
Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran; Medical Geology and Environment Research Center, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran; Institute of Environment, University of Tabriz, Tabriz, East Azerbaijan, Iran; Traditional Medicine and Hydrotherapy Research Center, Ardabil University of Medical Sciences, Ardabil, Iran.
University of Campania "Luigi Vanvitelli", Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Caserta, Italy.
Mar Pollut Bull. 2023 Dec;197:115669. doi: 10.1016/j.marpolbul.2023.115669. Epub 2023 Nov 2.
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.
本研究考察了伊朗西北部希拉明地区沿海含水层对海水入侵（SWI）的脆弱性。在此，利用传统GALDIT框架（TGF）中现有的六种水文地质数据层构建了一张脆弱性地图。此外，通过从GALDIT模型中剔除含水层类型数据层并添加含水层介质和井密度数据层，制备了一种改进的传统GALDIT框架（mod-TGF）。据我们所知，在利用深度学习算法改进TGF方面存在研究空白。因此，本研究采用卷积神经网络（CNN）作为一种新的深度学习算法来改进mod-TGF框架，以评估沿海含水层的脆弱性。基于研究结果，CNN模型可将mod-TGF的性能提高30%以上。本研究可为进一步的含水层脆弱性研究提供参考。
