Groundwater modelling and GIS-based vulnerability mapping coupled with evolutionary heuristic optimization in the eastern coast of Saudi Arabia

Groundwater modelling and GIS-based vulnerability mapping coupled with evolutionary heuristic optimization in the eastern coast of Saudi Arabia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Groundwater modelling and GIS-based vulnerability mapping coupled with evolutionary heuristic optimization in the eastern coast of Saudi Arabia S. I. Abba, Mohammed Benaafi, A. G. Usman, Dilber Uzun Ozsahin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4884446/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Dec, 2024 Read the published version in Earth Science Informatics → Version 1 posted 12 You are reading this latest preprint version Abstract Developing an efficient and reliable intelligent approach to the available groundwater (GW) resources appears crucial for achieving Saudi Vision 2030 on the availability of freshwater resources, the prosperity of people, and economic development. The present study is based on a real-field investigation and experimental analysis using ion chromatography (IC) and inductively coupled plasma mass spectrometry (ICP-MS). Subsequently, ArcGIS 10.3 software and artificial intelligence (AI)-based metaheuristic optimization (MO) were used to create vulnerability maps and a modelling schema for the potassium (K + ) and sodium (Na + ) in the coastal region of eastern Saudi Arabia, respectively. For this purpose, extreme gradient boosting (XG-Boost) was used as a standalone model while differential evolution (DE) and firefly algorithms (FA) as optimization techniques. The results were validated using different statistical indices and graphical visualization. The optimal objective function for each data set through multiple iterations based on the root means square error (RMSE) index and the number of features was done using DE algorithms. The performance results of the optimized XGBoost algorithm (DE-XGBoost and FA-XGBoost) and the XGBoost algorithm indicated that FA algorithms outperformed merit with high accuracy for both K + and Na + . The numerical comparison depicted that the mean absolute error (MAE) for K + and Na + FA-XGBoost was 0.0173 and 0.028, respectively. The results showed that the FA-XGBoost method produced more accurate K + and Na + prediction GIS-maps than the other two algorithms. Hence, the current results justified the potential use of the intelligent tool for water resources management. artificial intelligence groundwater coastal aquifer GIS water resources Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Dec, 2024 Read the published version in Earth Science Informatics → Version 1 posted Editorial decision: Revision requested 08 Sep, 2024 Reviews received at journal 07 Sep, 2024 Reviews received at journal 03 Sep, 2024 Reviewers agreed at journal 24 Aug, 2024 Reviewers agreed at journal 17 Aug, 2024 Reviewers agreed at journal 17 Aug, 2024 Reviewers agreed at journal 17 Aug, 2024 Reviewers agreed at journal 16 Aug, 2024 Reviewers invited by journal 16 Aug, 2024 Editor assigned by journal 16 Aug, 2024 Submission checks completed at journal 13 Aug, 2024 First submitted to journal 09 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4884446","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":350968597,"identity":"da003098-bde6-4508-a4dc-1fad89690e22","order_by":0,"name":"S. I. 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The present study is based on a real-field investigation and experimental analysis using ion chromatography (IC) and inductively coupled plasma mass spectrometry (ICP-MS). Subsequently, ArcGIS 10.3 software and artificial intelligence (AI)-based metaheuristic optimization (MO) were used to create vulnerability maps and a modelling schema for the potassium (K\u003csup\u003e+\u003c/sup\u003e) and sodium (Na\u003csup\u003e+\u003c/sup\u003e) in the coastal region of eastern Saudi Arabia, respectively. For this purpose, extreme gradient boosting (XG-Boost) was used as a standalone model while differential evolution (DE) and firefly algorithms (FA) as optimization techniques. The results were validated using different statistical indices and graphical visualization. The optimal objective function for each data set through multiple iterations based on the root means square error (RMSE) index and the number of features was done using DE algorithms. The performance results of the optimized XGBoost algorithm (DE-XGBoost and FA-XGBoost) and the XGBoost algorithm indicated that FA algorithms outperformed merit with high accuracy for both K\u003csup\u003e+\u003c/sup\u003e and Na\u003csup\u003e+\u003c/sup\u003e. The numerical comparison depicted that the mean absolute error (MAE) for K\u003csup\u003e+\u003c/sup\u003e and Na\u003csup\u003e+\u003c/sup\u003e FA-XGBoost was 0.0173 and 0.028, respectively. The results showed that the FA-XGBoost method produced more accurate K\u003csup\u003e+\u003c/sup\u003e and Na\u003csup\u003e+\u003c/sup\u003e prediction GIS-maps than the other two algorithms. 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