Modelling the Isotope composition of groundwater using hydrochemical properties in eastern Saudi Arabia: Implementation of innovative data intelligence techniques
Study region: Al-Qatif coastal region in eastern Saudi Arabia is an arid region with limited surface water resources and vulnerable to seawater intrusion. Study focus: The study focused on modelling and prediction of the isotope composition (δ¹⁸O and δ²H) of coastal groundwater using Artificial Inte...
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| Language: | English |
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Elsevier
2025-02-01
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| Series: | Journal of Hydrology: Regional Studies |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581824004877 |
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| author | Mohammed Benaafi Waleed M. Hamanah Ebrahim Al-Wajih |
| author_facet | Mohammed Benaafi Waleed M. Hamanah Ebrahim Al-Wajih |
| author_sort | Mohammed Benaafi |
| collection | DOAJ |
| description | Study region: Al-Qatif coastal region in eastern Saudi Arabia is an arid region with limited surface water resources and vulnerable to seawater intrusion. Study focus: The study focused on modelling and prediction of the isotope composition (δ¹⁸O and δ²H) of coastal groundwater using Artificial Intelligence (AI) models utilizing readily available groundwater hydrochemical dataset. The study aims to understand the geochemical evolution of groundwater and the impact of seawater intrusion on arid coastal environment. New hydrological insight for the region: Eight AI algorithms (KNN, SVR, RF, ET, Bag, AdaBt, GRB, and CAT) and stacking ensemble models were developed to predict the δ¹ ⁸O and δ²H isotopes of the groundwater using a dataset of physicochemical parameters, ions and elements, and isotopes from 47 wells. The study shows that the stacking ensemble models outperformed individual algorithms. The optimum model for δ¹ ⁸O (O_M1) was achieved with R² of 0.9858, MAE of 0.0440, and Pearson correlation of 0.9941. for δ²H the optimal model (H_M1) was achieved with R² of 0.9317, MAE of 0.5334, and Pearson correlation of 0.9658. The significant relationship between hydrochemical parameters and isotopic composition indicate that the variation in groundwater chemistry is mostly associated with mixing processes, primarily driven by seawater intrusion in the coastal region. The study demonstrates the potential of AI-based models to predict the isotopic signature and groundwater dynamics in similar coastal arid environments. |
| format | Article |
| id | doaj-art-10e3efee047140d2a4dcc5580b6ec5c2 |
| institution | Kabale University |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-10e3efee047140d2a4dcc5580b6ec5c22025-01-22T05:42:11ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-02-0157102138Modelling the Isotope composition of groundwater using hydrochemical properties in eastern Saudi Arabia: Implementation of innovative data intelligence techniquesMohammed Benaafi0Waleed M. Hamanah1Ebrahim Al-Wajih2Interdisciplinary Research Center for Membranes and Water Security, KFUPM, Dhahran 31261, Saudi Arabia; Department of Geosciences, College of Petroleum Engineering & Geosciences, KFUPM, Dhahran 31261, Saudi ArabiaApplied Research Center for Metrology, Standards, and Testing, Research and Innovation, KFUPM, Dhahran 31261, Saudi Arabia; Department of Electrical Engineering, College of Engineering and Physics, KFUPM, Dhahran 31261, Saudi ArabiaUniversity Schools, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia; Corresponding author.Study region: Al-Qatif coastal region in eastern Saudi Arabia is an arid region with limited surface water resources and vulnerable to seawater intrusion. Study focus: The study focused on modelling and prediction of the isotope composition (δ¹⁸O and δ²H) of coastal groundwater using Artificial Intelligence (AI) models utilizing readily available groundwater hydrochemical dataset. The study aims to understand the geochemical evolution of groundwater and the impact of seawater intrusion on arid coastal environment. New hydrological insight for the region: Eight AI algorithms (KNN, SVR, RF, ET, Bag, AdaBt, GRB, and CAT) and stacking ensemble models were developed to predict the δ¹ ⁸O and δ²H isotopes of the groundwater using a dataset of physicochemical parameters, ions and elements, and isotopes from 47 wells. The study shows that the stacking ensemble models outperformed individual algorithms. The optimum model for δ¹ ⁸O (O_M1) was achieved with R² of 0.9858, MAE of 0.0440, and Pearson correlation of 0.9941. for δ²H the optimal model (H_M1) was achieved with R² of 0.9317, MAE of 0.5334, and Pearson correlation of 0.9658. The significant relationship between hydrochemical parameters and isotopic composition indicate that the variation in groundwater chemistry is mostly associated with mixing processes, primarily driven by seawater intrusion in the coastal region. The study demonstrates the potential of AI-based models to predict the isotopic signature and groundwater dynamics in similar coastal arid environments.http://www.sciencedirect.com/science/article/pii/S2214581824004877Water scarcityAquifer depletionContaminationGroundwater sustainabilityMachine learningEnsemble learning |
| spellingShingle | Mohammed Benaafi Waleed M. Hamanah Ebrahim Al-Wajih Modelling the Isotope composition of groundwater using hydrochemical properties in eastern Saudi Arabia: Implementation of innovative data intelligence techniques Journal of Hydrology: Regional Studies Water scarcity Aquifer depletion Contamination Groundwater sustainability Machine learning Ensemble learning |
| title | Modelling the Isotope composition of groundwater using hydrochemical properties in eastern Saudi Arabia: Implementation of innovative data intelligence techniques |
| title_full | Modelling the Isotope composition of groundwater using hydrochemical properties in eastern Saudi Arabia: Implementation of innovative data intelligence techniques |
| title_fullStr | Modelling the Isotope composition of groundwater using hydrochemical properties in eastern Saudi Arabia: Implementation of innovative data intelligence techniques |
| title_full_unstemmed | Modelling the Isotope composition of groundwater using hydrochemical properties in eastern Saudi Arabia: Implementation of innovative data intelligence techniques |
| title_short | Modelling the Isotope composition of groundwater using hydrochemical properties in eastern Saudi Arabia: Implementation of innovative data intelligence techniques |
| title_sort | modelling the isotope composition of groundwater using hydrochemical properties in eastern saudi arabia implementation of innovative data intelligence techniques |
| topic | Water scarcity Aquifer depletion Contamination Groundwater sustainability Machine learning Ensemble learning |
| url | http://www.sciencedirect.com/science/article/pii/S2214581824004877 |
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