Sandstone groundwater salinization modelling using physicochemical variables in Southern Saudi Arabia: Application of novel data intelligent algorithms
Reliable modelling and simulation of groundwater management are crucial for sustainable development. Groundwater salinization is considered challenging and has recently led to the development of several emerging advancements and technologies, which grant a feasible solution to integrated water manag...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | Ain Shams Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447922002052 |
| _version_ | 1797860407417765888 |
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| author | S.I. Abba Mohammed Benaafi A.G. Usman Isam H. Aljundi |
| author_facet | S.I. Abba Mohammed Benaafi A.G. Usman Isam H. Aljundi |
| author_sort | S.I. Abba |
| collection | DOAJ |
| description | Reliable modelling and simulation of groundwater management are crucial for sustainable development. Groundwater salinization is considered challenging and has recently led to the development of several emerging advancements and technologies, which grant a feasible solution to integrated water management and desalination processes. For this purpose, Electrical Conductivity (EC) as the early Salinization sign is modelled using various computational techniques, namely, Least Square-boost (LSQ-Boost), Gaussian Process regression (GPR), support vector regression (SVR) and stepwise linear regression (SWLR). The experiment data from sandstone aquifers include parameters from the physical, chemical and hydrogeochemical aspects. Four different input combinations (C1-C4) were developed using linear and ranking nonlinear feature selection and validated modelling results weres assessed by mean square error (MSE), mean absolute error (MAE), root means square error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (R). The analysis also considers the effect of multicollinearity, and the variables affected, such as TDS (mg/L), were not included in the first three combinations. The novel GPR proved superior to other models, with GPR-C1 justified quantitatively (MSE = 0.0255, MAE = 25260.49 and RMSE = 0.1595) in the verification phase. Other intelligent models (SVR, LSQ-Boost) depicted promising for C3 and C4 combinations with more than 88–90% predictive accuracy. The explored novel GPR algorithm offered an excellent and reliable EC prediction tool. The study also suggested using direct correlated positive variables, including hydrochemical and topographic factors, in modelling groundwater salinization. This would lead to more effective water-resources-related planning and decision making. |
| first_indexed | 2024-04-09T21:45:19Z |
| format | Article |
| id | doaj.art-2aa63c8d9eb34f1cb5eea119db7e9137 |
| institution | Directory Open Access Journal |
| issn | 2090-4479 |
| language | English |
| last_indexed | 2024-04-09T21:45:19Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj.art-2aa63c8d9eb34f1cb5eea119db7e91372023-03-25T05:10:44ZengElsevierAin Shams Engineering Journal2090-44792023-04-01143101894Sandstone groundwater salinization modelling using physicochemical variables in Southern Saudi Arabia: Application of novel data intelligent algorithmsS.I. Abba0Mohammed Benaafi1A.G. Usman2Isam H. Aljundi3Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaInterdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia; Corresponding author at: Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.Department of Analytical Chemistry, Faculty of Pharmacy, Near East University, TRNC, Mersin 10, 99138 Nicosia, Turkey; Operational Research Centre in Healthcare, Near East University, Nicosia, CyprusInterdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia; Department of Chemical Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaReliable modelling and simulation of groundwater management are crucial for sustainable development. Groundwater salinization is considered challenging and has recently led to the development of several emerging advancements and technologies, which grant a feasible solution to integrated water management and desalination processes. For this purpose, Electrical Conductivity (EC) as the early Salinization sign is modelled using various computational techniques, namely, Least Square-boost (LSQ-Boost), Gaussian Process regression (GPR), support vector regression (SVR) and stepwise linear regression (SWLR). The experiment data from sandstone aquifers include parameters from the physical, chemical and hydrogeochemical aspects. Four different input combinations (C1-C4) were developed using linear and ranking nonlinear feature selection and validated modelling results weres assessed by mean square error (MSE), mean absolute error (MAE), root means square error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (R). The analysis also considers the effect of multicollinearity, and the variables affected, such as TDS (mg/L), were not included in the first three combinations. The novel GPR proved superior to other models, with GPR-C1 justified quantitatively (MSE = 0.0255, MAE = 25260.49 and RMSE = 0.1595) in the verification phase. Other intelligent models (SVR, LSQ-Boost) depicted promising for C3 and C4 combinations with more than 88–90% predictive accuracy. The explored novel GPR algorithm offered an excellent and reliable EC prediction tool. The study also suggested using direct correlated positive variables, including hydrochemical and topographic factors, in modelling groundwater salinization. This would lead to more effective water-resources-related planning and decision making.http://www.sciencedirect.com/science/article/pii/S2090447922002052AquiferArtificial IntelligenceElectrical ConductivityGroundwaterSalinization |
| spellingShingle | S.I. Abba Mohammed Benaafi A.G. Usman Isam H. Aljundi Sandstone groundwater salinization modelling using physicochemical variables in Southern Saudi Arabia: Application of novel data intelligent algorithms Ain Shams Engineering Journal Aquifer Artificial Intelligence Electrical Conductivity Groundwater Salinization |
| title | Sandstone groundwater salinization modelling using physicochemical variables in Southern Saudi Arabia: Application of novel data intelligent algorithms |
| title_full | Sandstone groundwater salinization modelling using physicochemical variables in Southern Saudi Arabia: Application of novel data intelligent algorithms |
| title_fullStr | Sandstone groundwater salinization modelling using physicochemical variables in Southern Saudi Arabia: Application of novel data intelligent algorithms |
| title_full_unstemmed | Sandstone groundwater salinization modelling using physicochemical variables in Southern Saudi Arabia: Application of novel data intelligent algorithms |
| title_short | Sandstone groundwater salinization modelling using physicochemical variables in Southern Saudi Arabia: Application of novel data intelligent algorithms |
| title_sort | sandstone groundwater salinization modelling using physicochemical variables in southern saudi arabia application of novel data intelligent algorithms |
| topic | Aquifer Artificial Intelligence Electrical Conductivity Groundwater Salinization |
| url | http://www.sciencedirect.com/science/article/pii/S2090447922002052 |
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