Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms
Owing to the reduction of surface-water resources and frequent droughts, the exploitation of groundwater resources has faced critical challenges. For optimal management of these valuable resources, careful studies of groundwater potential status are essential. The main goal of this study was to dete...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/13/5/658 |
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| author | Sadegh Karimi-Rizvandi Hamid Valipoori Goodarzi Javad Hatami Afkoueieh Il-Moon Chung Ozgur Kisi Sungwon Kim Nguyen Thi Thuy Linh |
| author_facet | Sadegh Karimi-Rizvandi Hamid Valipoori Goodarzi Javad Hatami Afkoueieh Il-Moon Chung Ozgur Kisi Sungwon Kim Nguyen Thi Thuy Linh |
| author_sort | Sadegh Karimi-Rizvandi |
| collection | DOAJ |
| description | Owing to the reduction of surface-water resources and frequent droughts, the exploitation of groundwater resources has faced critical challenges. For optimal management of these valuable resources, careful studies of groundwater potential status are essential. The main goal of this study was to determine the optimal network structure of a Bayesian network (BayesNet) machine-learning model using three metaheuristic optimization algorithms—a genetic algorithm (GA), a simulated annealing (SA) algorithm, and a Tabu search (TS) algorithm—to prepare groundwater-potential maps. The methodology was applied to the town of Baghmalek in the Khuzestan province of Iran. For modeling, the location of 187 springs in the study area and 13 parameters (altitude, slope angle, slope aspect, plan curvature, profile curvature, topography wetness index (TWI), distance to river, distance to fault, drainage density, rainfall, land use/cover, lithology, and soil) affecting the potential of groundwater were provided. In addition, the statistical method of certainty factor (CF) was utilized to determine the input weight of the hybrid models. The results of the OneR technique showed that the parameters of altitude, lithology, and drainage density were more important for the potential of groundwater compared to the other parameters. The results of groundwater-potential mapping (GPM) employing the receiver operating characteristic (ROC) area under the curve (AUC) showed an estimation accuracy of 0.830, 0.818, 0.810, and 0.792, for the BayesNet-GA, BayesNet-SA, BayesNet-TS, and BayesNet models, respectively. The BayesNet-GA model improved the GPM estimation accuracy of the BayesNet-SA (4.6% and 7.5%) and BayesNet-TS (21.8% and 17.5%) models with respect to the root mean square error (RMSE) and mean absolute error (MAE), respectively. Based on metric indices, the GA provides a higher capability than the SA and TS algorithms for optimizing the BayesNet model in determining the GPM. |
| first_indexed | 2024-03-09T06:10:08Z |
| format | Article |
| id | doaj.art-28fbcb3b84a94bbf8d8da3588d0777ed |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T06:10:08Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-28fbcb3b84a94bbf8d8da3588d0777ed2023-12-03T11:58:56ZengMDPI AGWater2073-44412021-02-0113565810.3390/w13050658Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic AlgorithmsSadegh Karimi-Rizvandi0Hamid Valipoori Goodarzi1Javad Hatami Afkoueieh2Il-Moon Chung3Ozgur Kisi4Sungwon Kim5Nguyen Thi Thuy Linh6Department of Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19697, IranDepartment of Mining Engineering, Isfahan University of Technology, Isfahan 84156-83111, IranControl and Information Processing from Department of Mechanics and Mechatronics, Academy of Engineering, Peoples’ Friendship University of Russia (RUDN University), 117198 Moscow, RussiaDepartment of Land, Water and Environmental Research, Korea Institute of Civil Engineering and Building Technology, Goyang 10223, KoreaCivil Engineering Department, Ilia State University, Tbilisi 0162, GeorgiaDepartment of Railroad Construction and Safety Engineering, Dongyang University, Yeongju 36040, KoreaInstitute of Research and Development, Duy Tan University, Danang 550000, VietnamOwing to the reduction of surface-water resources and frequent droughts, the exploitation of groundwater resources has faced critical challenges. For optimal management of these valuable resources, careful studies of groundwater potential status are essential. The main goal of this study was to determine the optimal network structure of a Bayesian network (BayesNet) machine-learning model using three metaheuristic optimization algorithms—a genetic algorithm (GA), a simulated annealing (SA) algorithm, and a Tabu search (TS) algorithm—to prepare groundwater-potential maps. The methodology was applied to the town of Baghmalek in the Khuzestan province of Iran. For modeling, the location of 187 springs in the study area and 13 parameters (altitude, slope angle, slope aspect, plan curvature, profile curvature, topography wetness index (TWI), distance to river, distance to fault, drainage density, rainfall, land use/cover, lithology, and soil) affecting the potential of groundwater were provided. In addition, the statistical method of certainty factor (CF) was utilized to determine the input weight of the hybrid models. The results of the OneR technique showed that the parameters of altitude, lithology, and drainage density were more important for the potential of groundwater compared to the other parameters. The results of groundwater-potential mapping (GPM) employing the receiver operating characteristic (ROC) area under the curve (AUC) showed an estimation accuracy of 0.830, 0.818, 0.810, and 0.792, for the BayesNet-GA, BayesNet-SA, BayesNet-TS, and BayesNet models, respectively. The BayesNet-GA model improved the GPM estimation accuracy of the BayesNet-SA (4.6% and 7.5%) and BayesNet-TS (21.8% and 17.5%) models with respect to the root mean square error (RMSE) and mean absolute error (MAE), respectively. Based on metric indices, the GA provides a higher capability than the SA and TS algorithms for optimizing the BayesNet model in determining the GPM.https://www.mdpi.com/2073-4441/13/5/658groundwater-potential mappingBayesian network modelmetaheuristic algorithmsgeographic information system (GIS)receiver operating characteristicarea under the curve |
| spellingShingle | Sadegh Karimi-Rizvandi Hamid Valipoori Goodarzi Javad Hatami Afkoueieh Il-Moon Chung Ozgur Kisi Sungwon Kim Nguyen Thi Thuy Linh Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms Water groundwater-potential mapping Bayesian network model metaheuristic algorithms geographic information system (GIS) receiver operating characteristic area under the curve |
| title | Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms |
| title_full | Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms |
| title_fullStr | Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms |
| title_full_unstemmed | Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms |
| title_short | Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms |
| title_sort | groundwater potential mapping using a self learning bayesian network model a comparison among metaheuristic algorithms |
| topic | groundwater-potential mapping Bayesian network model metaheuristic algorithms geographic information system (GIS) receiver operating characteristic area under the curve |
| url | https://www.mdpi.com/2073-4441/13/5/658 |
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