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Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms

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Department of Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19697, Iran
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Department of Mining Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
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Control and Information Processing from Department of Mechanics and Mechatronics, Academy of Engineering, Peoples’ Friendship University of Russia (RUDN University), 117198 Moscow, Russia
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Department of Land, Water and Environmental Research, Korea Institute of Civil Engineering and Building Technology, Goyang 10223, Korea
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Civil Engineering Department, Ilia State University, Tbilisi 0162, Georgia
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Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju 36040, Korea
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Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam
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Faculty of Environmental and Chemical Engineering, Duy Tan University, Danang 550000, Vietnam
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Author to whom correspondence should be addressed.
Academic Editor: Fi-John Chang
Water 2021, 13(5), 658; https://doi.org/10.3390/w13050658
Received: 27 December 2020 / Revised: 8 February 2021 / Accepted: 12 February 2021 / Published: 28 February 2021
(This article belongs to the Section Hydrology)
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. View Full-Text
Keywords: groundwater-potential mapping; Bayesian network model; metaheuristic algorithms; geographic information system (GIS); receiver operating characteristic; area under the curve groundwater-potential mapping; Bayesian network model; metaheuristic algorithms; geographic information system (GIS); receiver operating characteristic; area under the curve
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MDPI and ACS Style

Karimi-Rizvandi, S.; Goodarzi, H.V.; Afkoueieh, J.H.; Chung, I.-M.; Kisi, O.; Kim, S.; Linh, N.T.T. Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms. Water 2021, 13, 658. https://doi.org/10.3390/w13050658

AMA Style

Karimi-Rizvandi S, Goodarzi HV, Afkoueieh JH, Chung I-M, Kisi O, Kim S, Linh NTT. Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms. Water. 2021; 13(5):658. https://doi.org/10.3390/w13050658

Chicago/Turabian Style

Karimi-Rizvandi, Sadegh, Hamid V. Goodarzi, Javad H. Afkoueieh, Il-Moon Chung, Ozgur Kisi, Sungwon Kim, and Nguyen T.T. Linh. 2021. "Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms" Water 13, no. 5: 658. https://doi.org/10.3390/w13050658

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