Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods for Groundwater Potential Mapping

Saad AlAyyash (), A’kif Al-Fugara, Rania Shatnawi, Abdel Rahman Al-Shabeeb, Rida Al-Adamat and Hani Al-Amoush
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Saad AlAyyash: Department of Civil Engineering, Faculty of Engineering, Al al-Bayt University, Mafraq 25113, Jordan
A’kif Al-Fugara: Department of Surveying Engineering, Faculty of Engineering, Al al-Bayt University, Mafraq 25113, Jordan
Rania Shatnawi: Department of Civil Engineering, School of Built Environment Engineering, Al-Hussein Technical University, Amman 11822, Jordan
Abdel Rahman Al-Shabeeb: Department of Geographic Information Systems & Remote Sensing, Faculty of Earth and Environmental Sciences, Al al-Bayt University, Mafraq 25113, Jordan
Rida Al-Adamat: Department of Geographic Information Systems & Remote Sensing, Faculty of Earth and Environmental Sciences, Al al-Bayt University, Mafraq 25113, Jordan
Hani Al-Amoush: Department of Applied Earth and Environmental Sciences, Faculty of Earth and Environmental Sciences, Al al-Bayt University, Mafraq 25113, Jordan

Sustainability, 2023, vol. 15, issue 3, 1-22

Abstract: The groundwater contained in aquifers is among the most important water supply resources, especially in semi-arid and arid regions worldwide. This study aims to evaluate and compare the prediction capability of two well–known models, support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS), combined with a genetic algorithm (GA), invasive weed optimization (IWO), and teaching–learning-based optimization (TLBO) algorithms in groundwater potential mapping (GPM) the Azraq Basin in Jordan. The hybridization of the SVM and ANFIS models with the GA, IWO, and TLBO algorithms results in six models: SVM–GA, SVM–IWO, SVM–TLBO, ANFIS–GA, ANFIS–IWO, and ANFIS–TLBO. A database consisting of well data containing 464 wells with 12 predictive factors was developed for the groundwater potential mapping (GPM) of the study area. Of the 464 well locations, 70% (325 locations) were assigned for the training set and the rest (139 locations) for the validation set. The correlation between the 12 predictive factors and the well locations is analyzed using the frequency ratio (FR) statistical model. An area under receiver operating characteristic (AUROC) curve was used to evaluate and compare the models. According to the results, the SVM-based hybrid models outperformed other ANFIS hybrid models in the learning (training) and validation phases. The SVM–GA and SVM–TLBO hybrid models showed AUROC values of 0.984 and 0.971, respectively, in the training and validation phases. Moreover, the ANFIS–GA and ANFIS–TLBO hybrid models showed an AUROC of 0.979 and 0.984 in the training phase and an AUROC of 0.973 and 0.984 in the validation phase, respectively. The SVM–IWO and ANFIS–IWO hybrid models showed the lowest AUROC. This study demonstrated the more efficient results of the SVM-based hybrid models in comparison with the ANFIS-based hybrid models in terms of accuracy and modeling speed.

Keywords: azraq basin; Jordan; groundwater potential mapping; ANFIS; SVM; GA; TLBO; IWO (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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