Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping

• Published in: Water resources management Vol. 31; no. 9; pp. 2761 - 2775
• Main Authors: Naghibi, Seyed Amir, Ahmadi, Kourosh, Daneshi, Alireza
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.07.2017; Springer Nature B.V
• Subjects: Algorithms; Altitude; Area; Atmospheric Sciences; Civil Engineering; Earth and Environmental Science; Earth Sciences; Effectiveness; Efficiency; Environment; Gene mapping; Genetic algorithms; Geotechnical Engineering & Applied Earth Sciences; Groundwater; Groundwater potential; Hydrogeology; Hydrology/Water Resources; Kernel functions; Kernels; Land use; Lithology; Locations (working); Management; Mapping; Mathematical models; Methods; Optimization algorithms; Spring; Support vector machines; Training; Water; Water scarcity; water shortages; Water supply; Random forest; Support vector machine; Iran; Geographic information system; Genetic algorithm; Ardebil
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-017-1660-3

Regarding the ever increasing issue of water scarcity in different countries, the current study plans to apply support vector machine (SVM), random forest (RF), and genetic algorithm optimized random forest (RFGA) methods to assess groundwater potential by spring locations. To this end, 14 effective variables including DEM-derived, river-based, fault-based, land use, and lithology factors were provided. Of 842 spring locations found, 70% (589) were implemented for model training, and the rest of them were used to evaluate the models. The mentioned models were run and groundwater potential maps (GPMs) were produced. At last, receiver operating characteristics (ROC) curve was plotted to evaluate the efficiency of the methods. The results of the current study denoted that RFGA, and RF methods had better efficacy than different kernels of SVM model. Area under curve (AUC) of ROC value for RF and RFGA was estimated as 84.6, and 85.6%, respectively. AUC of ROC was computed as SVM- linear (78.6%), SVM-polynomial (76.8%), SVM-sigmoid (77.1%), and SVM- radial based function (77%). Furthermore, the results represented higher importance of altitude, TWI, and slope angle in groundwater assessment. The methodology created in the current study could be transferred to other places with water scarcity issues for groundwater potential assessment and management.