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

Author

Listed:
  • Seyed Amir Naghibi

    (Islamic Azad University)

  • Kourosh Ahmadi

    (Tarbiat Modares University)

  • Alireza Daneshi

    (Gorgan University of Agricultural Sciences and Natural Resources)

Abstract

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.

Suggested Citation

  • Seyed Amir Naghibi & Kourosh Ahmadi & Alireza Daneshi, 2017. "Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(9), pages 2761-2775, July.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:9:d:10.1007_s11269-017-1660-3
    DOI: 10.1007/s11269-017-1660-3
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    References listed on IDEAS

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    1. Mebane Jr., Walter R. & Sekhon, Jasjeet S., 2011. "Genetic Optimization Using Derivatives: The rgenoud Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i11).
    2. Seyed Naghibi & Hamid Pourghasemi, 2015. "A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5217-5236, November.
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