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A Comparative Study between Frequency Ratio Model and Gradient Boosted Decision Trees with Greedy Dimensionality Reduction in Groundwater Potential Assessment

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  • Shruti Sachdeva

    (Netaji Subhas University of Technology)

  • Bijendra Kumar

    (Netaji Subhas University of Technology)

Abstract

Khammam district in Telangana, India has gained notoriety for the increasing number of farmer suicides attributed to the augmenting crop failures. Climate change, causing sporadic and uneven rains in the largely agricultural state has increased the strain on the already dwindling water table. Hence, there is a need for an in-depth analysis into the current state of these resources for their sustainable utilization. This study deploys 21 factors for predicting the groundwater potential of the region. An inventory of 126 wells was utilized to construct the dataset with the influencing factors. The statistical method of Frequency ratio (FR) and a machine learning (ML) approach of Gradient Boosted Decision Trees with Greedy feature selection (GA-GBDT) have been applied. GA-GBDT model (accuracy: 81%) outperformed the FR model (accuracy: 63%) and it was deduced that ML has the capability to perform equally well and even better than the traditional statistical approaches in similar studies. The models were utilized to generate groundwater potential maps for the region. The FR model predicted 78 sq.km as having a very high potential to yield groundwater, while GA-GBDT estimated it to be 152 sq.km. The results could play a vital role in irrigation management and city planning.

Suggested Citation

  • Shruti Sachdeva & Bijendra Kumar, 2020. "A Comparative Study between Frequency Ratio Model and Gradient Boosted Decision Trees with Greedy Dimensionality Reduction in Groundwater Potential Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4593-4615, December.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:15:d:10.1007_s11269-020-02677-3
    DOI: 10.1007/s11269-020-02677-3
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    References listed on IDEAS

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    1. L. Lombardo & M. Cama & C. Conoscenti & M. Märker & E. Rotigliano, 2015. "Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: application to the 2009 storm event in Messi," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(3), pages 1621-1648, December.
    2. Omid Rahmati & Hamid Reza Pourghasemi, 2017. "Identification of Critical Flood Prone Areas in Data-Scarce and Ungauged Regions: A Comparison of Three Data Mining Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(5), pages 1473-1487, March.
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    Cited by:

    1. Amirhosein Mosavi & Farzaneh Sajedi Hosseini & Bahram Choubin & Massoud Goodarzi & Adrienn A. Dineva & Elham Rafiei Sardooi, 2021. "Ensemble Boosting and Bagging Based Machine Learning Models for Groundwater Potential Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 23-37, January.

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