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

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.