Comparative Analysis on Ensemble Learning Techniques for Groundwater Quality Assessment of Chhattisgarh Region

Groundwater is considered to be an essential resource for freshwater in Chhattisgarh. The importance of groundwater leads to a concern about water quality because the bad quality of water may lead to an unhealthy life. In this paper, we have used the numerous techniques of Ensemble Learning- (namely, Stacking, Bagging, and Boosting) for the classification of groundwater based on the Water Quality Index. The Stacking has been done by combining the following Classification Models Logistic Regression, k-Nearest Neighbors, Decision Tree, Support Vector Machine, and Naive Bayes. Bagging has been done using Bagged Decision Trees, Random Forest, and Extra Trees. Boosting has been performed using AdaBoost and Stochastic Gradient Boosting. Further, a comparative analysis of the performance of these Ensemble Learning Techniques has been presented. It has been observed that the maximum accuracy for classification has been given by the Stacking, Bagged Decision Trees, and Gradient Boosting with an accuracy of 0.9953.