Performance Evaluation of Machine Learning Models with Ensemble Learning approach in Classification of Water Quality Indices Based on Different Subset of Features

Abstract

Abstract Since fresh water resources in form of groundwaters which are the most available water resources for human consumption are extremely limited and due to uncontrolled human activities are prone to contamination, it’s of a great importance to constantly monitor the quality of the ground fresh water resources to provide a sustainable drinking water for people as well as protecting the ecosystem. One tool for modeling the water quality of a basin is Water Quality Index (WQI). However, calculating WQI is complicated and time- consuming, therefore, today, scientists are being inclined to propose simpler ways for modeling the quality of the water resources such as machine learning algorithms. In this study the performance of four machine learning algorithms with ensemble learning approach were evaluated to propose a classification model (classifier) with highest performance. Moreover, to identify the most important water quality parameters in the classification process, three feature selection methods with machine learning approach were applied. As a result, among four classifiers, XGBoost showed outstanding performance, with the accuracy of 96.9696% when all the parameters of interest were involved in the classification process. However, in order to make the model cost-effective it is suggested to conduct the classification with optimum parameters which in this case, for the dataset which was used in this study XGBoost classifier is suggested as the best classifier with the maximum accuracy of 95.606% with 10-Fold Cross Validation when seven parameters which were identified by Backward Feature Elimination Feature selector were involved in the classification process.