Prediction of Water Quality with Ensemble Learning Algorithms

Year 2023, , 36 - 44, 15.02.2023

Fatin Aljarah , Aydın Çetin

https://doi.org/10.54569/aair.1200695

Abstract

As monitoring and control of the quality of the water is one of the most important issues in the world since only 74% of the world's population use safely managed water where the water is treated well to reach the minimum limit of safety and quality standards. For observation of the water potability and to take immediate actions to improve the water quality, real-time monitoring and classification process are required. However, monitoring and controlling the quality of the water is not an easy task since it has many requirements such as the collection and analysis of data and measures to be taken. In this paper, we focus on applying machine learning for evaluation of the water quality. We have chosen five ensemble learning algorithms namely, Adaptive Boosting, Random Forest, Extra trees classifier, Gradient Boosting, and Stacking Classifier to evaluate their classification performances in defining the water quality. Results reveal that the Stacking Classifier has the highest performance among the five classifiers that we have studied.

Keywords

Water potability, artificial Intelligent, production WQ, machine learning, ML

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