Classification of Quality of Water Using Machine Learning

Classification of Quality of Water Using Machine Learning

P. Umamaheswari, M. Kamaladevi
Copyright: © 2023 |Pages: 16
DOI: 10.4018/978-1-6684-6791-6.ch009
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Abstract

Keeping a check on the quality of water is necessary for protecting both the health of humans and of the environment. AI can be used to classify and predict water quality. The proposed system uses several machine learning algorithms to manage water quality data gathered over a protracted period. Water quality index (WQI) is used to categorize the given samples by using machine learning and ensemble approaches. The studied classifiers included random forest classifier, CatBoost classifier, k nearest neighbors, logistic regression, etc. The authors used precision-recall curves, ROC curves and confusion matrices as performance metrics for the ML classifiers used. With an accuracy of 95.43%, the random forest model was shown to be the most accurate classifier. Furthermore, CatBoost classifier and k nearest neighbors provided satisfactory results with 94.86% and 94.08% accuracy, respectively. Therefore, CatBoost algorithm is considered to be a more reliable approach for the quality of water classification.
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The major reason behind the topic is that nowadays, there are many issues arose due to the water pollution. So water quality prediction is playing an important role in many areas related to agriculture, fishery and people investing more money in real estate business, etc. Mainly due to the increase in the content of nitrates, magnesium (Xu & Su, 2019) and lead percentages in ground water (drinking). In order to predict quality of water, many researchers had to predict the safe water using redox conditions and pH level present (Larocque et al., 2019) in ground water. As far as ground water is concerned, when pH condition increases then it leads to the increase in calcium and magnesium carbonates in the pipes. While this is higher, then pH does not pose any health risk, it can cause skin problems, such as becoming dry, itchy and irritated. When pH level decreases then acidity in water increases and it becomes toxic. It has been observed that aquatic pesticide pollution trends are mainly driven (Chow et al., 2020) by pesticide use and hydrology. Moreover they suggested baseline monitoring is essential to infer long-term water quality trends.

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