Machine learning algorithms for efficient water quality prediction

被引：0
作者：
Mourade Azrour
Jamal Mabrouki
Ghizlane Fattah
Azedine Guezzaz
Faissal Aziz
机构：
[1] Moulay Ismail University,Computer Sciences Department, IDMS Team, Faculty of Sciences and Techniques
[2] Mohammed V University in Rabat,Laboratory of Spectroscopy, Molecular Modeling, Materials, Nanomaterial, Water and Environment, CERNE2D, Faculty of Science
[3] Mohammed V University in Rabat,Civil Hydraulic and Environmental Engineering Laboratory, Water Treatment and Reuse Structure, Mohammadia School of Engineers
[4] Cadi Ayyad University,Department of Computer Science and Mathematics, High School of Technology
[5] University Cadi Ayyad,Laboratory of Water, Biodiversity and Climate Change, Semlalia Faculty of Sciences
来源：
Modeling Earth Systems and Environment | 2022年 / 8卷
关键词：
Machine learning; Data analysis; Artificial intelligence; Prediction; Water quality;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Water is an essential resource for human existence. In fact, more than 60% of the human body is made up of water. Our bodies consume water in every cell, in the different organisms and in the tissues. Hence, water allows stabilization of the body temperature and guarantees the normal functioning of the other bodily activities. Nevertheless, in recent years, water pollution has become a serious problem affecting water quality. Therefore, to design a model that predicts water quality is nowadays very important to control water pollution, as well as to alert users in case of poor quality detection. Motivated by these reasons, in this study, we take the advantages of machine learning algorithms to develop a model that is capable of predicting the water quality index and then the water quality class. The method we propose is based on four water parameters: temperature, pH, turbidity and coliforms. The use of the multiple regression algorithms has proven to be important and effective in predicting the water quality index. In addition, the adoption of the artificial neural network provides the most highly efficient way to classify the water quality.
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收藏
页码：2793 / 2801
页数：8
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