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Data-Driven Models for Water Potability: A Machine Learning Approach

Publication Type : Conference Paper

Publisher : IEEE

Source : 2024 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024 - Proceedings, 2024. DOI: 10.1109/INCOS59338.2024.10527476

Url : https://ieeexplore.ieee.org/document/10527476

Campus : Bengaluru

School : School of Engineering

Department : Electrical and Electronics

Year : 2024

Abstract : Water is an essential component in our daily lives. Water is used for different purposes, such as drinking, industrial, and irrigation. Many problems can be seen nowadays because of containment water. Water is being polluted, and as a result, the environment is experiencing many negative effects, such as global warming and different types of diseases spreading in the environment. Different ML models are used to predict water quality because it's very important for the environment and surroundings. Water Potability can be predicted by machine learning using parameters such as PH, Conductivity, Turbidity, Solids, Hardness, silver, nitrates, cadmium, lead, etc. As there is a huge increase in the contamination of water by industries from urban to rural areas, it is affecting the environment and our way of life. So good accessibility and better quality of water are needed. The point of this work is to get goodquality of water by using machine learning techniques. In recent years, numerous contaminants have put water quality at risk. Forecasting and model development for assessing water quality have become essential to control water pollution. The level of quality is measured using variables like water quality indexing (WQI) and water quality categorization.

Cite this Research Publication : Mishra, N., Sangeetha, S.V.T., Deepa, K., Venugopal, V., “Data-Driven Models for Water Potability: A Machine Learning Approach”, 2024 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024 - Proceedings, 2024. DOI: 10.1109/INCOS59338.2024.10527476

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