Water Quality Analysis and Prediction using Machine Learning

The main objective of this research is to estimate the water quality using machine learning technique. Water is considered as a vital resource that has an impact on many facets of human health and existence. People who live in metropolitan areas are often concerned about the quality of the water as...

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Bibliographic Details
Published in:	2023 7th International Conference on Computing Methodologies and Communication (ICCMC) pp. 175 - 180
Main Authors:	Brindha, D., Puli, Viswanath, NVSS, Bala Karthik Sobula, Mittakandala, Vamsi Stephen, Nanneboina, Guru Dinesh
Format:	Conference Proceeding
Language:	English
Published:	IEEE 23-02-2023
Subjects:	Conductivity Decision Tree Machine Learning Pollution measurement Random Forest regression Temperature measurement Urban areas User interfaces Water pollution Water quality Web User Interface (UI)
Online Access:	Get full text
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Summary:	The main objective of this research is to estimate the water quality using machine learning technique. Water is considered as a vital resource that has an impact on many facets of human health and existence. People who live in metropolitan areas are often concerned about the quality of the water as it is critical to monitor the quality of water. Water sample collection and laboratory analysis are time and resource-intensive processes. Analyzing water quality is a complicated subject because of the many variables that affect it. This concept is inextricably linked to the various purposes for which water is used. The goal of this study is to estimate water quality by acquiring several parameters, and using the machine learning method, Random Forest regression. In this case, the model uses parameters like pH, turbidity, dissolved oxygen, conductivity, and others.
DOI:	10.1109/ICCMC56507.2023.10083776