WATER QUALITY PREDICTION SYSTEM

WATER QUALITY PREDICTION SYSTEM

Ms.N.Radha1, Mr.D.P.Nitish Kumar2,Mr.R.Mohan3,Mr.D.Naresh Kumar4, Mr.Nandamudi Dinesh5

 1Assistant Professor, Department of Computer Science & Engineering, Dhirajlal Gandhi College of Technology, Salem, Tamilnadu, India

 2,3,4,5 UG Scholar, Department of Computer Science & Engineering, Dhirajlal Gandhi College of Technology, Salem, Tamilnadu, India

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Abstract - This project aims to predict the quality of water using machine learning and Internet of Things. Water quality is a critical factor in determining the suitability of water for various purposes, such as drinking, agriculture, and industrial use. Traditional methods of water quality monitoring can be time-consuming, expensive, and may not provide accurate results. Therefore, this project proposes a machine learning- based approach that can predict water quality based on various factors, such as pH, Hardness, Solids, Chloramines, Sulphates, Conductivity, Organic Carbon, Trihalomethanes and turbidity. The project will use supervised learning algorithms such as decision trees, random forests, Extreme Gradient Boosting and support vector machines to train the model on a dataset of water quality measurements. The model will then be tested on a separate dataset to evaluate its accuracy and effectiveness. The outcome of this project could be valuable in improving water quality management and ensuring the safe and efficient use of water resources. The proposed system has been tested on real-world water quality data, and the results demonstrate its effectiveness in predicting water quality parameters. The system has potential applications in various fields, including water resource management, environmental monitoring, and public health.

Key Words:  Water Quality, machine learning, sensors, Extreme Gradient boosting, random forest