By: Akshat Choudhary, Dr Manjot Kaur, Dr Gurpreet Singh, and Dr Chaitanya Singla
1. Akshat Choudhary, Student, Department of Computer Science & Engineering, Chandigarh Engineering College, Chandigarh Group of Colleges, Jhanjeri-140307 Mohali, India
2. Dr. Manjot Kaur, Associate Professor, Department of Computer Science & Engineering, Chandigarh Engineering College, Chandigarh Group of Colleges, Jhanjeri-140307 Mohali, India
3.Dr. Gurpreet Singh, Professor and Program Lead, Department of Computer Science & Engineering, Chandigarh Engineering College, Chandigarh Group of Colleges, Jhanjeri-140307 Mohali, India
4.Dr. Chaitanya Singla, Associate Professor, Department of Computer Science & Engineering, Chandigarh Engineering College, Chandigarh Group of Colleges, Jhanjeri-140307 Mohali, India
Quality of drinking water is one of the critical determinants of public health, leading to severe environmental and health-related issues when poor. The paper examines the integration of machine learning techniques that could improve accuracy and efficiency in assessing drinking water quality. The traditional techniques of monitoring drinking water are resource-intensive yet lack timely insight into contamination events. In this scenario, an evaluation of the performance of ML models such as Random Forest, SVM, and Neural Networks is conducted to analyze comprehensive datasets generated from water sensors. The methodology followed includes data collection, preprocessing, feature extraction, and training model, resulting in real-time predictions of water potability. Findings have shown that ML approaches outperform traditional statistical methods to look forward to effective management of the environment and to lower health risks from water pollution. This research indicates not only the potential of ML in the automated prediction of water quality but also the requirement for better monitoring strategies while securing safe water as new complications from climate change and pollution continue to deteriorate.
Keywords: Machine Learning (ML), Water Potability Prediction, Feature Engineering, Support Vector Machines (SVM), Water Quality Index (WQI), Neural Networks, Predictive Modeling.
Citation:
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