Integration of an Innovative Machine Learning Model with Water Quality Index for Industrial Applications

Abstract

The integration of machine learning (ML) in assessing Water Quality Index (WQI) has revolutionized industrial water management by enabling accurate predictions and efficient resource use. This study focuses on developing a Naive Bayes-based model to assess WQI, leveraging key parameters such as pH, dissolved oxygen, and nitrate levels. The approach combines data preprocessing, feature selection, and probabilistic modelling to classify water quality into predefined categories, ensuring actionable insights for industrial applications. Notably, the model demonstrates versatility, proving applicable in sectors like aquaculture, manufacturing, and wastewater treatment. For example, in the sugar industry, the model predicts pollutant levels, enabling real-time interventions for effluent management, compliance with environmental standards, and sustainability. The research underscores ML’s potential to address industrial water challenges, fostering advancements in environmental conservation and public health. This innovative methodology exemplifies the transformative role of ML in achieving effective, scalable, and sustainable water quality solutions.