A Data-Driven Approach for Microbial Contamination Prediction in Indian Rivers using BOD with Interpretable Machine Learning

Water quality research is essential to the sustainability of the environment and the safety of people, especially in countries like India where surface water bodies are under increasing stress from human interference. This study analyzes the relationship between physicochemical parameters such as pH, temperature, (DO) dissolved oxygen, (BOD) biochemical oxygen demand, and microbial contamination (as measured by Total Coliform counts).and electrical conductivity, in India's major rivers and states. The study examines regional variation in important water quality indicators and pinpoints regions of concern where water is most susceptible to contamination using a dataset that includes 534 observations from 18 Indian states. According to the findings, states like Maharashtra, Bihar, Uttar Pradesh, and Karnataka have higher monitoring data frequencies, which may reflect in-creased industrialization, urbanization, and awareness of water quality problems. States like Delhi, Chhattisgarh, and Kerala, on the other hand, have many fewer reported observations, which may indicate either underreporting or a lack of monitoring infrastructure. Low DO values were seen in regions with high BOD and Total Coliform levels, suggesting oxygen depletion brought on by microbial and organic pollution. The findings highlight the immediate need for real-time data gathering systems, a standardized national framework for water monitoring, and legislative actions to address disparate regional capacity. This study highlights how important it is to combine microbiological evaluations with physicochemical studies to provide a complete picture of water quality.