Chemical and Biological Profiling of Narmada River: A Random Forest Model-Based Water Quality Analysis

Water quality is a critical indicator of the health of aquatic ecosystems and the sustainability of water resources for human use. In this study, the water quality of the Narmada River was analyzed by examining key physicochemical and biological parameters that impact the river's ecosystem. Utilizing a secondary dataset spanning from 1990 to 2012, collected from the Hoshangabad district of Madhya Pradesh, seven water quality parameters were evaluated, including pH, dissolved oxygen, biochemical oxygen demand, and nitrate concentrations. The objective of this study is to assess the condition of the Narmada River water in relation to the Surface Water Quality Standards for Indian Rivers and to provide insights into the degradation trends over time.

To achieve this, a Random Forest algorithm, a robust machine learning technique, was implemented using the RapidMiner analytical tool to classify and predict water quality. This model effectively identifies patterns in the dataset, enabling a thorough understanding of the factors contributing to the declining water quality. Our findings indicate that the river’s water quality has steadily deteriorated, primarily due to increasing domestic sewage and industrial effluent discharge into the river, particularly in urban areas along its course. The results of this analysis present a critical alert to environmental policymakers and water resource managers, emphasizing the urgent need for improved water treatment facilities and regular monitoring protocols to mitigate further pollution.

This study highlights the efficacy of data-driven approaches like Random Forest in environmental monitoring and underscores the importance of integrating machine learning techniques with traditional water quality assessments to enable more informed decision-making for sustainable water management.