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.
Author (s) Details
Mamta Gour
Department of Chemistry, Medicaps University, Indore, India.
Sanjeev Gour
Department of Computer Science, Medicaps University, Indore, India.
Please see the book here:- https://doi.org/10.9734/bpi/rdcbr/v9/3146
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