Abstract
Coimbatore has diverse industrial presence mostly small scale industries ranging from textile unit, casting industries and Electroplating industries. The rapid urbanization and industrialization in Coimbatore city, leads to the generation and disposal of wastewater without treatment from households, hospitals, commercial buildings and industries directly into the stream, thus making the stream water to be polluted. Sanganur stream is one of the major streams flowing through the city of Coimbatore, Tamil Nadu, India. Sanganur stream runs mainly within the city limits, starting from Sanganur pallam and drains into Singanallur lake of Coimbatore city. This study focuses on assessing and predicting water pollution levels using artificial neural networks (ANN) as a machine learning approach. To assess the water pollution level in the stream, surface water is collected and analysed for various physicochemical parameters such as pH, TDS, chlorides, sulphates, DO, BOD, COD and nitrates. Water pollution index is calculated based on the assessed parameters. The ANN model, implemented in MATLAB©, utilized a feed-forward backpropagation neural network with a tan-sigmoid transfer function. The study also derived a mathematical model from the ANN architecture. The ANN model demonstrated high accuracy (R2 = 0.99, MSE = 0.00079), and the derived mathematical model closely mirrored the ANN output. Due to the unlined nature of the stream the wastewater flowing in the stream can contaminate the nearby surrounding water bodies, hence prediction is necessary to take necessary control measures. Hierarchical cluster analysis categorized sampling locations based on pollution levels, providing insights for targeted mitigation measures. Overall, this study emphasizes the efficacy of machine learning in predicting water pollution and underscores the importance of proactive measures to address pollution in the Sanganur stream.
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Data Availability
The data used in this article involves collection of water quality physico chemical characterisation data from the selected location during the period of January 2018 to February 2020. Modeling was carried out using MATLAB version R2016a and IBM SPSS Statistics 26. The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request..
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RS: Validation, Supervision, Project administration, Writing—Review & Editing. AMSA: Conceptualization, Investigation, Resources, Data Curation, Formal analysis, Writing—Review & Editing. RV: Software modelling, Methodology, Writing—Original Draft &Editing,
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Saraswathi, R., Mohammed Siraj Ansari, A. & Vignesh, R. Application of Machine Learning–ANN in Predicting the Pollution Index of Sanganur Stream in Coimbatore City, Tamil Nadu, India. Iran J Sci Technol Trans Civ Eng (2024). https://doi.org/10.1007/s40996-024-01399-5
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DOI: https://doi.org/10.1007/s40996-024-01399-5