An Efficient ML-based Prediction Model for Analysis of Water Quality and Pollution in Yamuna River, India
Keywords:
MLP-AWQP, WQI, Yamuna River, LR, SVM, XGB, RF, water pollutionAbstract
The level of pollution in the areas around the Yamuna River and industrial activity has significantly increased, thus it is crucial to assess the general state of the water quality in these areas. The production of agriculture, ecosystem services, and human health are all at risk due to the growing worldwide problem of water pollution. The unique characteristics of ensemble-based modeling and machine learning can provide a thorough understanding of the growing concerns about water quality. This study suggests a model that makes use of several methods, such as support vector machines, random forests, multinomial logistic regression, and extreme gradient boosting, to forecast pollution and water quality. It provides a thorough analysis of the water quality assessment and provides insights into the general state of the water quality in Delhi's Yamuna River, India, and near industrial areas. It achieves the best accuracy of 100% with the extreme gradient boosting technique.
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Dataset Link: https://www.dpcc.delhigovt.nic.in/
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