Abstract
Objective: In this study, prediction using classification techniques are used to predict the water quality of the 17 selected rivers in the year 2011 using their water quality in 2008 to interpret whether the water quality has improved or deteriorated. Methods/Analysis: For this prediction, we have used data mining classification techniques using Waikato Environment for Knowledge Analysis (WEKA) API to the dataset of selected 17 Indian rivers. The data used for prediction was created from ambient water quality of Aquatic Resources in India in 2008 and 2011. Data is obtained from data portal which was published under National Data Sharing and Accessibility Policy (NDSAP) and the contributor was Ministry of Environment and Forests Central Pollution Control Board (CPCB). Findings: Out of the four techniques used, prediction of classes, i.e. excellent, good, average and fair is best done by Naive Bayes followed by J48, SMO and REPTree technique.
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Acknowledgements
I profoundly thank Bharati Vidyapeeth’s College of Engineering for constant support and encouragement.
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Bindra, H., Jain, R., Singh, G., Garg, B. (2019). Application of Classification Techniques for Prediction of Water Quality of 17 Selected Indian Rivers. In: Balas, V., Sharma, N., Chakrabarti, A. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 808. Springer, Singapore. https://doi.org/10.1007/978-981-13-1402-5_18
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DOI: https://doi.org/10.1007/978-981-13-1402-5_18
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