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Application of Predictive Intelligence in Water Quality Forecasting of the River Ganga Using Support Vector Machines
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Author(s): Anil Kumar Bisht (MJP Rohilkhand University, India), Ravendra Singh (MJP Rohilkhand University, India), Rakesh Bhutiani (Gurukul Kangri Vishwavidhayalaya, India)and Ashutosh Bhatt (Birla Institute of Applied Sciences, India)
Copyright: 2019
Pages: 13
Source title:
Predictive Intelligence Using Big Data and the Internet of Things
Source Author(s)/Editor(s): P.K. Gupta (Jaypee University of Information Technology, India), Tuncer Ören (University of Ottawa, Canada)and Mayank Singh (University of KwaZulu-Natal, South Africa)
DOI: 10.4018/978-1-5225-6210-8.ch009
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Abstract
Predicting the water quality of rivers has attracted a lot of researchers all around the globe. A precise prediction of river water quality may benefit the water management bodies. However, due to the complex relationship existing among various factors, the prediction is a challenging job. Here, the authors attempted to develop a model for forecasting or predicting the water quality of the river Ganga using application of predictive intelligence based on machine learning approach called support vector machine (SVM). The monthly data sets of five water quality parameters from 2001 to 2015 were taken from five sampling stations from Devprayag to Roorkee in the Uttarakhand state of India. The experiments are conducted in Python 2.7.13 language (Anaconda2 4.3.1) using the radial basis function (RBF) as a kernel for developing the non-linear SVM-based classifier as a model for water quality prediction. The results indicated a prediction performance of 96.66% for best parameter combination which proved the significance of predictive intelligence in water quality forecasting.
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