International Journal of Mathematical, Engineering and Management Sciences

ISSN: 2455-7749

SVM Model to Predict the Water Quality Based on Physicochemical Parameters

SVM Model to Predict the Water Quality Based on Physicochemical Parameters

Manisha Koranga
Department of Information Technology, D. S. B. Campus, Nainital, Uttarakhand, India.

Pushpa Pant
Department of Geography, M. B. Government P.G. College, Haldwani, Uttarakhand, India.

Durgesh Pant
School of Computer Sciences & Information Technology, Uttarakhand Open University, Haldwani, Uttarakhand, India.

Ashutosh Kumar Bhatt
Department of Computer Science, Birla Institute of Applied Sciences Bhimtal, Uttarakhand, India.

R. P. Pant
Department of Allied Sciences, Graphic Era Hill University, Bhimtal Campus, Bhimtal, Uttarakhand, India.

Mangey Ram
Department of Mathematics; Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India.

Tarun Kumar
Department of Chemistry, M. B. Government P. G. College, Haldwani, Uttarakhand, India.

DOI https://doi.org/10.33889/IJMEMS.2021.6.2.040

Received on October 12, 2020
Accepted on January 05, 2021


Analysis of water quality is a very important and challenging task in the management of water bodies and requires immediate attention as it adversely affects the health of living beings. Three parameters namely, pH, Total Dissolved Solids (TDS), and Turbidity were used for data analysis. In this study for mapping of training samples from input space to higher dimensional feature space, LibSVM, (a library of SVM) was used with the use of two kernel function types Radial Basis Function and Polynomial function. For performing the experiment, the three parameter combinations (C, d, ϒ) were evaluated based upon the kernel by taking various range values to obtain the best type of kernel functions through a 10-fold cross-validation process. After performing all experiments, a comparative analysis was done to evaluate the best parameter combination (C, d, ϒ) and the values of performance measures. The result shows that the optimum model developed using LibSVM with the use of Polynomial Kernel function which gives an accuracy of 99.434% in predicting water quality.

Keywords- Support vector machine, Water quality, LibSVM, Kernel functions, Nainital lake.


Koranga, M., Pant, P., Pant, D., Bhatt, A. K., Pant, R. P., Ram, M., & Kumar, T. (2021). SVM Model to Predict the Water Quality Based on Physicochemical Parameters. International Journal of Mathematical, Engineering and Management Sciences, 6(2), 645-659. https://doi.org/10.33889/IJMEMS.2021.6.2.040.

Conflict of Interest

The authors confirm that there is no conflict of Interest to publish the paper in the journal.


The authors thanks to the Uttarakhand Science Education and Research Centre (USERC), Dehradun, Uttarakhand, India and Jal Sansthan Nainital, Uttarakhand, India for their support.


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