Home > Archive > 2019 > Volume 9 Number 5 (Oct. 2019) >
IJMLC 2019 Vol.9(5): 688-693 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.5.859

Application of ANN for Water Quality Index

Rajiv Gupta, A N Singh, and Anupam Singhal

Abstract—Attempt has been made to create a Water Quality Index (WQI) based on artificial neural network (ANN) and globally accepted parameters. Several methods to measure WQI are available in the research and ambiguity problems exist where all the sub-indices of WQI are acceptable but overall index is not acceptable. In this study, we have tried to develop the WQI based on the WHO (world Health Organization) parameters (Dissolved Oxygen, pH, Turbidity, E. Coli and Electric Conductivity). The results also reveal changes in ANN based result from various input neural network model and its parameters. Even within same model, changes occur with variation in parameter. Based on the statistical parameter of regression value, the parameter and network model would be selected. With the dataset created for this study have shown the Cascade network is best for predicting the WQI.

Index Terms—Artificial neural network, cascade network, water quality index, who parameters.

Rajiv Gupta, A N Singh and Anupam Singhal are with the Dept. of Civil Engineering, BITS-Pilani, Rajasthan, India (e-mail: rajiv@pilani.bits-pilani.ac.in).


Cite: Rajiv Gupta, A N Singh, and Anupam Singhal, "Application of ANN for Water Quality Index," International Journal of Machine Learning and Computing vol. 9, no. 5, pp. 688-693, 2019.

Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

Article Metrics