Water Quality Index Process Using Artificial Neural Networks

Authors

  • Muthukumaran V Department of Mathematics, REVA University
  • Vinoth kumar V Department of Computer Science and Engineering, Jain (Deemed to be University), Bangalore, India

DOI:

https://doi.org/10.59461/ijitra.v1i1.12

Keywords:

Artificial neural networks, runoff modeling and forecasting, Levenberg-Marquardt training algorithm, Bhargava; Meteorological; Artificial Neural Network

Abstract

This Study intends to explore the association among the water quality record (WQI) for water framework drives four free environment factors. Our logical examination was driven on the Euphrates River inside Karbala city, Iraq over the period between 2008 to 2021.The nonlinear backslide perfect was gotten to base the WQI since the coefficient of affirmation and least mix-up regard stayed improved than persons gained by the ANN. The outcomes got in this exhibit that the LM strategy is more proficient and viable in catching the non-direct and complex spillover measure in a huge Indian catchment.

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Published

2022-05-26

How to Cite

V, M., & V, V. kumar. (2022). Water Quality Index Process Using Artificial Neural Networks. International Journal of Information Technology, Research and Applications, 1(1), 33–37. https://doi.org/10.59461/ijitra.v1i1.12

Issue

Vol. 1 No. 1 (2022): IJITRA

Section

Regular Issue

License

Copyright (c) 2022 Muthukumaran V, Vinoth kumar V

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.