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International Journal of Current Microbiology and Applied Sciences (IJCMAS)
IJCMAS is now DOI (CrossRef) registered Research Journal. The DOIs are assigned to all published IJCMAS Articles.
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National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2020)
[Effective from January 1, 2020]
For more details click here

ICV 2018: 95.39
Index Copernicus ICI Journals Master List 2018 - IJCMAS--ICV 2018: 95.39
For more details click here

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Original Research Articles

PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : editorijcmas@gmail.com / submit@ijcmas.com
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2018: 95.39
NAAS RATING 2020: 5.38

Int.J.Curr.Microbiol.App.Sci.2020.9(3): 127-135
DOI: https://doi.org/10.20546/ijcmas.2020.903.016


Rainfall-Runoff Modelling Using Artificial Neural Networks (ANNs) Model
Ashish Krishna Yadav*, Veerendra Kumar Chandola, Abhishek Singh and Bhaskar Pratap Singh
Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi-221005, Uttar Pradesh, India
*Corresponding author
Abstract:

Water resource assessment involved various variables that can be simplified and tackled by developing a suitable mathematical model. Rainfall-Runoff (RR) modeling considered as a major hydrologic process and is essential for water resources management. This study presents the development of rainfall-runoff model based on artificial neural networks (ANNs) models in Shipra river basin of Madhya Pradesh. The ability of model was evaluated based on sum of squares error (SSE) and relative error. The Sum of squares error obtained during this study was 30.525 in training and 53.076 in testing and the Relative error value obtained was 0.939 in training and 0.874 in testing at Mahidpur station but at Ujjain station, the SSE obtained during this study was found to be 30.488during training and 10.703during testing while the relative error value obtained was 0.938 in training and 0.915 in testing. The model was found suitable for simulating hydrological response of the basin to the rainfall and predicting daily runoff with high degree of accuracy. The study demonstrates the applicability of ANN approach using the statistical tool SPSS 16.0 in developing effective non-linear models of rainfall-runoff process in order to represent the internal hydrologic structure of the watershed.


Keywords: Artificial Neural Networks (ANNs), Sum of squares error (SSE), Relative error (RE)
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How to cite this article:

Ashish Krishna Yadav, Veerendra Kumar Chandola, Abhishek Singh and Bhaskar Pratap Singh. 2020. Rainfall-Runoff Modelling Using Artificial Neural Networks (ANNs) Model.Int.J.Curr.Microbiol.App.Sci. 9(3): 127-135. doi: https://doi.org/10.20546/ijcmas.2020.903.016