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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 2019: 96.39
Index Copernicus ICI Journals Master List 2019 - IJCMAS--ICV 2019: 96.39
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.2021.10(8): 247-257
DOI: https://doi.org/10.20546/ijcmas.2021.1008.029


Runoff Modeling using ANN-based Generalized Feed Forward (GFF) and Multi Linear Regression (MLR) Technique for Narmada River Basin, Gujarat
Shrisht Suman1, Vikram Singh1, Tushar Rathod1* and C. John Wesley2
1Department of Soil and Water conservation Engineering, VIAET, SHUATS, Prayagraj, India
2Center for Geospatial Technology, VIAET, SHUATS, Prayagraj, India
*Corresponding author
Abstract:

Runoff prediction is one of the most important topics in water resources planning, development and management on a sustainable basis. The Generalized Feed Forward (GFF) and Multiple Linear Regression (MLR). This study was undertaken to develop and evaluate the applicability of the GFF and MLR models by way of training and testing of developed models during monsoon period (June to September) for Narmada River Basin in Chota Udaipur district of Gujarat state of India. The daily data of rainfall, runoff, minimum & maximum temperature and wind speed were used in the study for monsoon season. The daily data were split into two sets: a training data set from 2004 to 2008 and a testing data set from 2009 to 2010 for Narmada river basin. The input pairs in the training data set were applied to the network of a selected architecture and training was performed using a back propagation algorithm for GFF models. A number of networks were constructed and each of them was trained separately, and the best network was selected based on the accuracy of the predictions in the testing phase. The following statistical indices such as mean squared error (MSE), coefficient of efficiency (CE), coefficient of determination (R2) and coefficient of correlation (r) were applied to test the performance of the developed GFF and MLR models. The predicted suspended sediment using GFF models were found to be the best performing models for Narmada river basin. It was evident that MLR models fit very poorly for the dataset under study. The current day’s runoff can be simulated using the data of minimum temperature & wind speed (GFF–8).


Keywords: Water, Agriculture, renewable resource, rainfall intensity, duration, initial soil moisture
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How to cite this article:

Shrisht Suman, Vikram Singh, Tushar Rathod and John Wesley, C. 2021. Runoff Modeling using ANN-based Generalized Feed Forward (GFF) and Multi Linear Regression (MLR) Technique for Narmada River Basin, Gujarat.Int.J.Curr.Microbiol.App.Sci. 10(8): 247-257. doi: https://doi.org/10.20546/ijcmas.2021.1008.029