UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 4 Issue 3
March-2017
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR1703045


Registration ID:
180545

Page Number

212-220

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Title

Rainfall Runoff Relationship using Neural Networks and Fuzzy Logic

Abstract

Rainfall-runoff models are used to describe the hydrological behavior of a catchment. The relationship between rainfall and runoff is known to be highly non- linear, time varying and spatially distributed. This relationship is important in dealing with water resources management schemes. Recently, soft computing techniques such as Artificial Neural Networks, Fuzzy Logic, Adaptive Neuro- Fuzzy Inference System have emerged to model the various hydrological processes. This study presents the application of Artificial Neural Networks and Fuzzy Logic to model rainfall- runoff for Osmansagar catchment, Hyderabad, India. To study the performance of the models, various statistical indices such as Threshold statistics, Average absolute relative error, Correlation coefficient, Mean square error, Nash coefficient of efficiency were estimated.. The Nash coefficient of efficiency of the ANN and Fuzzy model were found to be 95% and 92 % during training/ calibration and 91% and 96% during testing / validation. The correlation coefficient between the observed and computed series for both the models are 0.97, 0.96 respectively during training /calibration and 0.97, 0.99 respectively during testing/validation periods. The results indicate that ANN and Fuzzy logic can effectively be used to establish relationship between rainfall and runoff.

Key Words

Rainfall, Runoff, Neural networks, Fuzzy Logic

Cite This Article

"Rainfall Runoff Relationship using Neural Networks and Fuzzy Logic", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.4, Issue 3, page no.212-220, March-2017, Available :http://www.jetir.org/papers/JETIR1703045.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Rainfall Runoff Relationship using Neural Networks and Fuzzy Logic", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.4, Issue 3, page no. pp212-220, March-2017, Available at : http://www.jetir.org/papers/JETIR1703045.pdf

Publication Details

Published Paper ID: JETIR1703045
Registration ID: 180545
Published In: Volume 4 | Issue 3 | Year March-2017
DOI (Digital Object Identifier): http://doi.one/10.1729/IJCRT.17755
Page No: 212-220
Country: Dilsukhnagar, Telangana, India .
Area: Science & Technology
ISSN Number: 2349-5162
Publisher: IJ Publication


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