Rainfall-runoff modeling in hilly watershed using heuristic approaches with gamma test

Rainfall-runoff modeling in hilly watershed using heuristic approaches with gamma test In this study, daily rainfall-runoff modeling was done using co-active neuro-fuzzy inference system (CANFIS) and multi-layer perceptron neural network (MLPNN) approaches in the hilly Naula watershed of Ramganga River in Uttarakhand, India. The daily observed rainfall and runoff data from June 1, 2000, to October 31, 2004, were used for training and testing of the applied models. Before starting the modeling process, the gamma test (GT) was used to select the best combination of input variables for each model. The simulated values of runoff from CANFIS and MLPNN models were compared with the observed ones with respect to root mean squared error (RMSE), Nash-Sutcliffe efficiency (CE), Pearson correlation coefficient (PCC). This study provides a conclusive evidence that the CANFIS shows better accuracy than the MLPNN models. Therefore, according to the best fitting CANFIS-10 model, the runoff of the present day depends on rainfall and runoff of current and previous 2 days for the studied area. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Arabian Journal of Geosciences Springer Journals

Rainfall-runoff modeling in hilly watershed using heuristic approaches with gamma test

Loading next page...
 
/lp/springer_journal/rainfall-runoff-modeling-in-hilly-watershed-using-heuristic-approaches-dvAHiAQAjy
Publisher
Springer Journals
Copyright
Copyright © 2018 by Saudi Society for Geosciences
Subject
Earth Sciences; Earth Sciences, general
ISSN
1866-7511
eISSN
1866-7538
D.O.I.
10.1007/s12517-018-3614-3
Publisher site
See Article on Publisher Site

Abstract

In this study, daily rainfall-runoff modeling was done using co-active neuro-fuzzy inference system (CANFIS) and multi-layer perceptron neural network (MLPNN) approaches in the hilly Naula watershed of Ramganga River in Uttarakhand, India. The daily observed rainfall and runoff data from June 1, 2000, to October 31, 2004, were used for training and testing of the applied models. Before starting the modeling process, the gamma test (GT) was used to select the best combination of input variables for each model. The simulated values of runoff from CANFIS and MLPNN models were compared with the observed ones with respect to root mean squared error (RMSE), Nash-Sutcliffe efficiency (CE), Pearson correlation coefficient (PCC). This study provides a conclusive evidence that the CANFIS shows better accuracy than the MLPNN models. Therefore, according to the best fitting CANFIS-10 model, the runoff of the present day depends on rainfall and runoff of current and previous 2 days for the studied area.

Journal

Arabian Journal of GeosciencesSpringer Journals

Published: May 30, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off