A comparative study of SWAT, RFNN and RFNN-GA for predicting river runoff

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099056" target="_blank" >RIV/61989100:27240/16:86099056 - isvavai.cz</a>

Result on the web

<a href="http://www.indjst.org/index.php/indjst/article/view/92308/69820" target="_blank" >http://www.indjst.org/index.php/indjst/article/view/92308/69820</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.17485/ijst/2016/v9i17/92308" target="_blank" >10.17485/ijst/2016/v9i17/92308</a>

Result language

angličtina

Original language name

A comparative study of SWAT, RFNN and RFNN-GA for predicting river runoff

Original language description

Background/Objectives: Data-driven models such as Recurrent Fuzzy Neural Network (RFNN) have been proven to be great methods for modeling, characterizing and predicting various kinds of nonlinear hydrologic time series data such as rainfall, water quality and river runoff. In modeling and predicting river runoff, the most important advantage of datadriven models is that they do not need as much data as do physical models such as the Soil and Water Assessment Tool (SWAT). In Vietnam, most of data which are required by SWAT are not available, thus data-driven models seem to be more suitable for predicting river runoff than SWAT. The objective of this study is to investigate the performance of SWAT, RFNN and an improvement of RFNN (RFNN-GA), which is a hybrid of RFNN and Genetic Algorithm (GA) in predicting the runoff of Srepok River in Central Highland of Vietnam. Methods/Statistical Analysis: Coefficient of correlation (R2) and mean absolute relative error (MARE) are used to analysis and compare the performance of SWAT, RFNN and RFNN-GA. Findings: The experimental results demonstrate that RFNN and RFNN-GA give the performance better than that of SWAT and they are able to be applied to real applications. Among these methods, RFNN-GA is the most superior. Application/Improvements: In the terms of MARE and R2, RFNN-GA improves RFNN 0.9% and 2.2%, respectively; and improves SWAT 27.4% and 12.5%, respectively. RFNN-GA was deployed to predict the runoff of Srepok River in Central Highland of Vietnam.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

IN - Informatics

OECD FORD branch

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Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Indian journal of science and technology

ISSN

0974-6846

e-ISSN

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Volume of the periodical

9

Issue of the periodical within the volume

17

Country of publishing house

IN - INDIA

Number of pages

11

Pages from-to

1-11

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-84970016395