Prediction of the Rainfall – Runoff Relationship Using Neuro-Fuzzy and Support Vector Machines.

Rainfall- Runoff relationship analyzes are essential for the protection of flood rooting, management of water resources and design of water structures. In this study, Neuro-Fuzzy (NF) and Support Vector Machines (SVM) methods are applied for Rainfall- Runoff prediction. Daily hydrological and season...

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Main Authors: Bestami TAȘAR, Fatih UNES, Hakan VARCİN
Format: Article
Language:English
Published: Cluj University Press 2019-03-01
Series:Aerul şi Apa: Componente ale Mediului
Subjects:
Online Access:http://aerapa.conference.ubbcluj.ro/2019/PDF/24_TA%C5%9EAR%20et%20al.%20237-246.pdf
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author Bestami TAȘAR
Fatih UNES
Hakan VARCİN
author_facet Bestami TAȘAR
Fatih UNES
Hakan VARCİN
author_sort Bestami TAȘAR
collection DOAJ
description Rainfall- Runoff relationship analyzes are essential for the protection of flood rooting, management of water resources and design of water structures. In this study, Neuro-Fuzzy (NF) and Support Vector Machines (SVM) methods are applied for Rainfall- Runoff prediction. Daily hydrological and seasonal data taken from Muskegon basin in USA were used for present study. 1397 daily data of rainfall, temperature and runoff from the study area were analyzed by NF and SVM methods. The results show that the SVM method lead to low errors and high determinations in the Rainfall-Runoff modeling. Models results are compared with daily observed data. SVM method can be used as an alternative to classical methods in Rainfall- Runoff prediction.
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spelling doaj.art-94403760cf9a42f48242f1cb16a76d8d2023-12-02T02:15:13ZengCluj University PressAerul şi Apa: Componente ale Mediului2067-743X2019-03-01201923724610.24193/AWC2019_24Prediction of the Rainfall – Runoff Relationship Using Neuro-Fuzzy and Support Vector Machines.Bestami TAȘAR0Fatih UNES1Hakan VARCİN2Iskenderun Technical University, Civil Engineering Department, Iskenderun, TurkeyIskenderun Technical University, Civil Engineering Department, Iskenderun, TurkeyIskenderun Technical University, Civil Engineering Department, Iskenderun, TurkeyRainfall- Runoff relationship analyzes are essential for the protection of flood rooting, management of water resources and design of water structures. In this study, Neuro-Fuzzy (NF) and Support Vector Machines (SVM) methods are applied for Rainfall- Runoff prediction. Daily hydrological and seasonal data taken from Muskegon basin in USA were used for present study. 1397 daily data of rainfall, temperature and runoff from the study area were analyzed by NF and SVM methods. The results show that the SVM method lead to low errors and high determinations in the Rainfall-Runoff modeling. Models results are compared with daily observed data. SVM method can be used as an alternative to classical methods in Rainfall- Runoff prediction.http://aerapa.conference.ubbcluj.ro/2019/PDF/24_TA%C5%9EAR%20et%20al.%20237-246.pdfPredictionRainfallRunoffSupport vector machinesNeuro-fuzzy
spellingShingle Bestami TAȘAR
Fatih UNES
Hakan VARCİN
Prediction of the Rainfall – Runoff Relationship Using Neuro-Fuzzy and Support Vector Machines.
Aerul şi Apa: Componente ale Mediului
Prediction
Rainfall
Runoff
Support vector machines
Neuro-fuzzy
title Prediction of the Rainfall – Runoff Relationship Using Neuro-Fuzzy and Support Vector Machines.
title_full Prediction of the Rainfall – Runoff Relationship Using Neuro-Fuzzy and Support Vector Machines.
title_fullStr Prediction of the Rainfall – Runoff Relationship Using Neuro-Fuzzy and Support Vector Machines.
title_full_unstemmed Prediction of the Rainfall – Runoff Relationship Using Neuro-Fuzzy and Support Vector Machines.
title_short Prediction of the Rainfall – Runoff Relationship Using Neuro-Fuzzy and Support Vector Machines.
title_sort prediction of the rainfall runoff relationship using neuro fuzzy and support vector machines
topic Prediction
Rainfall
Runoff
Support vector machines
Neuro-fuzzy
url http://aerapa.conference.ubbcluj.ro/2019/PDF/24_TA%C5%9EAR%20et%20al.%20237-246.pdf
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