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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Cluj University Press
2019-03-01
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| 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 |
| Summary: | 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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| ISSN: | 2067-743X |