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Comparing Performance of ANN and SVM Methods for Regional Flood Frequency Analysis in South-East Australia.
- Source :
- Water (20734441); Oct2022, Vol. 14 Issue 20, pN.PAG-N.PAG, 18p
- Publication Year :
- 2022
-
Abstract
- Design flood estimations at ungauged catchments are a challenging task in hydrology. Regional flood frequency analysis (RFFA) is widely used for this purpose. This paper develops artificial intelligence (AI)-based RFFA models (artificial neural networks (ANN) and support vector machine (SVM)) using data from 181 gauged catchments in South-East Australia. Based on an independent testing, it is found that the ANN method outperforms the SVM (the relative error values for the ANN model range 33–54% as compared to 37–64% for the SVM). The ANN and SVM models generate more accurate flood quantiles for smaller return periods; however, for higher return periods, both the methods present a higher estimation error. The results of this study will help to recommend new AI-based RFFA methods in Australia. [ABSTRACT FROM AUTHOR]
- Subjects :
- SUPPORT vector machines
FLOODS
ARTIFICIAL neural networks
ARTIFICIAL intelligence
Subjects
Details
- Language :
- English
- ISSN :
- 20734441
- Volume :
- 14
- Issue :
- 20
- Database :
- Complementary Index
- Journal :
- Water (20734441)
- Publication Type :
- Academic Journal
- Accession number :
- 159962150
- Full Text :
- https://doi.org/10.3390/w14203323