Comparing performance of ANN and SVM methods for regional flood frequency analysis in South-East Australia

Amir Zalnezhad, Ataur Rahman, Nastaran Nasiri, Mehdi Vafakhah, Bijan Samali, Farhad Ahamed

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

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.
Original languageEnglish
Article number3323
Number of pages18
JournalWater
Volume14
Issue number20
DOIs
Publication statusPublished - 2022

Open Access - Access Right Statement

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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