Regional flood frequency analysis using the FCM-ANFIS algorithm : a case study in south-eastern Australia

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

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

Regional flood frequency analysis (RFFA) is widely used to estimate design floods in ungauged catchments. Both linear and non-linear methods are adopted in RFFA. The development of the non-linear RFFA method Adaptive Neuro-fuzzy Inference System (ANFIS) using data from 181 gauged catchments in south-eastern Australia is presented in this study. Three different types of ANFIS models, Fuzzy C-mean (FCM), Subtractive Clustering (SC), and Grid Partitioning (GP) were adopted, and the results were compared with the Quantile Regression Technique (QRT). It was found that FCM performs better (with relative error (RE) values in the range of 38-60%) than the SC (RE of 44-69%) and GP (RE of 42-78%) models. The FCM performs better for smaller to medium ARIs (2 to 20 years) (ARI of five years having the best performance), and in New South Wales, over Victoria. In many aspects, the QRT and FCM models perform very similarly. These developed RFFA models can be used in south-eastern Australia to derive more accurate flood quantiles. The developed method can easily be adapted to other parts of Australia and other countries. The results of this study will assist in updating the Australian Rainfall Runoff (national guide)-recommended RFFA technique.
Original languageEnglish
Article number1608
Number of pages17
JournalWater
Volume14
Issue number10
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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