Regional Flood Frequency Analysis Using the FCM-ANFIS Algorithm: A Case Study in South-Eastern Australia

Water

Mehdi Vafakhah¹  Farhad Ahamed²  Amir Zalnezhad³  Bijan Samali³  Ataur Rahman³ 

[1] Department of Watershed Management, Faculty of Natural Resources, Tarbiat Modarres University, Noor P.O. Box 46417–76489, Mazandaran Province, Iran;School of Computer, Data and Mathematical Sciences, Western Sydney University, Penrith, NSW 2751, Australia;School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia;

关键词: ANFIS;    flood;    fuzzy logic;    RFFA;    ungauged catchments;    Australian Rainfall and Runoff;   

DOI  :  10.3390/w14101608

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown