Development of a convolutional neural network based regional flood frequency analysis model for South-east Australia

Nilufa Afrin, Farhad Ahamed and Ataur Rahman ()
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Nilufa Afrin: Western Sydney University
Farhad Ahamed: Western Sydney University
Ataur Rahman: Western Sydney University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2024, vol. 120, issue 12, No 37, 11349-11376

Abstract: Abstract Flood is one of the worst natural disasters, which causes significant damage to economy and society. Flood risk assessment helps to reduce flood damage by managing flood risk in flood affected areas. For ungauged catchments, regional flood frequency analysis (RFFA) is generally used for design flood estimation. This study develops a Convolutional Neural Network (CNN) based RFFA technique using data from 201 catchments in south-east Australia. The CNN based RFFA technique is compared with multiple linear regression (MLR), support vector machine (SVM), and decision tree (DT) based RFFA models. Based on a split-sample validation using several statistical indices such as relative error, bias and root mean squared error, it is found that the CNN model performs best for annual exceedance probabilities (AEPs) in the range of 1 in 5 to 1 in 100, with median relative error values in the range of 29–44%. The DT model shows the best performance for 1 in 2 AEP, with a median relative error of 24%. The CNN model outperforms the currently recommended RFFA technique in Australian Rainfall and Runoff (ARR) guideline. The findings of this study will assist to upgrade RFFA techniques in ARR guideline in near future.

Keywords: Regional flood frequency analysis; Artificial intelligence; Convolutional neural network; Support vector machine; Decision tree (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11069-024-06669-z

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