Regional Flood Frequency Analysis Using an Artificial Neural Network Model

This paper presents the results from a study on the application of an artificial neural network (ANN) model for regional flood frequency analysis (RFFA). The study was conducted using stream flow data from 88 gauging stations across New South Wales (NSW) in Australia. Five different models consistin...

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Bibliographic Details
Published in:Geosciences (Basel) 2020-04, Vol.10 (4), p.127
Main Authors: Kordrostami, Sasan, Alim, Mohammad A, Karim, Fazlul, Rahman, Ataur
Format: Article
Language:English
Subjects:
artificial neural network
floods
regional flood frequency
ungauged catchments
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Summary:This paper presents the results from a study on the application of an artificial neural network (ANN) model for regional flood frequency analysis (RFFA). The study was conducted using stream flow data from 88 gauging stations across New South Wales (NSW) in Australia. Five different models consisting of three to eight predictor variables (i.e., annual rainfall, drainage area, fraction forested area, potential evapotranspiration, rainfall intensity, river slope, shape factor and stream density) were tested. The results show that an ANN model with a higher number of predictor variables does not always improve the performance of RFFA models. For example, the model with three predictor variables performs considerably better than the models using a higher number of predictor variables, except for the one which contains all the eight predictor variables. The model with three predictor variables exhibits smaller median relative error values for 2- and 20-year return periods compared to the model containing eight predictor variables. However, for 5-, 10-, 50- and 100-year return periods, the model with eight predictor variables shows smaller median relative error values. The proposed ANN modelling framework can be adapted to other regions in Australia and abroad.
ISSN:2076-3263
2076-3263
DOI:10.3390/geosciences10040127