Abstract
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.
Highlights
Floods are the most damaging natural disasters that cause enormous economic loss and social disruptions across the landscape
This study was conducted based on eight predictor variables that included (i) mean annual rainfall (MAR); (ii) areal potential evapo-transpiration (MAE), (iii) drainage area (AREA) (iv) 6-hour duration rainfall for a 2-year return period (I62); (v) shape factor (SF); (vi) stream density (SDEN); (vii) river slope (S1085) and (viii) proportion of forest (FOREST)
The results show similar performances by Model 1 and Model 3, with little differences in the root mean squared error (RMSE) values for all six flood quantiles
Summary
Floods are the most damaging natural disasters that cause enormous economic loss and social disruptions across the landscape. Floods accounted for roughly 45% of all disasters (and people affected by them) and caused an average of 6000 casualties in each year [1]. Floods cause billions of dollars of damage annually worldwide, and even in the world’s driest inhabited continent, Australia, flooding is the costliest natural disaster [1,2,3]. Floods have become more frequent and highly disastrous due to global climate change [1]. A design flood is the peak discharge used to design hydraulic structures (e.g., bridge, culvert, retaining wall) and the magnitude of the flood is represented by the annual exceedance probability (AEP) [4]
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