IAHR, founded in 1935, is a worldwide independent member-based organisation of engineers and water specialists working in fields related to the hydro-environmental sciences and their practical application. Activities range from river and maritime hydraulics to water resources development and eco-hydraulics, through to ice engineering, hydroinformatics, and hydraulic machinery.
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You are here : eLibrary : IAHR World Congress Proceedings : 34th Congress - Brisbane (2011) : Artificial neural networks based regional flood estimation methods for eastern australia: identifica...
Artificial neural networks based regional flood estimation methods for eastern australia: identification of optimum regions
Author :
In Australia, design flood estimation in smaller ungauged catchments is often carried out
using the rational method. Recently, application of quantile regression technique has been
investigated in Australia. In contrast to these traditional methods, Artificial Neural Networks (ANNs) can be applied to regional flood frequency analysis (RFFA). The ANNs do not impose a model structure on the data and can better deal with non-linearity of the input and output relationship. This paper focuses on the development and testing of the ANNs based RFFA methods for eastern Australia. A number of alternative regions are tested e.g. (a) each of the states of NSW, Victoria,Queensland and Tasmania is considered to be a separate region; (b) all these states form one region; and (c) summer and winter dominated parts of these states form two separate regions. Independent
testing shows that option (b) is the best performing and can provide quite accurate design flood estimates with a median relative error values in the range of 39% to 56%.
File Size : 731,336 bytes
File Type : Adobe Acrobat Document
Chapter : IAHR World Congress Proceedings
Category : 34th Congress - Brisbane (2011)
Article : n/a
Date Published : 26/07/2011
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