@inproceedings{4f01f4c33286416aaedf591e2782475e,
title = "Application of artificial neural networks for regional flood estimation in Australia : formation of regions based on catchment attributes",
abstract = "Most of the traditional regional flood frequency analysis (RFFA) methods are based on linear models. Artificial neural networks (ANNs) can be used to develop non-linear models in RFFA. This paper uses data from 452 gauging stations from eastern Australia to identify the optimum regions in the ANN-based RFFA modeling in Australia. From an independent testing, it has been found that that K-Means cluster analysis generate the best performing regions in the catchment characteristics data space with two regions. However, the best ANN-based RFFA model is achieved when all the data set of 452 catchments are combined together.",
author = "K. Aziz and A. Rahman and G. Fang and S. Shrestha",
year = "2011",
doi = "10.4203/ccp.97.30",
language = "English",
isbn = "9781905088461",
publisher = "Civil-Comp Press",
booktitle = "Proceedings of the Second International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering, Chania, Crete, Greece, 6-9 September 2011",
note = "International Conference on Soft Computing Technology in Civil_Structural and Environmental Engineering ; Conference date: 06-09-2011",
}