Title :
Estuarine flood modelling using artificial neural networks
Author :
Fazel, Seyyed Adel Alavi ; Blumenstein, Michael ; Mirfenderesk, Hamid ; Tomlinson, Rodger
Author_Institution :
Sch. of Inf. & Commun. Technol., Griffith Univ. Gold Coast Campus, Gold Coast, QLD, Australia
Abstract :
Prediction of water levels at estuaries poses a significant challenge for modelling of floods due to the influence of tidal effects. In this study, a two-stage forecasting system is proposed. In the first stage, the tidal portion of the available records is used to develop a tidal prediction system. The predictions of the first stage are used for flood modelling in the second. Experimental results suggest that the proposed flood modelling approach is advantageous for forecasting flood levels with more than 1 hour lead times.
Keywords :
floods; forecasting theory; geophysics computing; neural nets; tides; artificial neural networks; estuarine flood modelling; flood level forecasting; tidal effects; tidal prediction system; two-stage forecasting system; water level prediction; Artificial neural networks; Floods; Forecasting; Mathematical model; Predictive models; Time series analysis; Training;
Conference_Titel :
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6627-1
DOI :
10.1109/IJCNN.2014.6889704
