Estuarine flood modelling using Artificial Neural Networks

被引：0
|
作者：
Fazel, Seyyed Adel Alavi [1 ]
Blumenstein, Michael [1 ]
Mirfenderesk, Hamid [2 ]
Tomlinson, Rodger [3 ]
机构：
[1] Griffith Univ, Sch Informat & Commun Technol, Gold Coast Campus, Nathan, Qld 4111, Australia
[2] Gold Coast City Council, Gold Coast, Australia
[3] Griffith Univ, Sch Environm, Gold Coast Campus, Nathan, Qld 4111, Australia
来源：
PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2014年
关键词：
RAINFALL-RUNOFF MODELS;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：631 / 637
页数：7
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