Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/64934
Title: Real-time prediction of water stage with artificial neural network approach
Authors: Chau, KW 
Cheng, CT
Issue Date: 2002
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2002, v. 2557, p. 715 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: An accurate water stage prediction allows the pertinent authority to issue a forewarning of the impending flood and to implement early evacuation measures when required. Existing methods including rainfall-runoff modeling or statistical techniques entail exogenous input together with a number of assumptions. In this paper, neural networks are used to predict real-time water levels in Shing Mun River of Hong Kong with different lead times on the basis of the upstream gauging stations or stage/time history at the specific station. The network is trained by using two different algorithms. It is demonstrated that the artificial neural network approach, which is able to provide model-free estimates in deducing the output from the input, is an appropriate forewarning tool. It is shown from the training and verification simulation that the water stage prediction results are highly accurate and are obtained in very short computational time. Both these two factors are important in water resources management. Besides, sensitivity analysis is carried out to evaluate the most suitable network characteristics including number of input neurons, number of hidden layers, number of neurons in hidden layer, number of output neurons, learning rate, momentum factor, activation function, number of training epoch, termination criterion, etc. under this specific circumstance. The findings lead to the reduction of any redundant data collection as well as the accomplishment of cost-effectiveness.
Description: AI 2002: Advances in Artificial Intelligence, 15th Australian Joint Conference on Artificial Intelligence Canberra, Australia, December 2-6, 2002
URI: http://hdl.handle.net/10397/64934
ISSN: 0302-9743 (print)
1611-3349 (online)
DOI: 10.1007/3-540-36187-1_64
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

WEB OF SCIENCETM
Citations

29
Last Week
0
Last month
Citations as of Sep 19, 2018

Page view(s)

32
Last Week
1
Last month
Citations as of Sep 16, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.