modeling neural network
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2009 ◽  
Vol 03 (02) ◽  
pp. 101-111 ◽  
Author(s):  
MONGKONKORN SRIVICHAI ◽  
FUMIHIKO IMAMURA ◽  
SEREE SUPHARATID

The 2004 Indian Ocean tsunami caused significant damages to Thailand. This great experience enhances the necessity of an early warning system. This paper describes three techniques for developing the tsunami warning system (Numerical modeling, Neural network modeling and web-based online displaying). Hypothetical earthquake and tsunami sources are obtained from past events and are mostly concentrated in the Andaman microplate. The GRNN is seen to provide reasonable predictions of wave heights for variable location, magnitude and depth of earthquake. The obtained online tsunami database warning system will be very useful for Thailand's response measure against the future damages.


2005 ◽  
Vol 32 (6) ◽  
pp. 1039-1050 ◽  
Author(s):  
Richard Turcotte ◽  
Anne-Catherine Favre ◽  
Pierre Lacombe ◽  
Charles Poirier ◽  
Jean-Pierre Villeneuve

Real-time assessments of stream flows under ice cover, using two distinct objective approaches, were compared over a 5-year period. Approaches based on artificial neural networks, defining mathematical relationships between stream flow, water level, and air temperature, and on a deterministic hydrological model were applied at eight gauged sites located in southern Quebec. Good results were obtained using both approaches, when no snowmelt contributes to the rise of the inflows. In the other hydrological situations, the neural network results were the best, but results of both approaches were sensibly poorer. Nevertheless, the potential for increasing the skills of the deterministic model seems high. Otherwise, a preliminary analysis showed that both approaches lead to stream-flow estimations that are not radically worse than the ones performed by the team of experts.Key words: discharge measurement, hydrology, ice-affected stream flow, hydrological modeling, neural network.


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