A Study of Applying Real-Time Hydrological Monitoring Data on River Flood Forecasting Model
Date Issued
2010
Date
2010
Author(s)
Lin, Shu-Horng
Abstract
Taiwan located at the sub-tropic monsoon climate area. Typhoon occurrences often cause huge damages in summers and autumns. An early warning system based on the accurate flood forecast with the real-time hydrological monitoring data can be used to reduce the flood damage effectively.
A flash flood routing model with artificial neural networks (ANN) predictions was developed for stage profiles forecasting. At gauge stations in a river the artificial neural networks were used to predict the 1-3 hour lead time river stages, which were taken as interior boundaries in the flash flood routing model for the forecast of longitudinal stage profiles, including un-gauged sites of a whole river. The flash flood routing model was based on the dynamic wave equations with discretization processes of the four-point finite difference method. Five typhoon events were applied to calibrate the rainfall-stage model and other three events were simulated to verify the model’s capability. The results revealed that the flash flood river routing model incorporating with artificial neural networks can provide accurate river stages for flood forecasting.
In addition, a single river segment flood forecasting model was developed for comparison. In the single segment model, the 1-3 hour lead time river stages predictions from the ANN at the two adjacent gauge stations are imposed as upstream and downstream boundaries, respectively. The results show that the single segment model can provide accurate 1-3 hour lead time stage forecast at un-gauged sites efficiently.
Subjects
Dynamic routing model
Unsteady flow
Flash flood routing
Artificial neural network
River stage forecasting
Type
thesis
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