Application of Artificial Neural Networks on Flood Routing and Forecasting
Date Issued
2007
Date
2007
Author(s)
Chung, Shih-Feng
DOI
zh-TW
Abstract
Taiwan located at the sub-tropic monsoon climate area. Typhoon occurrences often cause huge damages in summers and autumns. Flood forecasting model is a useful tool to capture precise meteorological and hydrological information for flood mitigations and responses emergency. Traditional flood forecasting model using the rainfall-runoff forecasting technique was limited to long time computation due to complicated approach. In order to obtain hydrological information in upriver rapidly, this study uses artificial neural networks (ANN) model to predict the stage of upriver in observed stations along a river. Moreover, for the sake of forecasting the stage of each section in the river, this study utilizes real-time observed stage to correct the initial stage with the incorporation of flood routing process for flood forecasting. Three typhoon events were simulated to confirm the accuracy of the forecasting model. The results are compared among dynamic routing, initial stage correction for forecasting, and integrated flood forecasting model with ANN, so as to obtain the suitable approach from three methods.
The results reveal that the accuracy of dynamic routing is seriously influenced by predicting stage in upriver, lateral inflow discharge, and pumping stations discharge. The initial stage of forecasting is able to well predict the stage for the lead time of two hours, but less performance after three hours. Moreover, the accuracy of predicting stage at stage stations has significant influence on integrated flood forecasting model with ANN, which the lateral inflow discharge and pumping stations discharge become less influence. By referencing the above forecasting information, better decisions can be made by the emergency operation agency during storm occurrences.
Subjects
動力波
初始值修正
整合類神經網路水位預報
洪水位預報
類神經網路
Dynamic routing model
initial stage of forecasting
integrated flood forecasting model with ANN
Artificial Neural Network
flood forecasting
Type
thesis
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