In order to forecast the flood discharge of downstream gauging station by the artificial neural network (ANN) algorithm efficiently, the linear transfer function method (LTF) and parameter significance T-test are proposed to determine the number of network input elements. In addition, time series ARIMA model for all upstream gauging stations is constructed to offer the forecasting discharges which are input data for watershed ANN flood forecasting model. From the application in Wu-Shi basin, the model verified results of the following one hour through the following three hours flood forecasting are good. One may conclude that the algorithm of time series ANN flood forecasting can simulate the phenomena of flood transportation and forecast the flood discharge of watershed efficiently.
|Number of pages||6|
|State||Published - 1 Jan 1998|
|Event||Proceedings of the 1998 International Water Resources Engineering Conference. Part 2 (of 2) - Memphis, TN, USA|
Duration: 3 Aug 1998 → 7 Aug 1998
|Conference||Proceedings of the 1998 International Water Resources Engineering Conference. Part 2 (of 2)|
|City||Memphis, TN, USA|
|Period||3/08/98 → 7/08/98|