Author(s): Tawatchai Tingsanchali; Chayanis Manusthiparom
Linked Author(s): Tawatchai Tingsanchali
Keywords: Neural networks; Back propagation; Flood Forecasting; Tidal rivers
Abstract: Flow in tidal rivers is greatly affected by tides and upland flood discharges. A neural network model with back-propagation algorithm (BPNN) is applied for forecasting hourly water levels and discharges in the Chao Phraya River at Bangkok Memorial Bridge (Station C4) which is located about 48 km from the river mouth. The considered river reach is from the river mouth at Fort Chula (km 1) to Bang Sai (km 112). The river flow is influenced by the effects of the upstream discharge at Bang Sai, the tide levels at Fort Chula and the local inflows along the river reach. The neural network model is trained and tested for three-hour ahead flood forecasting based on the observed hourly water level data during high stages in 1996,1995 and 1992. The accuracy of flood forecast is evaluated by using a statistical performance index and is found to be very satisfactory.
Year: 2001