Hydrology and Earth System Sciences www.hydrol-earth-syst-sci.net 1027-5606 1607-7938 13 8 2009 10.5194/hess-13-1413-2009 http://www.hydrol-earth-syst-sci.net/13/1413/2009/ http://www.hydrol-earth-syst-sci.net/13/1413/2009/hess-13-1413-2009.html http://www.hydrol-earth-syst-sci.net/13/1413/2009/hess-13-1413-2009.pdf 1413 1425 2009-08-07 An artificial neural network model for rainfall forecasting in Bangkok, Thailand N. Q. Hung nguyenquang.hung@ait.ac.th M. S. Babel S. Weesakul N. K. Tripathi School of Engineering and Technology, Asian Institute of Technology, Thailand This paper presents a new approach using an Artificial Neural Network technique to improve rainfall forecast performance. A real world case study was set up in Bangkok; 4 years of hourly data from 75 rain gauge stations in the area were used to develop the ANN model. The developed ANN model is being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. Aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information. Preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness), the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input data, advanced ANN model to apply with continuous data containing rainy and non-rainy period, allowed model to issue forecast at any moment. Additionally, forecasts by ANN model were compared to the convenient approach namely simple persistent method. Results show that ANN forecasts have superiority over the ones obtained by the persistent model. Rainfall forecasts for Bangkok from 1 to 3 h ahead were highly satisfactory. 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