An artificial neural network model for rainfall forecasting in Bangkok, Thailand N. Q. Hung, M. S. Babel, S. Weesakul, and N. K. Tripathi School of Engineering and Technology, Asian Institute of Technology, Thailand
Abstract. 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. Sensitivity analysis indicated that the most
important input parameter besides rainfall itself is the wet bulb
temperature in forecasting rainfall.
Citation: Hung, N. Q., Babel, M. S., Weesakul, S., and Tripathi, N. K.: An artificial neural network model for rainfall forecasting in Bangkok, Thailand, Hydrol. Earth Syst. Sci., 13, 1413-1425, doi:10.5194/hess-13-1413-2009, 2009.