An artificial neural network model for rainfall forecasting in

Abstract. This paper presents a new approach using an Arti-ficial 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 forecast-ing 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 in-formation. Preliminary tests showed that a generalized feed-forward ANN model using hyperbolic tangent transfer func-tion achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (rela-tive humidity, air pressure, wet bulb temperature and cloudi-ness), 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 mo-ment. 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. Sen-sitivity analysis indicated that the most important input pa-rameter besides rainfall itself is the wet bulb temperature in forecasting rainfall.