Integration of Fuzzy C-Means and artificial neural network for short-term localized rainfall forecasting in tropical climate

This paper proposes and analyses the applicability of integrating Fuzzy C-Means (FCM) and artificial neural network (ANN) in rainfall forecasting. The algorithm of ANN and FCM clustering are integrated and applied to forecast short-term localized rainfall in tropical weather. Rainfall forecasting in this paper is divided into state forecast (raining or not raining) and rainfall rate forecast. Various type of back propagation extended network with hidden layers of ANN structured were trained. Training algorithm of Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient are used and trained. Transfer function in each neuron uses linear, logistic sigmoid and hyperbolic tangent sigmoid. Initial statistical analysis of weather parameter, data pre-processing approach and FCM clustering method were used to organize input data for the ANN forecast model. Input parameters such as atmospheric pressure, temperature, dew point, humidity and wind speed have been used. One to six hour predicted rainfall forecast are compared and analyzed. The result indicates that the integrated of FCM-ANN forecast model yield 80% for 1 h forecast.