Prediction of rainfall time series using modular soft computingmethods

In this paper, several soft computing approaches were employed for rainfall prediction. Two aspects were considered to improve the accuracy of rainfall prediction: (1)carrying out a data-preprocessing procedure and (2)adopting a modular modeling method. The proposed preprocessing techniques included moving average (MA) and singular spectrum analysis (SSA). The modular models were composed of local support vectors regression (SVR) models or/and local artificial neural networks (ANN) models. In the process of rainfall forecasting, the ANN was first used to choose data-preprocessing method from MA and SSA. Modular models involved preprocessing the training data into three crisp subsets (low, medium and high levels) according to the magnitudes of the training data, and finally two SVRs were performed in the medium and high-level subsets whereas ANN or SVR was involved in training and predicting the low-level subset. For daily rainfall record, the low-level subset tended to be modeled by the ANN because it was overwhelming in the training data, which is based on the fact that the ANN is very efficient in training large-size samples due to its parallel information processing configuration. Four rainfall time series consisting of two monthly rainfalls and two daily rainfalls from different regions were utilized to evaluate modular models at 1-day, 2-day, and 3-day lead-time with the persistence method and the global ANN as benchmarks. Results showed that the MA was superior to the SSA when they were coupled with the ANN. Comparison results indicated that modular models (referred to as ANN-SVR for daily rainfall simulations and MSVR for monthly rainfall simulations) outperformed other models. The ANN-MA also displayed considerable accuracy in rainfall forecasts compared with the benchmark.