Development of an effective data-driven model for hourly typhoon rainfall forecasting

SummaryIn this paper, we proposed a new typhoon rainfall forecasting model to improve hourly typhoon rainfall forecasting. The proposed model integrates multi-objective genetic algorithm with support vector machines. In addition to the rainfall data, the meteorological parameters are also considered. For each lead time forecasting, the proposed model can subjectively determine the optimal combination of input variables including rainfall and meteorological parameters. For 1- to 6-h ahead forecasts, an application to high- and low-altitude metrological stations has shown that the proposed model yields the best performance as compared to other models. It is found that meteorological parameters are useful. However, the use of the optimal combination of input variables determined by the proposed model yields more accurate forecasts than the use of all input variables. The proposed model can significantly improve hourly typhoon rainfall forecasting, especially for the long lead time forecasting.

•A typhoon rainfall forecasting model integrating SVM and MOGA is proposed.•Dominant meteorological parameters affecting typhoon rainfall are determined.•The proposed model provides more accurate forecasts of hourly typhoon rainfall.•The proposed model significantly improves the long lead-time forecasts.