A real-time forecasting model for the spatial distribution of typhoon rainfall

Abstract

• We propose a model to yield 1- to 6-h ahead forecasts of hourly rainfall. • A proposal for spatial characteristics of the rainfall process is presented. • We identify the optimal input combinations for each lead time. • The model yields accurate forecasts of the spatial distribution of rainfall. Accurate forecasts of hourly rainfall are necessary for early warning systems during typhoons. In this paper, a typhoon rainfall forecasting model is proposed to yield 1- to 6-h ahead forecasts of hourly rainfall. First, an input optimization step integrating multi-objective genetic algorithm (MOGA) with support vector machine (SVM) is developed to identify the optimal input combinations. Second, based on the results of the first step, the forecasted rainfall from each station is used to obtain the spatial characteristics of the rainfall process is presented. An actual application to Tsengwen river basin is conducted to demonstrate the advantage of the proposed model. The results clearly indicate that the proposed model effectively improves the forecasting performance and decreases the negative impact of increasing forecast lead time. The optimal input combinations can be obtained from the proposed model for different stations with different geographical conditions. In addition, the proposed model is capable of producing more acceptable the results of rainfall maps than other model. In conclusion, the proposed modeling technique is useful to improve the hourly typhoon rainfall forecasting and is expected to be helpful to support disaster warning systems.