Typhoon flood forecasting using integrated two-stage Support Vector Machine approach

Accurate runoff forecasts are essential for flood mitigation and warning. In this paper, a two-stage flood forecasting model that is based on Support Vector Machine (SVM) is presented. In the first stage, the observed typhoon characteristics and observed rainfall are used to produce rainfall forecast; and in the second stage, the forecasted rainfall and observed runoff are used to produce runoff forecast. A dataset of 16 typhoon storms from Taiwan were used to evaluate the two-stage SVM model. The SVM model generated accurate rainfall and runoff forecasts with a 1–6 h lead time, especially for the peak runoff values. A substantial performance improvement of flood forecast is shown for the 4- to 6-h lead time. In conclusion, the SVM model provides an operational advantage by increasing the forecast lead time during typhoon events.