Improving the Long Lead-Time Inundation Forecasts Using Effective Typhoon Characteristics

Abstract

In this paper, a new type of inundation forecasting model with the effective typhoon characteristics is proposed by integrating support vector machine (SVM) with multi-objective genetic algorithm (MOGA). Firstly, a comparison of the proposed model and an existing model based on back-propagation network (BPN) is made to highlight the improvement in forecasting performance. Next, the proposed model is compared with the SVM-based model without typhoon characteristics to investigate the influence of typhoon characteristics on inundation forecasting. Effective typhoon characteristics for improving forecasting performance are identified as well. An application to Chiayi City, Taiwan, is conducted to demonstrate the superiority of the proposed model. The results confirm that the proposed model with the effective typhoon characteristics does improve the forecasting performance and the improvement increases with increasing lead-time, especially for long lead-time forecasting. The proposed model is capable of optimizing the input to decrease the negative impact when increasing forecast lead time. In conclusion, effective typhoon characteristics are recommended as key inputs for inundation forecasting during typhoons.