Self-organizing radial basis neural network for predicting typhoon-induced losses to rice

The issue of the typhoon-induced economic losses to rice is investigated. In this study, we propose a hybrid self-organizing radial basis (SORB) neural network for estimating economic losses of rice for the whole Taiwan as well as three sub-regions. The data sets of 143 typhoon events from 1961 to 2008 were collected and analyzed. Data include rice losses and typhoon-related meteorological factors. A number of different input combinations of meteorological and temporal variables are implemented to select the optimal network for predicting the losses, and a two-stage clustering method is used to explore the spatial classification of 15 counties in Taiwan into three sub-regions. The simulation results indicate that the constructed SORB network has a great ability to capture the relationship between typhoon-related variables and rice losses. Furthermore, the SORB model also demonstrates its outstanding reliability and predictability for efficiently providing a valuable reference for counties in Taiwan that could protect farmers from exposure to increasing weather-related risk and accelerate the official decision making process on compensation for rice losses after the invasion of typhoons.