Investigate typhoon paths by self-organizing maps and predict long-term flow during typhoon periods

Taiwan is often attacked by typhoons due to its geographic location in Monsoon Asia. This island suffers from severe typhoon threats during summer and autumn in each year, and different typhoon paths may cause different disasters in Taiwan. For example, the heavy rainfall coupled with high intensity induced by Typhoons AERE (2004), KROSA (2007), MOLAVE (2009), SAOLA (2012) and SOULIK (2013) brought disasters in northern, middle and southern Taiwan, which made great challenges to reservoir operators. Taiwan has small catchments, steep-sloped terrains and rapid river flow. Therefore the development of forecast models for river flow or reservoir inflow are mainly of short-term scales, e.g. hourly forecasting, for providing a shorter response time to disaster management and flood mitigation. If we can provide flow prediction for the whole typhoon period before a typhoon hits Taiwan, it will help to make the regulation strategy of reservoir discharge and provide the reference guide of real-time flood control operation. In this study, we analyze the relationship between the classification of typhoon paths and reservoir inflow patterns to propose the methodology of long-term inflow prediction during the whole typhoon period. The main idea is to first classify typhoon paths by using the self-organizing maps (SOM), then define the flow characteristic curves based on the classification results of typhoon paths, and consequently combine all the results with the total rainfall forecast of the catchment to obtain the desired long-term inflow prediction. The proposed model is divided into two stages: typhoon path classification; and reservoir inflow forecast. In the stage of typhoon path classification, the latitude and longitude coordinates of each typhoon path are converted into a grid vector, and the SOM is used to classify all the grid vectors to generate a topological map of typhoon paths. As a result, the flow characteristic curve and statistics of each neuron in the topological map can be obtained. In the stage of reservoir inflow forecast, we use the testing typhoon events to make inflow forecasts. The typhoon path of each testing event is classified into a neuron of the SOM. Then long-term inflow prediction can be made for the whole typhoon period based on the flow characteristic curve, the runoff coefficient and the total rainfall forecast (provided by the Central Bureau in Taiwan) of the neuron into which the testing typhoon event is classified. The results indicate that the performances of the neurons in the topological map of typhoon paths are distinct from each other, and the topological relationship between a neuron and its neighboring neurons also shows slight differences in typhoon paths. Besides, the typhoon paths and peak flows in each neuron are quite similar, and the flow characteristic curves of most of the neurons in the SOM shows high similarity. The proposed inflow prediction model can grasp the trend for the whole typhoon period and accurately predict the timing of peak flow before a typhoon hits Taiwan. Results indicate that peak flow and total forecasted inflow also fall within the acceptable range, and the predicted and actual flow hydrographs produce a high correlation coefficient.