Probabilistic typhoon rainfall forecasting using a modified fuzzy inference model

Machine learning can yield timely forecasts of rainfall and flood hazard variables. Such an application of machine learning in disaster mitigation is a key topic in hydroinformatics. In this study, a modified fuzzy inference model-featuring revised implication and defuzzification processes-was used to perform probabilistic typhoon rainfall forecasting. The revised implication process exerted a higher weight of the rainfall variable than other typhoon variables on forecasting. The defuzzification process was modified into a resampling process to generate a predicted probability distribution. The modified fuzzy inference model was applied to real-time probabilistic typhoon rainfall forecasting with lead times of 1 to 3 hours. Predicted confidence intervals with respect to actual typhoon rainfall data validated the capability of the probabilistic forecasting method, although the predicted confidence intervals are wider than the perfect interval in view of a quantitative measure of the reliability diagram. In addition, the probabilistic forecasts could be condensed into deterministic forecasts using the median of the predicted confidence interval. The deterministic forecasts also had satisfactory performance in typhoon rainfall forecasting.