Stochastic Simulation of Typhoon in Northwest Pacific Basin Based on Machine Learning

Abstract

Typhoons have caused serious economic losses and casualties in coastal areas all over the world. The big size of the tropical cyclone sample by stochastic simulation can effectively evaluate the typhoon hazard risk, and the typhoon full-track model is the most popular model for typhoon stochastic simulation. Based on the advantages of machine learning in dealing with nonlinear problems, this study uses a backpropagation neural network (BPNN) to replace the regression model in the empirical track model, reestablishes the neural network model for track and intensity prediction in typhoon stochastic simulation, and constructs full-track typhoon events of 1000 years for Northwest Pacific basin. The validation results indicate that the BPNN can improve the accuracy of typhoon track and intensity prediction.