College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China.
State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China.
Comput Intell Neurosci. 2022 Feb 17;2022:6760944. doi: 10.1155/2022/6760944. eCollection 2022.
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.
台风在全球沿海地区造成了严重的经济损失和人员伤亡。通过随机模拟获得的大型热带气旋样本可以有效地评估台风灾害风险，而台风全轨迹模型是台风随机模拟中最受欢迎的模型。基于机器学习在处理非线性问题方面的优势，本研究使用反向传播神经网络（BPNN）代替经验轨迹模型中的回归模型，重新建立了台风随机模拟中的轨迹和强度预测神经网络模型，并构建了西北太平洋盆地 1000 年的全轨迹台风事件。验证结果表明，BPNN 可以提高台风轨迹和强度预测的准确性。
