基于卫星资料的西太平洋台风强度监测机器学习方法对比研究OA

Typhoons are natural disasters that severely impact coastal areas,and accurately monitoring their intensity is crucial for disaster prevention and mitigation.Combining satellite observations with deep learning technology has become a pomising new method for typhoon intensity monitoring.However,the accuracy of different deep learning methods remains unclear.This study evaluates the performance of six convolutional neural network models based on deep learning in monitoring typhoon intensity in the Western Pacific.Using Himawari-8/9 satellite cloud product data and the China Meteorological Administration's best track data from 2015 to 2024,we analyzed the computational effectiveness of LeNet-5,AlexNet,DenseNet-121,VGG-16,ResNet-50,and GoogleNet-InceptionV3 models.This study not only explores the applicability and performance of these models in different scenarios but also visualizes the feature extraction steps of the models to clarify the differences between them and their working principles.LeNet-5 and AlexNet show the largest biases in extremely weak(TD)and extremely strong(SuperTY)categories;DenseNet-121 maintains relatively uniform bias distribution across all intensity levels;ResNet-50,VGG-16,and GoogleNet-InceptionV3 demonstrate stable performance in medium intensity ranges(TS,STS,TY,STY).Overall,the GoogleNet-InceptionV3 model achieves the highest accuracy with an R² of 0.89,while ResNet-50,with an R²of 0.87,offers faster computational speed.