Typhoon Comprehensive Classification and Travel Route Intelligent Forecast Based on Convolutional Neural Networks

This study, based on typhoon data provided by the China Meteorological Administration and focusing on the critical issue of typhoon disaster warning and defense, thoroughly examines two core components: typhoon classification and intelligent prediction of typhoon paths. An innovative evaluation system that integrates time series clustering with hierarchical clustering is developed to standardize various indicators and accurately capture the characteristics of different typhoon categories. Furthermore, the combination of a three-dimensional convolutional neural network (CNN) and an improved long short-term memory network (LSTM) significantly enhances the accuracy and efficiency of path recognition. The experimental results demonstrate that the goodness of fit for wind speed as a category indicator and precipitation as a path indicator reaches 0.9973 and 0.9906, respectively, thereby validating the robust performance of the proposed model. This innovative outcome not only provides a new direction for typhoon path prediction but also substantially advances the field of typhoon disaster warning and defense.