Research on 3D geographic information and path prediction algorithm for typhoon disaster

By integrating historical typhoon data, real-time meteorological data and marine environment data, this paper constructs a typhoon path prediction model based on 3D GIS, and combines deep learning and machine learning technology to mine the potential laws of typhoon path changes. The research focuses on the integration of multi-source data, including cloud image information provided by Fengyun meteorological satellite, ocean surface data monitored by ocean buoy and terrain data in geographic information database. Through the construction of 3D time series structure, a comprehensive spatial 3D model is formed, which can reflect the development and evolution of typhoon. The path prediction algorithm is based on CNN and LSTM networks of deep learning, which can automatically learn the spatial characteristics in typhoon images and the long-term dependence in time series data to realize accurate path prediction. The experimental results show that the proposed CNN-LSTM model is superior to the traditional numerical prediction method in prediction accuracy, with an mean absolute error (MAE) of 47.66 km and a root mean square error (RMSE) of 65.81 km, showing good performance and timeliness. This study provides a reliable scientific basis for disaster prevention and mitigation, and directly displays the prediction results through 3D GIS technology to assist decision support.