Spatial distribution and dynamic prediction algorithm of meteorological disasters based on three-dimensional geographic information system

By integrating high-precision remote sensing images, meteorological observation data and UAV aerial photography data, a 3D geographic information system (3D GIS) platform is constructed in this study, which reveals the distribution law of meteorological disasters in 3D space and realizes the dynamic prediction of disasters. The platform uses ArcGIS Pro of Esri as the main tool, and uses its powerful 3D data analysis capabilities, spatial interpolation, surface analysis and other tools, as well as Python programming and ArcGIS API for Python to realize the automation of data processing and analysis. Research methods include hot spot analysis, spatial clustering analysis and dynamic prediction model construction combining spatial regression analysis and time series analysis. In the experimental area, a mountainous and multi-water province in the south of China was selected, and multi-source data were integrated through ArcGIS Pro platform, key characteristic variables were extracted, and a dynamic prediction model was built. The experimental results show that the model has high accuracy in forecasting disasters such as floods and landslides, which provides a scientific basis for meteorological disaster early warning and management.