Regional Weather Sensitivity Analysis and Power Outage Risk Assessment Algorithm Combining GIS and AI

Based on GIS (Geographic Information System) and AI (Artificial Intelligence), a regional meteorological sensitivity analysis method is studied. But in the existing research methods, the data acquisition is difficult, the consideration of spatial correlation is not enough, the model complexity is not enough, the prediction accuracy is not high. Aiming at the above problems, this article can adopt GIS technology to collect and process multi-source, multi-scale and multi-space-time meteorological and geographic information. On this basis, the sensitivity of meteorological elements is analyzed in real time, and the fault risk of power system is evaluated to provide accurate early warning and decision support. The purpose of this project is to enhance the recognition of regional weather sensitivity and power grid loss risk, and to provide scientific basis and technical support for relevant departments and policymakers. Experimental results show that the method is effective, and the sensitivity of artificial neural network based on artificial neural network to regional meteorological sensitivity can reach 0.87. This project can make up for the shortcomings of existing research methods and improve the accuracy and practicability of regional meteorological sensitivity analysis and failure risk assessment.