FIRE HAZARD FORECASTING BASED ON NEURAL NETWORK PROCESSING OF SPACE MONITORING DATA

This article explores the potential of artificial intelligence technologies for fire risk forecasting. It examines current applications of artificial intelligence technologies in crisis management systems and analyzes existing solutions for using Earth remote sensing data for emergency prevention. An approach is proposed that includes processing statistical data on heat points, visualizing them, and then forecasting them using machine learning and deep learning methods. A functional model for converting raw data into fire hazard indicators has been developed, and the results are visualized in the form of heat maps. The practical significance of this study lies in its potential to support management decision-making at the municipal level.