Abstract
At present, the protection of the population from emergencies that occur daily and cause harm to people and the country's territory necessitates organizational measures for monitoring, research, forecasting, and prevention. This study focuses on different types of emergencies, including natural, social, and man-made disasters. With the increasing volume of information on the Internet, there is a growing need to analyze the continuous flow of data published on news websites. In this study, machine learning-based methods and approaches were utilized. A research analysis of emergency-related data was conducted, identifying the key factors influencing the frequency of incidents. Additionally, emergencies were classified and assessed based on their types. During the evaluation of various algorithms, the most effective machine learning methods were determined. Data was collected from open sources in text format and subsequently processed using natural language preprocessing techniques. By leveraging historical weather data for the city of Almaty, a correlation between emergencies and weather conditions was identified.