SCIENTIFIC BASIS FOR IMPROVING THE EMERGENCY PREDICTION SYSTEM

Аннотация

This article examines the scientific basis for improving emergency situation (ES) forecasting systems. The study analyzes ways to increase the accuracy, speed, and efficiency of forecasting using artificial intelligence (AI) and big data technologies [1]. Studies conducted on the example of seismically active regions of Uzbekistan, in particular the Fergana Valley, have shown the advantages of new systems - 20% increased accuracy, 30% reduced time, and 80% adaptation to local infrastructure. The article emphasizes the importance of modern approaches to preventing economic and social damage from ES. At the same time, the financial and technical constraints on the implementation of AI and big data in Uzbekistan were also considered. Although the study is limited by the lack of real-world testing, it provides suggestions for future adaptation to local conditions. The article presents practical solutions for increasing security and optimizing resources. Keywords: emergency situations, forecasting systems, artificial intelligence, big data, seismic activity, security, Uzbekistan. Introduction. Disasters (D) including earthquakes, floods, and man-made disasters pose a significant threat to human life, infrastructure, and the economy. In seismically active countries such as Uzbekistan, such as the Fergana Valley and Tashkent region, D-Earthquake early warning systems are a matter of life and death [2]. Current systems are often limited to seismic sensors and suffer from problems with accuracy (about 65%) and speed (10 seconds warning). This creates difficulties in evacuation and damage reduction [3]. Therefore, it is urgent to improve the systems using modern technologies - artificial intelligence (AI) and big data. This study aims to study the scientific foundations of flood forecasting, test the capabilities of AI algorithms and big data, and identify ways to implement them in Uzbekistan. The study is aimed at reducing the risk of flood and ensuring the safety of the population, using international experience (Japan, USA) and local statistics. As a result, the effectiveness of new systems and their adaptability to local conditions will be proven. Literature review. Research on earthquake prediction has been rapidly developing in recent years. In Japan, earthquake prediction systems are based on seismic sensors and real-time data analysis, which has reduced warning times to 10-15 https://scientific-jl.com/new 11 33 11 Volume–78_Issue-1_June-2025 JOURNAL OF NEW CENTURY INNOVATIONS seconds [5]. In the USA, big data and AI have made climate earthquake (e.g., hurricane) predictions 20% more accurate. Uzbekistan has national strategies for earthquake management, but there is a lag in integrating modern technologies. International studies have shown that AI algorithms are effective in identifying complex patterns in earthquake prediction [4]. However, in developing countries, infrastructure and financial constraints make it difficult to implement these technologies. Uzbek scientists emphasize the need to take into account local conditions when managing PV. This review will help identify the scientific basis for improvement. Methodology. The study is based on a literature review and analysis of existing systems. Data were collected from international experiences, Uzbek statistics, and scientific publications. The following methods were used in detail: Data analysis: Historical data on seismicity (earthquake frequency, flood risk) was processed using big data technology. For example, a 50-year seismic database in the Fergana Valley was analyzed, which helped identify hazardous areas. The analysis was carried out using special algorithms in the Python programming language. Modeling: Prediction models were tested using SI algorithms, in particular, machine learning (Random Forest and Neural Networks). Data from 1000 FV cases were used for testing, 70% of which were allocated for training and 30% for testing. Comparison: The performance of current systems (based only on seismic sensors) and improved approaches (integrated with SI) was compared. For example, accuracy, error rate, and time parameters were measured, and the results were presented in tabular form. Expert assessment: Interviews were conducted with 5 experts on FV (employees of the Ministry of Emergency Situations of Uzbekistan and scientists). The interviews discussed the suitability of the systems for local conditions, their costs, and practical application (the analysis was conducted using the example of the Fergana Valley of Uzbekistan). Results. The study identified the following results in improving PV prediction systems: Increased accuracy: SI algorithms have increased the accuracy of earthquake prediction by 15-20%. For example, while traditional sensors show an accuracy of 65%, with the help of SI this indicator has reached 80-85%, as machine learning has analyzed seismic patterns in more depth. Speed: Big data technology has reduced the time it takes to