КЕҢІСТІКТІК-УАҚЫТТЫҚ (SPATIOTEMPORAL) ТАЛДАУ НЕГІЗІНДЕ ГИДРОЛОГИЯЛЫҚ ҚАУІП-ҚАТЕРДІ ЕРТЕ АНЫҚТАУ

Abstract

This article examines the early detection of hydrological hazards using spatiotemporal analysis and multimodal data integration. The study is based on the creation of an event-based dataset combining geospatial, hydrometeorological, and satellite features for Kazakhstan over a long period of time. An approach to constructing a predictive model is proposed that takes into account only pre-event information, ensuring a correct formulation of the early warning problem. Various machine learning methods and their hybrid combinations were used to evaluate the effectiveness of the model. Experimental results demonstrate that integrating heterogeneous data sources improves the accuracy of flood risk forecasting. The findings confirm the potential of spatiotemporal models for developing early warning systems for hydrological hazards.