Development of Avalanche Prediction Algorithms Based on a Set of Parameters

Denissova N. Petrova O. Fedkin E. Daumova G. Sergazinov E.
2025 Engineering and Technology Publishing

Journal of Advances in Information Technology
2025 #16 Issue 6 904 - 915 pp.

This article presents the results of a study conducted to develop an avalanche predictive model based on a set of climatic data. The research area includes the territory of East Kazakhstan, where a sharply continental climate prevails with hot summers and cold and snowy winters. With climate change, despite the low altitudes in this mountainous area, the problem of avalanche safety is acute in the region. To compile a avalanche predictive model, meteorological data from regional weather stations for 23 years (2001–2024) and meteorological observations in avalanche-prone areas for 19 years (2005–2024) were analyzed. This information was compared with the recorded data on spontaneous avalanches over the past 11 years (2013–2024). A database was created to carry out the research. The meteorological data is analyzed using mathematical statistics methods with the construction of probable trends of regional climatic changes. MATLAB data analysis has shown a significant relationship between sudden warming, increased wind speed, and precipitation that precedes avalanches. The analysis showed the need to take these parameters into account when developing a forecast model, as the likelihood of dangerous weather events will increase every year. The avalanche prediction was performed using regression analysis (logistic regression). The Loginom Community statistical software package is used for this purpose. The quality of the constructed predictive model was assessed. In the future, it will be used to predict spontaneous avalanche based on observations of meteorological data in avalanche-prone areas of the East Kazakhstan region.

avalanches , climate change , databases , logical database schema , logistic regression , model training , monitoring system , probability of event , regression analysis of data

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Department of Information Technology, D. Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk, Kazakhstan
School of Geosciences, D. Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk, Kazakhstan

Department of Information Technology
School of Geosciences

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