Avalanche Hazard Forecasting and Monitoring based on Meteorological Data and Remote Sensing Technologies using Google Earth Engine Platform

Rakhymberdina M. Denissova N. Bekishev Y. Daumova G. Shults R. Assylkhanova Z. Kapasov A.
September 2025 Engineered Science Publisher

ES Energy and Environment
2025 #29

This research presents an automated multi-factor model on the GEE platform, integrating ERA5-Land meteorological data, topographic information (SRTM), and vegetation cover characteristics (Copernicus) to forecast and monitor avalanche hazards across an area of 97,800 km² encompassing 497 avalanche-prone sites. The analysis of five winter seasons (2019-2024) is based on the following threshold criteria: cumulative 3-day snowfall ≥ 10 cm, slope steepness ≥ 30°, snow depth ≥ 60 cm, and temperature variation ≥ 28°C; the contribution of wind (≥ 8 m/s) was assessed separately. The resulting binary risk masks showed that the average area of high-risk zones was 2776,06 km² (2.83% of the territory). Validation using adjusted ground-based data demonstrated high overall accuracy (Accuracy = 0.92) and good precision (Precision = 0.73), with moderate recall (Recall=0.57; F1-score=0.64), indicating a minimization of false alarms at the cost of potentially missing some events. Sensitivity analysis confirmed the dominant influence of slope steepness: adjusting the threshold by ±10% changed the extent of hazardous zones from 1.38% to 3.72%. The wind factor had limited significance at the regional scale during the study period. The developed interactive GEE-based web application enables reproducible generation of risk maps and can support timely planning.

Avalanche monitoring , Combined mask , GIS technologies , Remote sensing

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School of Earth Sciences, D. Serikbayev East Kazakhstan Technical University, 19 Serikbayev street, Ust-Kamenogorsk, 070000, Kazakhstan
Department of Information Technology, D. Serikbayev East Kazakhstan Technical University, 19 Serikbayev street, Ust-Kamenogorsk, 070000, Kazakhstan
King Fahd University of Petroleum and Minerals, 4340 Academic Ring Road, Dhahran, 34463, Saudi Arabia

School of Earth Sciences
Department of Information Technology
King Fahd University of Petroleum and Minerals

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