Scenario-Based Assessment of Earth-Dam Stability Using Open Geospatial Data and Machine Learning: A Case Study of the Sherubay-Nur Reservoir, Kazakhstan

This article presents an integrated computational framework for scenario-based stability assessment of earth dams using open-access satellite data (Sentinel-2, Landsat 8/9), aerial imagery captured by UAVs, the FABDEM digital elevation model, and machine learning methods. The approach enables water surface detection, reservoir area calculation, water level reconstruction, and subsequent stability assessment via limit equilibrium modelling (Rocscience Slide2D). Tested on the Sherubay-Nur Reservoir (Kazakhstan), the method established a morphometric relationship between water level (534.20–536.80 m) and surface area (30.41–34.39 km2). Stability calculations showed a decrease in the factor of safety from 1.207 to 1.187 as the water level rose. Although all calculated values exceeded 1.0, this condition alone does not demonstrate compliance with the applicable stability criteria; the results indicate a consistent decrease in the factor of safety with increasing reservoir water level. The methodology developed does not replace field hydrometric, geodetic and geotechnical observations, but can be used as an additional analytical tool for retrospective analysis, supplementary model-based estimation of water levels and preliminary scenario-based assessment of the condition of hydraulic structures, particularly where on-site observations are incomplete or access to the site is restricted.