D. Krivoguz

Spatiotemporal dynamics of urban thermal environments shaped by surface characteristics and their ecological implications

Interpretable geospatial machine learning for seasonal land surface temperature prediction using multi-source earth observation data

An interpretable machine learning framework to predict seasonal MODIS clear-sky daytime LST across Rostov Oblast, Russia, using multi-source Earth observation data from 2015 to 2024 provides a spatially explicit and interpretable approach for seasonal clear-sky daytime LST prediction in understudied temperate continent...

Hybrid ensemble machine learning algorithms for landscape ecological vulnerability assessment to riverbank erosion

Vulnerability zones were most concentrated in the middle and lower reaches of the study area particularly in the states of Bihar and West Bengal and Sensitivity analysis revealed that elevation, rainfall, soil type and geomorphology were the most influential factors.