Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development

• المؤلفون الرئيسيون: Pande C.B., Egbueri J.C., Costache R., Sidek L.M., Wang Q., Alshehri F., Din N.M., Gautam V.K., Chandra Pal S.
• مؤلفون آخرون: 57193547008
• التنسيق: مقال
• منشور في: Elsevier Ltd 2025
• الموضوعات: Adaptive boosting; Atmospheric temperature; Climate models; Correlation methods; Decision making; Forecasting; Land surface temperature; Land use; Machine learning; Rain; Satellites; Surface measurement; Surface properties; Sustainable development; Energy; Ensemble models; Google earth engine; Google earths; LANDSAT; Machine-learning; Normalized difference vegetation index; Satellite data; SDG; Climate change

Accurate prediction of Land Surface Temperature (LST) is critical for understanding and mitigating the effects of climate change and land use dynamics. This study proposes a novel approach that leverages ensemble models and correlation analysis based on Landsat-8 satellite data to forecast LST and e...