An improved machine learning-based model for prediction of diurnal and spatially continuous near surface air temperature.

ABSTRACT: Near-surface air temperature (Tair) is crucial for assessing urban thermal conditions and their impact on human health. Traditional Tair estimation methods, reliant on sparse weather stations, often miss spatial variability. This study proposes a novel framework using a federated learning artificial neural network (FLANN) for fine-scale Tair prediction. Leveraging spatially complete thermal data from Landsat 8/9, Sentinel 3, and Himawari 8/9 (105 acquisition days, 2013-2023), and data from automatic weather stations, 23 predictor variables were extracted. After rigorous selection processes, nine variables significantly correlated with Tair were identified. Comparative analysis against established machine learning and linear models, using cross-validation data, showed FLANN's superior perfo

SUBMITTER: Adeniran IA 

PROVIDER: S-EPMC11550329 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature