Integrating machine learning and deep learning with landscape metrics for urban heat island prediction

Abstract

Elevated temperatures in urban areas relative to surrounding rural areas, known as the urban heat island (UHI) effect, constitute a pressing challenge to urban sustainability, public health, and energy efficiency. With a comprehensive global dataset from NASA's Socioeconomic Data and Applications Center (SEDAC) that encompasses land surface temperature (LST) and different urban characteristics, this study investigates the UHI phenomenon. The UHI intensity was predicted using advanced machine learning models, random forest, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), multilayer perceptron (MLP), and long short-term memory (LSTM) with attention mechanism. The LSTM with attention achieved top R2:0.9998 (day) and 0.9992 (night). Key landscape metrics include urban area size, population, and location. We analyzed spatial temporal UHI patterns to identify local factors like geometry and vegetation. These findings are critical for urban planners and policy makers to identify targeted mitigation options, including green space expansion, the use of low thermal mass, and urban climate resilience strategies. These results advance predictive modeling, supporting resilient, and sustainable cities.