Spatiotemporal Modeling and Prediction of Urban Thermal Field Variation and Land Use Dynamics in Riyadh Using Machine Learning and Remote Sensing

Urban areas in arid environments are increasingly affected by the urban heat island (UHI) effect, which intensifies thermal stress, disrupts ecological balance, and poses challenges for sustainable urban development. Understanding and predicting spatiotemporal variations in land surface temperature (LST) and land use dynamics is therefore critical for effective urban planning. This study develops a predictive framework for Riyadh, Saudi Arabia, using long-term Landsat time series data (1993–2023) and deep learning models to evaluate urban thermal patterns via the Urban Thermal Field Variation Index (UTFVI). Artificial Neural Networks (ANNs) with six hidden layers for LST and seven for UTFVI forecast future trends up to 2043. The results indicate that urban areas expanded by 521.62 km2, increasing from 8.73% to 19.56% between 1993 and 2023, and are projected to reach 1509.40 km2 (25.28%) by 2043, while vegetation coverage declined from 0.771% to 0.674%. The highest average summer LST increased from 56.73 °C in 1993 to 59.89 °C in 2023 and is predicted to rise to 60.79 °C by 2033 and 61.52 °C by 2043. Winter temperatures exhibited a comparable upward trend, rising from 30.75 °C to 32.33 °C in 2023 and projected to reach 34.48 °C by 2043. UTFVI analysis revealed a substantial expansion of weak thermal field zones, which covered 2778 km2 in 2023 and are expected to reach 3018.44 km2 (57%) by winter 2043, accompanied by a marked contraction of strong thermal field areas. The ANN models achieved a high predictive performance, with RMSE values of 0.759 (summer) and 0.789 (winter) for UTFVI and correlation coefficients of 0.91 and 0.89, respectively. Projections further indicate that, by 2043, approximately 39.31% of the study area will experience summer temperatures between 48 °C and 53 °C, compared to 5.59% in 2023. These findings highlight the accelerating interaction between urban growth and thermal intensification in arid cities. The proposed modeling framework provides a robust decision-support tool for urban planners and policymakers to mitigate UHI impacts and promote climate-resilient and sustainable urban development.