GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment

2026-05-14

AI Summary

This study addresses the growing threat of urban heatwaves, exacerbated by climate change, by proposing a GPU-accelerated ConvLSTM deep learning framework for accurate next-day thermal environment forecasting and integrated heat risk assessment. The model uniquely combines MODIS land surface temperature data with Open-Meteo meteorological forecasts and introduces mixed-precision training—a first in urban heatwave prediction—to enhance computational efficiency. By incorporating exposure and vulnerability indicators, the framework enables multidimensional heat risk evaluation. Evaluated in Sarajevo, the model achieves high predictive accuracy with an MAE of 0.2293, RMSE of 0.3089, and R² of 0.8877, while significantly reducing training time and generating high-resolution heat risk maps for actionable urban planning insights.

Abstract

Heatwaves are an important problem in cities, and climate change makes this problem more difficult. In this paper, we present a GPU-based deep learning framework for next-day prediction of urban thermal conditions and for heat risk assessment. The study was carried out in Sarajevo by using MODIS land surface temperature data and Open-Meteo forecast data. We tested several models, including convolutional models and spatiotemporal models. Among them, ConvLSTM with a mixed loss function gave the best results. The obtained values were MAE = 0.2293, RMSE = 0.3089, and R2 = 0.8877. The experiments also showed that results can be improved by using longer temporal series and additional meteorological variables. Since the framework was implemented on a GPU and trained with mixed precision, the execution time was reduced. Based on the predicted temperature fields, it was also possible to combine hazard information with exposure and vulnerability data in order to generate city heat risk maps. The proposed framework can be used as a practical basis for city heat analysis.

Problem

• heatwave prediction
• urban heat risk
• thermal conditions
• climate change
• risk assessment

Innovation

• GPU-accelerated deep learning
• ConvLSTM
• heatwave prediction
• urban heat risk assessment
• mixed loss function