GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment
For urban planners and climate adaptation teams, this provides a practical tool for heatwave prediction and risk mapping, though it is an incremental application of existing methods to a specific city.
The paper presents a GPU-accelerated deep learning framework for next-day urban heat prediction and risk assessment, achieving MAE=0.2293, RMSE=0.3089, and R2=0.8877 using ConvLSTM with mixed loss on Sarajevo data.
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.