GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment
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Computer Science > Machine Learning
Title:GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment
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.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.16435 [cs.LG] |
| (or arXiv:2605.16435v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.16435
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Adis Alihodžić Prof. dr. [view email][v1] Thu, 14 May 2026 21:13:09 UTC (1,602 KB)
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