用GPU加速深度学习预测城市热浪，提升风险评估精度。
GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment
- 基于ConvLSTM与混合损失函数，融合遥感与气象数据预测热浪。
- 模型MAE达0.2293，RMSE为0.3089，决定系数R²达0.8877。
- 适合城市规划与气候应急部门用于热风险地图生成。
热浪是城市面临的重要问题，气候变化加剧了这一挑战。本文提出一种基于GPU的深度学习框架，用于次日城市热环境预测与热风险评估。研究以萨拉热窝为例，使用MODIS地表温度数据和Open-Meteo预报数据，测试了多种卷积模型与时空模型。其中，采用混合损失函数的ConvLSTM表现最佳，得到MAE = 0.2293，RMSE = 0.3089，R² = 0.8877。实验表明，使用更长的时间序列及额外气象变量可进一步提升性能。由于框架在GPU上运行并采用混合精度训练，显著降低了执行时间。基于预测温度场，可融合灾害暴露与脆弱性数据，生成城市热风险地图。该框架可作为城市热分析的实用基础。
原文摘要 · Abstract (English)
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.
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