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

2022-07-14 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Geospatial AI · Depth: Intermediate, extended

Summary

A GPU-accelerated deep learning framework has been developed for next-day urban thermal condition prediction and heat risk assessment, focusing on Sarajevo. The framework integrates MODIS land surface temperature data with Open-Meteo forecast data, utilizing spatiotemporal models like ConvLSTM. Experiments showed that ConvLSTM with a mixed loss function achieved the best results, with MAE = 0.2293, RMSE = 0.3089, and R squared = 0.8877. Performance improved significantly with longer temporal series and additional meteorological variables from multiple locations. The framework's GPU implementation and mixed-precision training reduced execution time, enabling the generation of city heat risk maps by combining predicted temperature fields with exposure and vulnerability data.

Key takeaway

For urban planners and environmental modelers developing heatwave early warning systems, this framework demonstrates that integrating multi-source climate data with GPU-accelerated ConvLSTM models can significantly improve next-day urban thermal predictions. You should consider enriching your datasets with longer temporal series and diverse meteorological inputs to enhance model accuracy and generate more reliable heat risk assessments for targeted interventions.

Key insights

GPU-accelerated ConvLSTM models predict next-day urban thermal conditions and heat risk by integrating satellite and meteorological data.

Principles

• Dataset quality directly impacts predictive performance.
• Spatiotemporal models excel in urban thermal prediction.
• Hybrid loss functions improve prediction realism.

Method

The method involves combining MODIS LST and Open-Meteo data, preprocessing into $32\times 32$ grids, training ConvLSTM models with a hybrid loss function on GPU, and generating risk maps from predicted thermal fields, exposure, and vulnerability layers.

In practice

• Use multi-location meteorological data for better accuracy.
• Employ ConvLSTM for spatiotemporal urban heat modeling.
• Accelerate training with GPU and mixed precision.

Topics

• ConvLSTM
• GPU Acceleration
• Heatwave Prediction
• Urban Heat Risk Assessment
• MODIS LST Data

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.