Modular Deep-Learning-Based Early Warning System for Deadly Heatwave Prediction

• URL: http://arxiv.org/abs/2512.09074v1
• Date: Tue, 09 Dec 2025 19:37:49 GMT
• Title: Modular Deep-Learning-Based Early Warning System for Deadly Heatwave Prediction
• Authors: Shangqing Xu, Zhiyuan Zhao, Megha Sharma, José María Martín-Olalla, Alexander Rodríguez, Gregory A. Wellenius, B. Aditya Prakash,
• Abstract summary: DeepTherm is a modular early warning system for deadly heatwave prediction without requiring heat-related mortality history.<n>By highlighting the flexibility of deep learning, DeepTherm employs a dual-prediction pipeline, disentangling baseline mortality in the absence of heatwaves.<n>Results demonstrate consistent, robust, and accurate performance across diverse regions, time periods, and population groups while allowing trade-off between missed alarms and false alarms.
• Score: 53.09098740555834
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Severe heatwaves in urban areas significantly threaten public health, calling for establishing early warning strategies. Despite predicting occurrence of heatwaves and attributing historical mortality, predicting an incoming deadly heatwave remains a challenge due to the difficulty in defining and estimating heat-related mortality. Furthermore, establishing an early warning system imposes additional requirements, including data availability, spatial and temporal robustness, and decision costs. To address these challenges, we propose DeepTherm, a modular early warning system for deadly heatwave prediction without requiring heat-related mortality history. By highlighting the flexibility of deep learning, DeepTherm employs a dual-prediction pipeline, disentangling baseline mortality in the absence of heatwaves and other irregular events from all-cause mortality. We evaluated DeepTherm on real-world data across Spain. Results demonstrate consistent, robust, and accurate performance across diverse regions, time periods, and population groups while allowing trade-off between missed alarms and false alarms.

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