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

TL;DR

This paper introduces DeepTherm, a modular deep learning system that predicts deadly heatwaves in urban areas without needing historical heat-related mortality data, demonstrating robustness and accuracy across diverse regions.

Contribution

DeepTherm is a novel, flexible deep learning-based early warning system that predicts deadly heatwaves without relying on heat-related mortality history.

Findings

Consistent and accurate performance across regions and time periods

Robust prediction with adjustable trade-offs between false alarms and missed alarms

Effective in diverse population groups

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.