arXiv AI July 24, 2026 By Alex O. Davies, Eunice Lo, Rui Zhu

Demographically-Informed Heat-Mortality Risk Curves via Risk Graph Neural Networks

arXiv:2607. 21131v1 Announce Type: cross Abstract: Estimating heat-related mortality risk is a core task in environmental epidemiology, typically addressed with Distributed Lag Non-linear Models (DLNMs); interpretable exposure-response surfaces fitted to temperature-mortality time series.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 10

Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting

arXiv:2509. 25017v2 Announce Type: replace Abstract: Wildfires are among the most severe natural hazards, posing a significant threat to both humans and natural ecosystems.

By Spyros Kondylatos, Nikolas Papadopoulos, Gustau Camps-Valls, Ioannis Papoutsis

arXiv Machine Learning

Jun 5

Learning to model pediatric asthma exacerbation from multiple risk factors: a case study in coastal Virginia

arXiv:2606. 06174v1 Announce Type: new Abstract: Childhood asthma is a common illness exacerbated by air pollution as well as meteorological and neighborhood-level socioeconomic factors.

By Jonathan Colen, Eric Werner, Maryam Golbazi, Heather Richter, Diana McSpadden, Amy Quinn, Jocel Santos, Mary Jane Darling, Mary Margaret Gleason

safety

arXiv AI

Jun 4

Physics-Informed Machine Learning for Short-Term Flood Prediction

arXiv:2606. 04143v1 Announce Type: cross Abstract: Accurate flood forecasting is essential for mitigating disaster risks and protecting communities.

By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni

safety

arXiv Machine Learning

Sep 25

Capturing Unseen Spatial Heat Extremes Through Dependence-Aware Generative Modeling

The paper introduces DeepX-GAN, a deep generative model that captures spatial dependence in rare climate extremes. It can simulate statistically plausible unseen heat extremes—both direct-hit and near-miss events—beyond the observed record. Applied to the Middle East and North Africa, the model shows that unseen heat extremes disproportionately affect vulnerable countries and that future warming could create new persistent hotspots, underscoring the need for spatially adaptive resilience planning.

By Xinyue Liu, Xiao Peng, Shuyue Yan, Yuntian Chen, Dongxiao Zhang, Zhixiao Niu, Hui-Min Wang, Xiaogang He

rag diffusion safety

arXiv Machine Learning

Jun 30

CRPS-LAM: Probabilistic Regional Weather Forecasting with Continuous Ranked Probability Score

arXiv:2510. 09484v3 Announce Type: replace Abstract: Limited-Area Models (LAMs) enable weather forecasting over regional domains at higher resolutions than what is computationally feasible for global models.

By Erik Larsson, Joel Oskarsson, Tomas Landelius, Fredrik Lindsten

diffusion

arXiv Machine Learning

Jul 31

Weather Emulators at the Frontier of Heat Extremes Predictability

arXiv:2607. 28220v1 Announce Type: cross Abstract: Atmospheric predictability declines rapidly beyond the next ten days, such that forecasts at longer lead times primarily convey large-scale trends rather than specific states.

By Cas Decancq, Thomas Mortier, Jessica Keune, Diego G. Miralles

benchmarks

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Related stories

Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting

Learning to model pediatric asthma exacerbation from multiple risk factors: a case study in coastal Virginia

Physics-Informed Machine Learning for Short-Term Flood Prediction

Capturing Unseen Spatial Heat Extremes Through Dependence-Aware Generative Modeling

CRPS-LAM: Probabilistic Regional Weather Forecasting with Continuous Ranked Probability Score

Weather Emulators at the Frontier of Heat Extremes Predictability