most citedDeep Learning Methods for Daily Wildfire Danger Forecasting

3 citations · 4 across the 2 of their papers we have counts for

collaborators

5 papers

physics.ao-ph2025

On the Predictive Skill of Artificial Intelligence-based Weather Models for Extreme Events using Uncertainty Quantification

Rodrigo Almeida, Noelia Otero, Miguel-Ángel Fernández-Torres +1

Accurate prediction of extreme weather events remains a major challenge for artificial intelligence-based weather prediction systems. While deterministic models such as FuXi, Graph…

cs.LG20241 cited

Explainable Earth Surface Forecasting under Extreme Events

Oscar J. Pellicer-Valero, Miguel-Ángel Fernández-Torres, Chaonan Ji +2

With climate change-related extreme events on the rise, high dimensional Earth observation data presents a unique opportunity for forecasting and understanding impacts on ecosystem…

cs.AI20243 cited

AI for Extreme Event Modeling and Understanding: Methodologies and Challenges

Gustau Camps-Valls, Miguel-Ángel Fernández-Torres, Kai-Hendrik Cohrs +22

In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences. Here, AI improved weather forecasting, model emulation, parameter…

cs.LG20242 cited

DeepExtremeCubes: Integrating Earth system spatio-temporal data for impact assessment of climate extremes

Chaonan Ji, Tonio Fincke, Vitus Benson +12

With climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show prom…

cs.LG20213 cited

Deep Learning Methods for Daily Wildfire Danger Forecasting

Ioannis Prapas, Spyros Kondylatos, Ioannis Papoutsis +5

Wildfire forecasting is of paramount importance for disaster risk reduction and environmental sustainability. We approach daily fire danger prediction as a machine learning task, u…