March 14, 2024, 4:41 a.m. | Shan Zhao, Ioannis Prapas, Ilektra Karasante, Zhitong Xiong, Ioannis Papoutsis, Gustau Camps-Valls, Xiao Xiang Zhu

cs.LG updates on

arXiv:2403.08414v1 Announce Type: new
Abstract: Wildfire forecasting is notoriously hard due to the complex interplay of different factors such as weather conditions, vegetation types and human activities. Deep learning models show promise in dealing with this complexity by learning directly from data. However, to inform critical decision making, we argue that we need models that are right for the right reasons; that is, the implicit rules learned should be grounded by the underlying processes driving wildfires. In that direction, we …

abstract arxiv causal complexity cs.lg danger data decision decision making deep learning forecasting graph graph neural networks however human making networks neural networks prediction show type types weather wildfire

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