English(EN) A Graph Neural Network for Global Daily Fire Radiative Power Prediction at Medium-Range Lead Times

图神经网络提前数天预测全球火灾活动

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员开发了一种图神经网络模型，能够提前数天预测全球火灾辐射功率（FRP）。该模型旨在通过解决当前业务系统存在的局限性来改进空气质量和气溶胶预报，这些系统使用过时的火灾观测数据并假设火灾活动没有演变。新模型在 2020-2022 年的数据上进行训练，并在 2023-2024 年进行评估，与持续性模型相比显示出显著改进，在七天提前量时均方误差最多可降低 43%。虽然它能够可靠地检测到大火，但在准确校准其辐射功率和定位小火方面仍存在挑战。 AI

影响通过改进火灾活动预测，增强了空气质量和气溶胶预报的预测能力。

排序理由详细介绍大气物理预测新模型的学术论文。[lever_c_research降级：ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Li Zhang, Jun Wang, Isidora Jankov, Yongxin Liu, Gonzalo A. Ferrada, Ravan Ahmadov, Ligia Bernardet, Haonan Chen, Shobha Kondragunta · 2026-10-09 04:00

用于中程提前量全球每日火辐射功率预测的图神经网络

arXiv:2610.11022v1 Announce Type: cross Abstract: Skillful prediction of biomass-burning activity several days in advance is important for air-quality forecasting and aerosol prediction. Two operational constraints motivate this work. First, the GBBEPx satellite fire radiative po…

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用于中程提前量全球每日火辐射功率预测的图神经网络

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