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评估野火GPT:对人工智能模型进行量化野火传播预测的比较分析
Meghana Ramesh1, Ziheng Sun1, Yunyao Li2
1Center for Spatial Information Science and Systems, Department of Geography and Geoinformation Sciences, Department of Atmospheric Oceanic Earth Sciences, George Mason University, 4087 University Dr STE 3120, Fairfax, VA 22030 USA.
概括
像Wildfire这样的通用人工智能GPT在量化野火预测方面遇到了困难. 特定领域的模型,如TabNet,对于准确预测火力辐射功率 (FRP) 的传播至关重要.
科学领域:
- 环境科学 环境科学
- 人工智能的人工智能
- 遥感 遥感 遥感 遥感
背景情况:
- 野火GPT是一个通用的AI工具,经常用于野火讨论.
- 需要对野火行为进行可靠的定量预测,特别是火焰辐射功率 (FRP).
- 一般用途的人工智能可能缺乏用于准确科学预测的专业能力.
研究的目的:
- 为了评估野火GPT对火灾辐射功率 (FRP) 传播的定量预测性能.
- 将WildfireGPT的性能与基于TabNet的专门预测模型进行比较.
- 突出一般AI在复杂环境建模中的局限性.
主要方法:
- 利用现实世界的NASA火力辐射功率 (FRP) 数据集进行实验.
- 开发和训练一个基于TabNet的模型,使用诸如蒸汽压力缺陷 (VPD),温度 (T),压力 (P) 和火灾天气指数 (FWI) 等变量.
- 评估了WildfireGPT (使用RAG和LLM) 和TabNet模型对定量FRP预测的表现.
主要成果:
- 基于TabNet的模型在FRP预测中显示出高相关性与低的平均绝对误差 (MAE) 和平均平方误差 (MSE).
- 在提供与提示相同的输入数据时,WildfireGPT在定量FRP预测中表现不可靠.
- 与此任务的特定领域模型相比,通用AI工具显示出显著的缺陷.
结论:
- 像WildfireGPT这样的通用AI工具不适合进行定量野火预测任务.
- 特定领域的人工智能模型对于准确和可操作的野火管理是必要的.
- 对人工智能工具的明智使用至关重要,优先考虑关键应用程序的专业模型.
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