相关实验视频
Updated: Jun 12, 2025

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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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关于既定方法与新兴的人工智能和机器学习方法的兼容性,用于灾害风险分析
Shital Thekdi1, Unal Tatar2, Joost Santos3
1Robins School of Business, University of Richmond, Richmond, Virginia, USA.
Risk analysis : an official publication of the Society for Risk Analysis
|September 20, 2024
概括
像人工智能 (AI) 和机器学习 (ML) 这样的先进分析正在改变灾害风险分析 (RA). 本研究将已建立的RA方法与AI/ML方法进行比较,评估它们对于未来灾害风险评估的兼容性.
科学领域:
- 灾害风险分析 灾害风险分析
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 越来越多的人对AI/ML对灾害风险分析 (RA) 的兴趣.
- 需要将新兴的AI/ML方法与已建立的RA实践进行比较.
- 已建立的RA方法被广泛接受,并在各种应用中使用.
研究的目的:
- 研究已建立的RA方法与AI/ML进步的兼容性.
- 在灾难RA中评估AI/ML方法,考虑有用性,信任性和可解释性.
- 为将AI/ML整合到灾难RA实践中提供见解.
主要方法:
- 从RA和AI专业人士那里获得了专家的意见.
- 研究了既有RA方法与AI/ML技术之间的兼容性.
- 从专家的角度利用经验证据.
主要成果:
- 提供了对已建立和基于AI/ML的灾难RA方法的关键见解.
- 经验证据支持这些方法的兼容性评估.
- 突出了AI/ML集成中的有用性,信任和可解释性等因素.
结论:
- 人工智能/ML方法为灾难RA提供了新的能力.
- 人工智能/ML与既有方法的兼容性对于进步至关重要.
- 专家的观点对于指导AI/ML在灾难RA中的未来至关重要.
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