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全球预测未开采水域的极端洪水
Grey Nearing1, Deborah Cohen2, Vusumuzi Dube2
1Google, . nearing@google.com.
Nature
|March 21, 2024
概括
人工智能现在可靠地预测未经挖掘的河流流域的极端洪水事件, 这种人工智能洪水预测为重大事件提供了更高的准确性, 提升了全球的预警系统.
科学领域:
- 水文与水资源
- 环境科学中的人工智能
- 减少自然灾害风险
背景情况:
- 洪水是常见的自然灾害,
- 有效的洪水风险减缓依赖于准确及时的洪水警告.
- 传统的水文模型需要大量的校准数据,这限制了它们在未挖掘的盆地中的使用.
研究的目的:
- 开发和评估基于人工智能 (AI) 的极端河流洪水预测系统.
- 与现有的最先进系统相比,评估人工智能预测的性能.
- 提高洪水预警的时间和准确性.
主要方法:
- 开发了一个基于人工智能的水文预测模型.
- 评估模型在预测未开采的流域极端事件中的可靠性.
- 人工智能预测的准确性和交付时间与科珀尼克斯紧急管理服务全球洪水意识系统 (GFAS) 的比较.
主要成果:
- 基于人工智能的预测可靠性与GFAS目前的预测相美.
- 与当前的一年准确度相比,人工智能模型对五年回报期事件的准确性相似或更好.
- 人工智能模型被整合到一个可操作的预警系统中,在80多个国家提供实时,开放的预测.
结论:
- 人工智能为缺乏数据的地区提供可靠的长期洪水预警提供了强大的工具.
- 人工智能可以更早,更准确地预警大规模,更具影响力的洪水.
- 提高水文数据的可用性对于进一步加强全球洪水预警系统至关重要.
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