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数据和模型中的不确定性量化:一种适用于北京地铁的极端降雨事件的预测模型
Liang Mu1, Yurui Kang1, Zixu Yan1
1School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.
Accident; analysis and prevention
|September 19, 2025
概括
准确的极端降雨预测对于地铁洪水预警至关重要. 一个新的模型量化了不确定性,改善了城市洪水管理的预测.
科学领域:
- 水文和水资源水文与水资源
- 城市规划 城市规划
- 环境科学中的人工智能
背景情况:
- 极端降雨事件是地铁站洪水的主要原因.
- 现有的降雨预测模型经常忽略数据和模型不确定性,降低预测可靠性.
- 准确的降雨量预测对于有效的洪水预警系统至关重要.
研究的目的:
- 开发一种用于预测极端降雨事件的概率密度 (PD-STGCN) 的新型模型,该模型集成了极端降雨事件的数据和模型不确定性量化.
- 为加强洪水风险评估提供点预测 (PP) 和概率密度预测 (PDP).
- 提高降雨预报的可靠性和准确性,用于城市洪水管理.
主要方法:
- 实现了一个时空图卷积网络 (STGCN) 架构.
- 集成的蒙特卡洛脱落 (MC脱落) 和不确定性量化预测差异.
- 开发了一个新的损失函数,并使用高斯核密度估计 (KDE) 来计算概率密度函数 (PDF).
主要成果:
- 拟议的PD-STGCN模型在点预测和概率密度预测任务中表现优于基准模型.
- 该模型成功量化了降雨数据和预测模型中的不确定性.
- 在北京极端降雨事件的验证证实了该模型的准确性和可靠性.
结论:
- 开发的PD-STGCN模型在预测极端降雨事件方面取得了重大进展.
- 这种新的预测工具通过将高精度与强大的可靠性相结合,提高了城市洪水管理.
- 系统地整合不确定性量化导致更可靠的降雨预测,以保护关键基础设施.
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