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整合学习,以提高关键基础设施对城市洪水的抵御力
Yogesh Bhattarai1, Vijay Chaudhary2, Curtis Walker3
1Department of Civil and Environmental Engineering, Howard University, Washington, DC, 20060, USA. yogesh.bhattarai@bison.howard.edu.
Scientific reports
|October 22, 2025
概括
这项研究提高了城市道路洪水预测使用集体机器学习群众来源的数据. 堆叠的超集模型显著提高了准确性,识别了面临高洪水风险的关键基础设施.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 计算机科学 计算机科学
背景情况:
- 极端降雨和洪水破坏城市系统,影响关键服务和流动性.
- 精确的道路网络洪水预测受到复杂的动态,粗的模型和数据限制的阻碍.
研究的目的:
- 通过整体机器学习模型增强道路网络洪水预测.
- 利用众筹的洪水数据集来提高预测准确度.
主要方法:
- 集体机器学习,特别是堆叠的超集体学习.
- 在华盛顿特区地区的众筹洪水数据上训练模型.
- 利用沙普利的增量解释来确定关键的预测因素.
主要成果:
- 堆叠的超集学习优于其他算法,达到0.84准确度,0.82精度和0.82F1分数.
- 人们认为,海拔是影响洪水预测准确性的重要因素.
- 该模型确定了5%的道路具有非常高的洪水概率和11%的道路具有高洪水概率,在高危地区有超过40%的能源和紧急服务.
结论:
- 集体机器学习,特别是堆叠,为城市道路洪水预测提供了强大的方法.
- 准确的洪水预测对于城市的抗灾能力和有效的应急管理至关重要.
- 识别高风险地区有助于保护关键基础设施免受极端天气事件的影响.
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