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Watershed Planning within a Quantitative Scenario Analysis Framework
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通过整合水力动力学模型和机器学习技术,加强城市地区的洪水风险评估
Alireza Khoshkonesh1, Rouzbeh Nazari1, Mohammad Reza Nikoo2
1Department of Civil Engineering, The University of Memphis, TN 38125, USA.
The Science of the total environment
|August 30, 2024
概括
这项研究通过将水力动力学模型与机器学习 (ML) 集成来增强城市洪水预测. 先进的额外树木-主要组件分析 (ET-PCA) 模型实现了近乎完美的准确性,从而实现了精确的洪水风险区分.
科学领域:
- 环境科学 环境科学
- 水文学的水文学
- 城市规划 城市规划
背景情况:
- 由于气候变化和基础设施发展,城市洪水风险正在增加.
- 创新的评估方法对于有效的城市洪水管理至关重要.
- 现有的方法需要改进,以准确预测和危险分析.
研究的目的:
- 将先进的水力动力学模型与机器学习 (ML) 集成,以改进城市洪水预测.
- 加强城市洪水危险分析和风险分区.
- 制定一个框架,提高全球城市抗洪能力.
主要方法:
- 整合1D和2D水力动力学模型,以精确的参数进行校准.
- 使用高分辨率的Lidar数据和复杂的建模模拟洪水场景.
- 应用先进的ML模型,包括额外树-主要组件分析 (ET-PCA).
- 纳入社会经济数据来绘制脆弱区域的地图.
主要成果:
- 2D水力动力学模型在预测洪水动态方面表现出卓越的准确性.
- ET-PCA ML模型实现了近乎完美的预测可靠性 (R2 = 0.999).
- 精确的洪水风险区分是ML模型所能实现的.
- 社会经济数据突出了脆弱的城市地区,为有针对性的缓解策略提供了信息.
结论:
- 将水力动力学模拟与ML相结合,可以显著提高城市洪水预测的准确性.
- 开发的框架为城市洪水风险评估和管理提供了可靠的工具.
- 这种方法支持城市规划者和政策制定者制定有效的洪水减缓和基础设施弹性战略.
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