深度学习,几何特征和水力动力学建模用于评估下水道缺陷对城市洪水的影响:中国广州的一个案例研究
Qianqian Zhou1, Zuxiang Situ2, Wanen Feng2
1School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou, China; Cross Research Institute of Ocean Engineering Safety and Sustainable Development, Guangdong University of Technology, Guangzhou, China.
Journal of environmental management
|December 6, 2023
概括
这项研究引入了用于检测和评估下水道缺陷的新框架,显示了它们如何显著增加城市洪水超过16%的情况. 该系统准确地识别出缺陷类型,并量化它们对洪水量的影响.
科学领域:
- 环境工程 环境工程
- 计算机科学 计算机科学
- 城市水文学 城市水文学
背景情况:
- 深度学习在使用大型数据集的自动下水道缺陷检测方面表现出色.
- 目前的方法缺乏详细的几何特征和缺陷的严重程度评估.
- 评估下水道缺陷对城市洪水的影响需要采用综合方法.
研究的目的:
- 为自动化下水道缺陷检测,细分,表征和影响评估开发一个全面的框架.
- 量化不同类型的下水道缺陷的严重程度.
- 用水力动力学建模评估下水道缺陷对城市洪水状况的影响.
主要方法:
- 集成DeepLabv3+用于缺陷细分.
- 开发一个自动化模块用于几何表征和严重性量化.
- 使用GIS和SWMM (风暴水管理模型) 进行水力动力学建模.
主要成果:
- 细分模型实现了高精度 (MPA 0.99,MIoU 0.74,FWIoU 0.95) 的研究结果.
- 严重性预测一致性达到98%的跌落,90%的障碍,90%的断裂,和83%的泄漏缺陷.
- 在调查的降雨事件中,下水道缺陷使总洪水量 (TFV) 增加了16%以上.
结论:
- 拟议的框架准确地检测,细分和描述下水道缺陷.
- 污水管道缺陷对城市洪水产生重大影响,增加了补充通道和总洪水量.
- 该框架为下水道缺陷管理和城市洪水减缓的决策提供了有价值的工具.
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