基于水位传感器网络的城市下水道系统的雨水流入和透的空间异质性识别:来自可解释深度学习方法的见解
Yue Zheng1, Xinyu Chen2, Qing Zhang3
1The Institute of Municipal Engineering, Zhejiang University, Hangzhou, China; Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, China.
Environmental research
|October 2, 2025
概括
识别严重降雨流入和透 (RDII) 的分捕获区对于城市下水道管理至关重要. 这项研究使用水位传感器和可解释的深度学习来确定RDII热点,降低成本并提高准确性.
科学领域:
- 环境工程 环境工程
- 城市水文学 城市水文学
- 数据科学数据科学数据科学
背景情况:
- 雨水流入和透 (RDII) 的空间异质性使城市下水道管理复杂化.
- 现有的RDII识别方法往往需要大量数据或无法捕捉复杂的下水道系统动态.
- 增加的传感器部署需要有效的数据挖掘用于RDII评估.
研究的目的:
- 开发一种新的方法来识别使用低成本水位传感器的严重RDII的分类捕捞.
- 利用可解释的深度学习算法来分析下水道系统数据.
- 在城市下水道网络中解决RDII的空间异质性挑战.
主要方法:
- 根据传感器位置将下水道系统划分为子捕获区.
- 开发深度学习预测模型,使用干燥和潮湿天气条件的水位数据.
- 应用可解释的人工智能 (XAI) 来分析深度学习模型并确定RDII严重程度.
主要成果:
- 深度学习模型有效地捕捉了监测站点和降雨量之间的响应关系.
- 水平传感器和可解释的深度学习的组合准确地识别了各个子捕捞区的不同程度的RDII.
- 拟议的方法在案例研究中证明了可行性,验证了其有效性.
结论:
- 低成本的水位传感器与可解释的深度学习相结合,为空间RDII识别提供了一种可行的方法.
- 与传统方法相比,这种方法可以降低监测和建模成本.
- 该技术在城市下水道管理和监测中具有广泛应用的巨大潜力.
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