Review of Intelligent Decision-Making Technologies for Urban Drainage System

被引:0
|
作者
Yin H. [1 ]
Zhang H. [1 ]
Xu Z. [1 ]
机构
[1] College of Environmental Science and Engineering, Tongji University, Shanghai
来源
关键词
Optimal regulation; Overflow pollution; Pipe detection; Smart drainage system; Urban flooding;
D O I
10.11908/j.issn.0253-374x.20542
中图分类号
学科分类号
摘要
Smart decision-making is the key technology of smart water systems. This paper reviews the realization of intelligent decision-making of urban drainage systems from pipe network diagnosis and evaluation, urban waterlogging prevention and control, and overflow pollution control in rainy days. For drainage pipe detection and assessment, the technical methods for source flow tracking based on water flow and chemical markers monitoring at divided sub-catchments were evaluated. For urban flooding control, the techniques of elaborate simulation of flooding risk area using numerical model, and the real-time forecast of precipitation and local flooding depth using machine-learning method were reviewed; for the drainage overflow pollution control, the optimal control of urban drainage system based on the integration of multi-objective algorithm, numerical model, and machine-learning were discussed. It is proposed that the reliability of the modeling system is the key for smart decision-making in urban drainage systems. Therefore, attention should be paid to the integration of quantitative analysis of water sources in the pipe network and waterlogging risk early warning and optimal operation scheduling of drainage systems. © 2021, Editorial Department of Journal of Tongji University. All right reserved.
引用
收藏
页码:1426 / 1434
页数:8
相关论文
共 44 条
  • [1] XU Zuxin, Planning theory and practice of river pollution control, (2003)
  • [2] LIAO Zhenliang, ZHI Guozheng, ZHOU Yiwen, Et al., To analyze the urban water pollution discharge system using the tracking and tracing approach, Environmental Earth Sciences, 75, (2016)
  • [3] ZHU Xiaoqing, YIN Junxian, ZHANG Lili, Et al., Study on smart water application system in Shenzhen, Water Resources and Hydropower Engineering, 50, S2, (2019)
  • [4] WANG Chunhua, YANG Chao, FANG Shiming, Et al., Internet-based smart drainage management and application performance, China Water & Wastewater, 32, 12, (2016)
  • [5] LIN Haobin, TENG Liangfang, XI Weihong, Et al., Design and application of smart urban drainage operation and management system in Ningbo, China Water & Wastewater, 32, 15, (2016)
  • [6] ZHANG Guozhan, LI Gang, ZHANG Pan, Discussion on the construction of wisdom drainage system in Baoding City based on internet of things, Water & Wastewater Engineering, 55, 8, (2019)
  • [7] KERKEZ B, GRUDEN C, LEWIS M, Et al., Smarter stormwater systems, Environmental Science & Technology, 50, (2016)
  • [8] NOLAN J R., Computer systems that learn: an empirical study of the effect of noise on the performance of three classification methods, Expert Systems with Applications, 23, 1, (2002)
  • [9] MOSELHI O, SHEHAB-ELDEEN T., Classification of defects in sewer pipes using neural networks, Journal of Infrastructure Systems, 6, 3, (2000)
  • [10] ZHANG C L, JIANG J, KAMEL M., Intrusion detection using hierarchical neural networks, Pattern Recognition Letters, 26, 6, (2005)