Flood forecasting within urban drainage systems using NARX neural network

被引:42
|
作者
Abou Rjeily, Yves [1 ,2 ]
Abbas, Oras [1 ]
Sadek, Marwan [1 ,2 ]
Shahrour, Isam [1 ]
Chehade, Fadi Hage [2 ]
机构
[1] Lille Univ Sci & Technol, Lab Genie Civil & Geoenvironm, Villeneuve Dascq, France
[2] Lebanese Univ, Modeling Ctr, Beirut, Lebanon
关键词
case study; flooding forecast; NARX neural network; proactivity; urban drainage systems; REAL-TIME CONTROL; IMPACTS;
D O I
10.2166/wst.2017.409
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Urbanization activity and climate change increase the runoff volumes, and consequently the surcharge of the urban drainage systems (UDS). In addition, age and structural failures of these utilities limit their capacities, and thus generate hydraulic operation shortages, leading to flooding events. The large increase in floods within urban areas requires rapid actions from the UDS operators. The proactivity in taking the appropriate actions is a key element in applying efficient management and flood mitigation. Therefore, this work focuses on developing a flooding forecast system (FFS), able to alert in advance the UDS managers for possible flooding. For a forecasted storm event, a quick estimation of the water depth variation within critical manholes allows a reliable evaluation of the flood risk. The Nonlinear Auto Regressive with eXogenous inputs (NARX) neural network was chosen to develop the FFS as due to its calculation nature it is capable of relating water depth variation in manholes to rainfall intensities. The campus of the University of Lille is used as an experimental site to test and evaluate the FFS proposed in this paper.
引用
收藏
页码:2401 / 2412
页数:12
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