Calibration of stormwater management model using flood extent data

Kim, Byunhyun; Sanders, Brett F.; Han, Kun‐Yeun; Kim, Youngjoo; Famiglietti, J. S.

doi:10.1680/wama.12.00051

Cited by 19 publications

(13 citation statements)

References 31 publications

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“…With a more camera-specific solution, Sakaino (2016) estimates water levels with a supervised histogram-based approach which assumes a straight water line on a wall visible in the footage. Similarly, Kim et al (2011) used a ruler in the camera's field of view as a reference for the water level measurement. A similar approach is used by Bhola et al (2018), who used the size of large objects like bridges to estimate the real height of automatically detected water surface in the image.…”

Section: Automatic Water Level Monitoring With Surveillance Imagesmentioning

confidence: 99%

Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network

Vitry

Kramer

Wegner

et al. 2019

Hydrol. Earth Syst. Sci.

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Abstract. In many countries, urban flooding due to local, intense rainfall is expected to become more frequent because of climate change and urbanization. Cities trying to adapt to this growing risk are challenged by a chronic lack of surface flooding data that are needed for flood risk assessment and planning. In this work, we propose a new approach that exploits existing surveillance camera systems to provide qualitative flood level trend information at scale. The approach uses a deep convolutional neural network (DCNN) to detect floodwater in surveillance footage and a novel qualitative flood index (namely, the static observer flooding index – SOFI) as a proxy for water level fluctuations visible from a surveillance camera's viewpoint. To demonstrate the approach, we trained the DCNN on 1218 flooding images collected from the Internet and applied it to six surveillance videos representing different flooding and lighting conditions. The SOFI signal obtained from the videos had a 75 % correlation to the actual water level fluctuation on average. By retraining the DCNN with a few frames from a given video, the correlation is increased to 85 % on average. The results confirm that the approach is versatile, with the potential to be applied to a variety of surveillance camera models and flooding situations without the need for on-site camera calibration. Thanks to this flexibility, this approach could be a cheap and highly scalable alternative to conventional sensing methods.

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Section: Automatic Water Level Monitoring With Surveillance Imagesmentioning

confidence: 99%

Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network

Vitry

Kramer

Wegner

et al. 2019

Hydrol. Earth Syst. Sci.

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show abstract

“…In addition, Kim et al [36] highlighted Manning resistance parameter for sewer pipes is the greatest source of uncertainty relative to surcharge prediction and thus flood extent prediction. Thus, this study calibrated the Manning roughness parameter of conduit, which was one of the most sensitive parameters that affected the concentration time in the basin and overflow in the manholes.…”

Section: Swmm Calibrationmentioning

confidence: 99%

A Simple and Robust Method for Simultaneous Consideration of Overland and Underground Space in Urban Flood Modeling

Son

Kim

Han

2016

Water

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This study proposed two methods, boundary-type and pond-type, to link overland and underground space in urban flood modeling. The boundary-type treats the exit of underground space as an interface for inflow of floodwater by imposing open boundary condition and pond-type considers underground space as an underground pond by configuring pond terrain. The effect of underground space in urban flood inundation was examined by coupling one-dimensional (1D) stormwater management model (SWMM) and two-dimensional (2D) overland flood model. The models were applied to the Hyoja drainage basin, Seoul, Korea where urban flood occurred due to heavy rainfall in 21 September 2010. The conduit roughness coefficient of SWMM was calibrated to minimize the difference between observed and predicted water depth of pipe. In addition, the surface roughness coefficient of 2D model was calibrated by comparing observed and predicted flood extent. Then, urban flood analysis was performed on three different scenarios involving a case not considering underground (Case 1) and cases considering underground, boundary-type (Case 2) and pond-type (Case 3). The simulation results have shown that the boundary-type is simple but robust method with high computational efficiency to link overland and underground space in urban flood modeling.

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“…The lack of monitoring methods and ensuing data scarcity are frequently decried in the urban pluvial flood modelling community (Gaitan et al, 2016;Hunter et al, 2008;El Kadi Abderrezzak et al, 2009;Leandro et al, 2009). In this context, researchers and practitioners have turned to alternative sources of data such as surveillance footage (Liu et al, 2015;Lv et al, 2018), ultrasonic-infrared sensor combinations (Mousa et al, 2016), field surveys (Kim et al, 2014) and first-hand reports (Kim et al, 2014;Yu et al, 2016). Although quantitative information (e.g.…”

mentioning

confidence: 99%

Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network

Vitry¹,

Krämer²,

Wegner³

et al. 2019

Preprint

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Abstract. In many countries, urban flooding due to local, intense rainfall is expected to become more frequent because of climate change and urbanization. Cities trying to adapt to this growing risk are challenged by a chronic lack of surface flooding data that is needed for flood risk assessment and planning. In this work, we propose a new approach that exploits existing surveillance camera systems to provide qualitative flood level trend information at scale. The approach uses a deep convolutional neural network (DCNN) to detect floodwater in surveillance footage and a novel qualitative flood index (SOFI) as a proxy for water level fluctuations visible from a surveillance camera’s viewpoint. To demonstrate the approach, we trained the DCNN on 1281 flooding images collected from the Internet and applied it to six surveillance videos representing different flooding and lighting conditions. The SOFI signal obtained from the videos had on average a 75 % correlation to the actual water level fluctuation. By retraining the DCNN with a few frames from a given video, performance for that video is further increased to 85 %. The results suggest that the approach can be used with almost any surveillance footage without the need for on-site camera calibration, making it cheap, highly scalable, and retroactively applicable to archived surveillance footage.

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Calibration of stormwater management model using flood extent data

Cited by 19 publications

References 31 publications

Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network

Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network

A Simple and Robust Method for Simultaneous Consideration of Overland and Underground Space in Urban Flood Modeling

Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network

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