Articles | Volume 23, issue 11
Hydrol. Earth Syst. Sci., 23, 4621–4634, 2019
https://doi.org/10.5194/hess-23-4621-2019
Hydrol. Earth Syst. Sci., 23, 4621–4634, 2019
https://doi.org/10.5194/hess-23-4621-2019

Research article 15 Nov 2019

Research article | 15 Nov 2019

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

Matthew Moy de Vitry et al.

Data sets

Data for: Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network M. Moy de Vitry https://doi.org/10.25678/000150

Model code and software

Data for: Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network M. Moy de Vitry https://doi.org/10.25678/000150

Video supplement

Water level trend monitoring with a surveillance camera using a deep convolutional neural network M. Moy de Vitry and S. Kramer https://doi.org/10.5446/43637

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Short summary
This work demonstrates a new approach to obtain flood level trend information from surveillance footage with minimal prior information. A neural network trained to detect flood water is applied to video frames to create a qualitative flooding metric (namely, SOFI). The correlation between the real water trend and SOFI was found to be 75 % on average (based on six videos of flooding under various circumstances). SOFI could be used for flood model calibration, to increase model reliability.