Preprints
https://doi.org/10.5194/hess-2021-20
https://doi.org/10.5194/hess-2021-20

  12 Feb 2021

12 Feb 2021

Review status: this preprint is currently under review for the journal HESS.

Deep learning for the estimation of water-levels using river cameras

Remy Vandaele1,3, Sarah L. Dance1,2, and Varun Ojha3 Remy Vandaele et al.
  • 1Department of Meteorology, University of Reading, U.K
  • 2Department of Mathematics, University of Reading, U.K
  • 3Department of Computer Sciences, University of Reading, U.K

Abstract. River level estimation is a critical task required for the understanding of flood events, and is often complicated by the scarcity of available data. Recent studies have proposed to take advantage of large networks of river camera images to estimate the river levels, but currently, the utility of this approach remains limited as it requires a large amount of manual intervention (ground topographic surveys and water image annotation). We develop an approach using an automated water semantic segmentation method to ease the process of river level estimation from river camera images. Our method is based on the application of a transfer learning methodology to deep semantic neural networks designed for water segmentation. Using datasets of image series extracted from four river cameras and manually annotated for the observation of a flood event on the Severn and Avon rivers, UK (21 November–5 December 2012), we show that our algorithm is able to automate the annotation process with an accuracy greater than 91 %. Then, we apply our approach to year-long image series from the same cameras observing the Severn and Avon (from 1 June 2019 to 31 May 2020) and compare our results with nearby river-gauge measurements. Given the high correlation (Pearson's Correlation Coefficient > 0.94) between our results and the river-gauge measurements, it is clear that our approach to automation of the water segmentation on river camera images could allow for straightforward, inexpensive observation of flood events, especially at ungauged locations.

Remy Vandaele et al.

Status: open (until 09 Apr 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-20', Kenneth Chapman, 19 Feb 2021 reply
  • RC2: 'Comment on hess-2021-20', Anonymous Referee #2, 01 Mar 2021 reply

Remy Vandaele et al.

Data sets

Deep learning for the estimation of water-levels using river cameras: networks and datasets Remy Vandaele, Sarah L. Dance, and Varun Ojha https://doi.org/10.17864/1947.282

Model code and software

Deep learning for the estimation of water-levels using river cameras: networks and datasets Remy Vandaele, Sarah L. Dance, and Varun Ojha https://doi.org/10.17864/1947.282

Remy Vandaele et al.

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Short summary
The acquisition of river level data is a critical task for the understanding of flood events, but is often complicated by the difficulty to install and maintain gauges able to provide such information. This study proposes to apply deep learning techniques on river camera images in order to automatically extract the corresponding water levels. This approach could allow for a new flexible way to observe flood events, especially at ungauged locations.