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Lookup NU author(s): Dr Varun OjhaORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2021 Remy Vandaele et al. 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 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 have developed 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 rivers Severn and Avon, UK (21 November-5 December 2012), we show that this 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 rivers Severn and Avon (from 1 June 2019 to 31 May 2020) and compare the results with nearby river-gauge measurements. Given the high correlation (Pearson's correlation coefficient >0.94) between these 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.
Author(s): Vandaele R, Dance SL, Ojha V
Publication type: Article
Publication status: Published
Journal: Hydrology and Earth System Sciences
Year: 2021
Volume: 25
Issue: 8
Pages: 4435-4453
Online publication date: 16/08/2021
Acceptance date: 10/07/2021
Date deposited: 09/03/2023
ISSN (print): 1027-5606
ISSN (electronic): 1607-7938
Publisher: Copernicus GmbH
URL: https://doi.org/10.5194/hess-25-4435-2021
DOI: 10.5194/hess-25-4435-2021
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