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Deep learning for automated river-level monitoring through river camera images: an approach based on water segmentation and transfer learning

Vandaele, R., Dance, S. L. ORCID: https://orcid.org/0000-0003-1690-3338 and Ojha, V. ORCID: https://orcid.org/0000-0002-9256-1192 (2021) Deep learning for automated river-level monitoring through river camera images: an approach based on water segmentation and transfer learning. Hydrology and Earth System Sciences. ISSN 1027-5606 (In Press)

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To link to this item DOI: 10.5194/hess-2021-20

Abstract/Summary

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 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 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.

Item Type:Article
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:99257
Publisher:Copernicus

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