Calibrated river-level estimation from river cameras using convolutional neural networks

Abstract

Monitoring river water levels is essential for the study of floods and mitigating their risks. River gauges are a well-established method for river water-level monitoring but many flood-prone areas are ungauged and must be studied through gauges located several kilometers away. Taking advantage of river cameras to observe river water levels is an accessible and flexible solution but it requires automation. However, current automated methods are only able to extract uncalibrated river water-level indexes from the images, meaning that these indexes are relative to the field of view of the camera, which limits their application. With this work, we propose a new approach to automatically estimate calibrated river water-level indexes from images of rivers. This approach is based on the creation of a new dataset of 32,715 images coming from 95 river cameras in the UK and Ireland, cross-referenced with gauge data (river water-level information), which allowed us to train convolutional neural networks. These networks are able to accurately produce two types of calibrated river water-level indexes from images: one for continuous river water-level monitoring, and the other for flood event detection. This work is an important step toward the automated use of cameras for flood monitoring.

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“…Aided by deep learning models, interpreting river water levels in the continuous domain from images can already be realized (Vandaele et al, 2023). Deep learning models can transform water level interpretation into regression tasks by directly establishing the mapping relationship between images and water level values.…”

Estimating flood discharge at river bridges using the entropy theory. Insights from Computational Fluid Dynamics flow fields

“…While monitoring river discharge on the ground has definite advantages (Fekete et al, 2012), the use of traditional methods (e.g., current meters, ADCPs) is not straightforward in case of high-flow conditions. Alternative methods have been proposed to estimate the velocity distributions and the flow discharge through indirect approaches (Bogning et al, 2018;Fekete and Vörösmarty, 2002;Spada et al, 2017;Vandaele et al, 2023;Zhang et al, 2019). These methods typically make https://doi.org/10.5194/hess-2023-253 Preprint.…”