Deep Learning-Based Automatic River Flow Estimation Using RADARSAT Imagery.

Ziadi, Samar; Chokmani, Karem ORCID: https://orcid.org/0000-0003-0018-0761; Chaabani, Chayma ORCID: https://orcid.org/0000-0003-3891-3748 et El Alem, Anas ORCID: https://orcid.org/0000-0002-8570-2110 (2024). Deep Learning-Based Automatic River Flow Estimation Using RADARSAT Imagery. Remote Sensing , vol. 16 , nº 10. p. 1808. DOI: 10.3390/rs16101808.

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Résumé

Estimating river flow is a key parameter for effective water resource management, flood risk prevention, and hydroelectric facilities planning. Yet, traditional gauging methods are not reliable under very high flows or extreme events. Hydrometric network stations are often sparse, and their spatial distribution is not optimal. Therefore, many river sections cannot be monitored using traditional flow measurements and observations. In the last few decades, satellite sensors have been considered as complementary observation sources to traditional water level and flow measurements. This kind of approach has provided a way to maintain and expand the hydrometric observation network. Remote sensing data can be used to estimate flow from rating curves that relate instantaneous flow (Q) to channel cross-section geometry (effective width or depth of the water surface). Yet, remote sensing has limitations, notably its dependence on rating curves. Due to their empirical nature, rating curves are limited to specific river sections (reaches) and cannot be applied to other watercourses. Recently, deep-learning techniques have been successfully applied to hydrology. The primary goal of this study is to develop a deep-learning approach for estimating river flow in the Boreal Shield ecozone of Eastern Canada using RADARSAT-1 and -2 imagery and convolutional neural networks (CNN). Data from 39 hydrographic sites in this region were used in modeling. A new CNN architecture was developed to provide a straightforward estimation of the instantaneous river flow rate. Our results yielded a coefficient of determination (R2) and a Nash–Sutcliffe value of 0.91 and a root mean square error of 33 m3/s. Notably, the model performs exceptionally well for rivers wider than 40 m, reflecting its capability to adapt to varied hydrological contexts. These results underscore the potential of integrating advanced satellite imagery with deep learning to enhance hydrological monitoring across vast and remote areas.

Type de document:	Article
Mots-clés libres:	deep learning; CNN; rating curve; flow; water level; radar images
Centre:	Centre Eau Terre Environnement
Date de dépôt:	09 juill. 2024 18:47
Dernière modification:	09 juill. 2024 18:47
URI:	https://espace.inrs.ca/id/eprint/15688

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