Articles | Volume 25, issue 8
https://doi.org/10.5194/hess-25-4435-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-4435-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning
Remy Vandaele
CORRESPONDING AUTHOR
Department of Meteorology, Meteorology Building, University of Reading, Reading RG6 6ET, UK
Department of Computer Sciences, Polly Vacher Building, Whiteknights, University of Reading, Reading RG6 6DH, UK
Sarah L. Dance
Department of Meteorology, Meteorology Building, University of Reading, Reading RG6 6ET, UK
Department of Mathematics and Statistics, Mathematics Building, Whiteknights, University of Reading, Reading RG6 6AX, UK
Varun Ojha
Department of Computer Sciences, Polly Vacher Building, Whiteknights, University of Reading, Reading RG6 6DH, UK
Related authors
No articles found.
Helen Hooker, Sarah Dance, David Mason, John Bevington, and Kay Shelton
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-178, https://doi.org/10.5194/hess-2024-178, 2024
Revised manuscript under review for HESS
Short summary
Short summary
This study introduces a method that uses satellite data to enhance flood map selection for forecast-based financing applications. Tested on the 2022 Pakistan floods, it successfully triggered flood maps in four out of five regions, including those with urban areas. The approach ensures timely humanitarian aid by updating flood maps, even when initial triggers are missed, aiding in better disaster preparedness and risk management.
Helen Hooker, Sarah L. Dance, David C. Mason, John Bevington, and Kay Shelton
Nat. Hazards Earth Syst. Sci., 23, 2769–2785, https://doi.org/10.5194/nhess-23-2769-2023, https://doi.org/10.5194/nhess-23-2769-2023, 2023
Short summary
Short summary
Ensemble forecasts of flood inundation produce maps indicating the probability of flooding. A new approach is presented to evaluate the spatial performance of an ensemble flood map forecast by comparison against remotely observed flooding extents. This is important for understanding forecast uncertainties and improving flood forecasting systems.
Gwyneth Matthews, Christopher Barnard, Hannah Cloke, Sarah L. Dance, Toni Jurlina, Cinzia Mazzetti, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 26, 2939–2968, https://doi.org/10.5194/hess-26-2939-2022, https://doi.org/10.5194/hess-26-2939-2022, 2022
Short summary
Short summary
The European Flood Awareness System creates flood forecasts for up to 15 d in the future for the whole of Europe which are made available to local authorities. These forecasts can be erroneous because the weather forecasts include errors or because the hydrological model used does not represent the flow in the rivers correctly. We found that, by using recent observations and a model trained with past observations and forecasts, the real-time forecast can be corrected, thus becoming more useful.
Elizabeth S. Cooper, Sarah L. Dance, Javier García-Pintado, Nancy K. Nichols, and Polly J. Smith
Hydrol. Earth Syst. Sci., 23, 2541–2559, https://doi.org/10.5194/hess-23-2541-2019, https://doi.org/10.5194/hess-23-2541-2019, 2019
Short summary
Short summary
Flooding from rivers is a huge and costly problem worldwide. Computer simulations can help to warn people if and when they are likely to be affected by river floodwater, but such predictions are not always accurate or reliable. Information about flood extent from satellites can help to keep these forecasts on track. Here we investigate different ways of using information from satellite images and look at the effect on computer predictions. This will help to develop flood warning systems.
Joanne A. Waller, Javier García-Pintado, David C. Mason, Sarah L. Dance, and Nancy K. Nichols
Hydrol. Earth Syst. Sci., 22, 3983–3992, https://doi.org/10.5194/hess-22-3983-2018, https://doi.org/10.5194/hess-22-3983-2018, 2018
Related subject area
Subject: Rivers and Lakes | Techniques and Approaches: Stochastic approaches
Warming of the Willamette River, 1850–present: the effects of climate change and river system alterations
Assimilation of transformed water surface elevation to improve river discharge estimation in a continental-scale river
Do small and large floods have the same drivers of change? A regional attribution analysis in Europe
Flood trends in Europe: are changes in small and big floods different?
A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers
Discharge hydrograph estimation at upstream-ungauged sections by coupling a Bayesian methodology and a 2-D GPU shallow water model
Large-scale hydrological model river storage and discharge correction using a satellite altimetry-based discharge product
Influence of solar forcing, climate variability and modes of low-frequency atmospheric variability on summer floods in Switzerland
Historical impact of water infrastructure on water levels of the Mekong River and the Tonle Sap system
Stochastic modeling of Lake Van water level time series with jumps and multiple trends
Predictability of Western Himalayan river flow: melt seasonal inflow into Bhakra Reservoir in northern India
The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter
Stefan A. Talke, David A. Jay, and Heida L. Diefenderfer
Hydrol. Earth Syst. Sci., 27, 2807–2826, https://doi.org/10.5194/hess-27-2807-2023, https://doi.org/10.5194/hess-27-2807-2023, 2023
Short summary
Short summary
Archival measurements and a statistical model show that average water temperature in a major US West Coast river has increased by 1.8 °C since 1850, at a rate of 1.1 °C per century. The largest factor driving modeled changes are warming air temperatures (nearly 75 %). The remainder is primarily caused by depth increases and other modifications to the river system. Near-freezing conditions, common historically, no longer occur, and the number of warm water days has significantly increased.
Menaka Revel, Xudong Zhou, Dai Yamazaki, and Shinjiro Kanae
Hydrol. Earth Syst. Sci., 27, 647–671, https://doi.org/10.5194/hess-27-647-2023, https://doi.org/10.5194/hess-27-647-2023, 2023
Short summary
Short summary
The capacity to discern surface water improved as satellites became more available. Because remote sensing data is discontinuous, integrating models with satellite observations will improve knowledge of water resources. However, given the current limitations (e.g., parameter errors) of water resource modeling, merging satellite data with simulations is problematic. Integrating observations and models with the unique approaches given here can lead to a better estimation of surface water dynamics.
Miriam Bertola, Alberto Viglione, Sergiy Vorogushyn, David Lun, Bruno Merz, and Günter Blöschl
Hydrol. Earth Syst. Sci., 25, 1347–1364, https://doi.org/10.5194/hess-25-1347-2021, https://doi.org/10.5194/hess-25-1347-2021, 2021
Short summary
Short summary
We estimate the contribution of extreme precipitation, antecedent soil moisture and snowmelt to changes in small and large floods across Europe.
In northwestern and eastern Europe, changes in small and large floods are driven mainly by one single driver (i.e. extreme precipitation and snowmelt, respectively). In southern Europe both antecedent soil moisture and extreme precipitation significantly contribute to flood changes, and their relative importance depends on flood magnitude.
Miriam Bertola, Alberto Viglione, David Lun, Julia Hall, and Günter Blöschl
Hydrol. Earth Syst. Sci., 24, 1805–1822, https://doi.org/10.5194/hess-24-1805-2020, https://doi.org/10.5194/hess-24-1805-2020, 2020
Short summary
Short summary
We investigate changes that occurred in small vs. big flood events and in small vs. large catchments across Europe over 5 decades. Annual maximum discharge series between 1960 and 2010 from 2370 gauges in Europe are analysed. Distinctive patterns of flood regime change are identified for large regions across Europe, which depend on flood magnitude and catchment size.
Theano Iliopoulou, Cristina Aguilar, Berit Arheimer, María Bermúdez, Nejc Bezak, Andrea Ficchì, Demetris Koutsoyiannis, Juraj Parajka, María José Polo, Guillaume Thirel, and Alberto Montanari
Hydrol. Earth Syst. Sci., 23, 73–91, https://doi.org/10.5194/hess-23-73-2019, https://doi.org/10.5194/hess-23-73-2019, 2019
Short summary
Short summary
We investigate the seasonal memory properties of a large sample of European rivers in terms of high and low flows. We compute seasonal correlations between peak and low flows and average flows in the previous seasons and explore the links with various physiographic and hydro-climatic catchment descriptors. Our findings suggest that there is a traceable physical basis for river memory which in turn can be employed to reduce uncertainty and improve probabilistic predictions of floods and droughts.
Alessia Ferrari, Marco D'Oria, Renato Vacondio, Alessandro Dal Palù, Paolo Mignosa, and Maria Giovanna Tanda
Hydrol. Earth Syst. Sci., 22, 5299–5316, https://doi.org/10.5194/hess-22-5299-2018, https://doi.org/10.5194/hess-22-5299-2018, 2018
Short summary
Short summary
The knowledge of discharge hydrographs is useful for flood modelling purposes, water resource management, and the design of hydraulic structures. This paper presents a novel methodology to estimate the unknown discharge hydrograph in an ungauged river section using only water level information recorded downstream. A Bayesian procedure is coupled with a 2-D hydraulic model parallelized for GPUs. Finally, the proposed procedure has been applied to estimate inflow hydrographs in real river reaches.
Charlotte Marie Emery, Adrien Paris, Sylvain Biancamaria, Aaron Boone, Stéphane Calmant, Pierre-André Garambois, and Joecila Santos da Silva
Hydrol. Earth Syst. Sci., 22, 2135–2162, https://doi.org/10.5194/hess-22-2135-2018, https://doi.org/10.5194/hess-22-2135-2018, 2018
Short summary
Short summary
This study uses remotely sensed river discharge data to correct river storage and discharge in a large-scale hydrological model. The method is based on an ensemble Kalman filter and also introduces an additional technique that allows for better constraint of the correction (called localization). The approach is applied over the entire Amazon basin. Results show that the method is able to improve river discharge and localization to produce better results along main tributaries.
J. C. Peña, L. Schulte, A. Badoux, M. Barriendos, and A. Barrera-Escoda
Hydrol. Earth Syst. Sci., 19, 3807–3827, https://doi.org/10.5194/hess-19-3807-2015, https://doi.org/10.5194/hess-19-3807-2015, 2015
Short summary
Short summary
The paper presents an index of summer flood damage in Switzerland from 1800 to 2009 and explores the influence of solar forcing, climate variability and low-frequency atmospheric circulation on flood frequencies. The flood damage index provides evidence that the 1817-1851, 1881-1927, 1977-1990 and 2005-present flood clusters are mostly in phase with palaeoclimate proxies and solar activity minima. Floods are influenced by atmospheric instability related to the principal summer mode.
T. A. Cochrane, M. E. Arias, and T. Piman
Hydrol. Earth Syst. Sci., 18, 4529–4541, https://doi.org/10.5194/hess-18-4529-2014, https://doi.org/10.5194/hess-18-4529-2014, 2014
Short summary
Short summary
Natural patterns of water levels in the Mekong are changing as a result of hydropower and irrigation development. Since 1991, significant changes in water level fluctuations and rising and falling rates have occurred along the lower Mekong. The changes were linked to temporal and spatial trends in water infrastructure development and can lead to impacts on ecosystem productivity. Climatic change is also important, but it has not been the main cause of the key water level alternations.
H. Aksoy, N. E. Unal, E. Eris, and M. I. Yuce
Hydrol. Earth Syst. Sci., 17, 2297–2303, https://doi.org/10.5194/hess-17-2297-2013, https://doi.org/10.5194/hess-17-2297-2013, 2013
I. Pal, U. Lall, A. W. Robertson, M. A. Cane, and R. Bansal
Hydrol. Earth Syst. Sci., 17, 2131–2146, https://doi.org/10.5194/hess-17-2131-2013, https://doi.org/10.5194/hess-17-2131-2013, 2013
D. A. Plaza, R. De Keyser, G. J. M. De Lannoy, L. Giustarini, P. Matgen, and V. R. N. Pauwels
Hydrol. Earth Syst. Sci., 16, 375–390, https://doi.org/10.5194/hess-16-375-2012, https://doi.org/10.5194/hess-16-375-2012, 2012
Cited articles
Bargoti, S. and Underwood, J. P.: Image segmentation for fruit detection and
yield estimation in apple orchards, J. Field Robot., 34,
1039–1060, https://doi.org/10.1002/rob.21699, 2017. a
Baruch, A.: An investigation into the role of crowdsourcing in generating
information for flood risk management, PhD thesis, Loughborough University, Loughborough,
2018. a
Caesar, H., Uijlings, J., and Ferrari, V.: Coco-stuff: Thing and stuff classes
in context, in: Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), 1209–1218, https://doi.org/10.1109/CVPR.2018.00132,
2018. a, b
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L.:
Deeplab: Semantic image segmentation with deep convolutional nets, atrous
convolution, and fully connected CRFs, IEEE T. Pattern Anal., 40, 834–848,
https://doi.org/10.1109/TPAMI.2017.2699184, 2017. a, b, c
Civil Aviation Authority: Unmanned aircraft and drones, available at:
https://www.caa.co.uk/Consumers/Unmanned-aircraft-and-drones/, last
access: 16 November 2020. a
Cooper, E. S., Dance, S. L., García-Pintado, J., Nichols, N. K., and Smith, P. J.: Observation operators for assimilation of satellite observations in fluvial inundation forecasting, Hydrol. Earth Syst. Sci., 23, 2541–2559, https://doi.org/10.5194/hess-23-2541-2019, 2019. a
Creutin, J., Muste, M., Bradley, A., Kim, S., and Kruger, A.: River gauging
using PIV techniques: a proof of concept experiment on the Iowa River,
J. Hydrol., 277, 182–194, https://doi.org/10.1016/S0022-1694(03)00081-7,
2003. a
Di Mauro, C., Hostache, R., Matgen, P., Pelich, R., Chini, M., van Leeuwen, P. J., Nichols, N. K., and Blöschl, G.: Assimilation of probabilistic flood maps from SAR data into a coupled hydrologic–hydraulic forecasting model: a proof of concept, Hydrol. Earth Syst. Sci., 25, 4081–4097, https://doi.org/10.5194/hess-25-4081-2021, 2021. a
Eltner, A., Elias, M., Sardemann, H., and Spieler, D.: Automatic image-based
water stage measurement for long-term observations in ungauged catchments,
Water Resour. Res., 54, 10–362, https://doi.org/10.1029/2018WR023913, 2018. a, b, c
Environment Agency: LIDAR Composite DSM 2017 – 1 m, available at:
https://data.gov.uk/dataset/80c522cc-e0bf-4466-8409-57a04c456197/lidar-composite-dsm-2017-1m (last access:
26 April 2021), 2017. a
Environment Agency: Real-time and Near-real-time river level data, available at:
https://data.gov.uk/dataset/0cbf2251-6eb2-4c4e-af7c-d318da9a58be/real-time-and-near-real-time-river-level-data,
last access: 29 September 2020. a
Environment Agency: Environment Agency Real Time Flood Monitoring API, Department for Environment Food & Rural Affairs [data set], available at: https://environment.data.gov.uk/flood-monitoring/doc/reference, last access: 3 August 2021. a
Etter, S., Strobl, B., van Meerveld, I., and Seibert, J.: Quality and timing of
crowd-based water level class observations, Hydrol. Process., 34,
4365–4378, https://doi.org/10.1002/hyp.13864, 2020. a, b
Filonenko, A., Wayhono, Hernández, D. C., Seo, D., and Jo, K.-H.: Real-time
flood detection for video surveillance, in: Proceedings of the IEEE
Industrial Electronics Society Conference (IECON), 004082–004085,
https://doi.org/10.1109/IECON.2015.7392736, 2015. a, b
Finlay, J.: Autumn and winter floods 2019–20, House of Commons Library, available at:
https://commonslibrary.parliament.uk/research-briefings/cbp-8803/ (last access: 3 August 2021),
2020. a
Flack, D. L., Skinner, C. J., Hawkness-Smith, L., O'Donnell, G., Thompson, R. J., Waller, J. A., Chen, A. S., Moloney, J., Largeron, C., Xia, X., Bienkinsop, S., Champion, A. J., Perks, M. T., Quinn, N., and Speight, L. J.: Recommendations for improving integration in national end-to-end
flood forecasting systems: An overview of the FFIR (Flooding From Intense
Rainfall) programme, Water, 11, 725, https://doi.org/10.3390/w11040725, 2019. a
Freedman, D., Pisani, R., and Purves, R.: Statistics (international student edition), W.W. Norton, New York, 2007. a
García-Pintado, J., Neal, J. C., Mason, D. C., Dance, S. L., and Bates,
P. D.: Scheduling satellite-based SAR acquisition for sequential assimilation
of water level observations into flood modelling, J. Hydrol., 495,
252–266, https://doi.org/10.1016/j.jhydrol.2013.03.050, 2013. a
García-Pintado, J., Mason, D. C., Dance, S. L., Cloke, H. L., Neal, J. C.,
Freer, J., and Bates, P. D.: Satellite-supported flood forecasting in river
networks: A real case study, J. Hydrol., 523, 706–724,
https://doi.org/10.1016/j.jhydrol.2015.01.084, 2015. a, b
Gilmore, T. E., Birgand, F., and Chapman, K. W.: Source and magnitude of error
in an inexpensive image-based water level measurement system, J.
Hydrol., 496, 178–186, https://doi.org/10.1016/j.jhydrol.2013.05.011, 2013. a
Giustarini, L., Hostache, R., Kavetski, D., Chini, M., Corato, G., Schlaffer,
S., and Matgen, P.: Probabilistic flood mapping using synthetic aperture
radar data, IEEE T. Geosci. Remote, 54,
6958–6969, https://doi.org/10.1109/TGRS.2016.2592951, 2016. a
Global Runoff Data Center: Global Runoff Data Base, temporal distribution of
available discharge data, available at:
https://www.bafg.de/SharedDocs/Bilder/Bilder_GRDC/grdcStations_tornadoChart.jpg (last access:
3 August 2021), 2016. a
Goodfellow, I., Bengio, Y., and Courville, A.: Deep Learning, MIT Press, available at:
http://www.deeplearningbook.org (last access: 3 August 2021), 2016. a
Grimaldi, S., Li, Y., Pauwels, V. R., and Walker, J. P.: Remote sensing-derived
water extent and level to constrain hydraulic flood forecasting models:
Opportunities and challenges, Surv. Geophys., 37, 977–1034,
https://doi.org/10.1007/s10712-016-9378-y, 2016. a, b
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., and Chen, T.: Recent advances in convolutional neural
networks, Pattern Recognition, 77, 354–377,
https://doi.org/10.1016/j.patcog.2017.10.013, 2018. a
Guo, Y., Liu, Y., Georgiou, T., and Lew, M. S.: A review of semantic
segmentation using deep neural networks, International Journal of Multimedia
Information Retrieval, 7, 87–93, https://doi.org/10.1007/s13735-017-0141-z, 2018. a
He, K., Zhang, X., Ren, S., and Sun, J.: Deep residual learning for image
recognition, in: Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), 770–778, https://doi.org/10.1109/CVPR.2016.90, 2016. a
Hintz, K. S., O'Boyle, K., Dance, S. L., Al-Ali, S., Ansper, I., Blaauboer, D., Clark, M., Cress, A., Dahoui, M., Darcy, R., Hyrkannen, J., Isaksen, L., Kaas, E., Korsholm, U. S., Lavannant, M., Le Bloa, G., Mallet, E., McNicholas, C., Onvlee-Hooimeijer, J., Sass, B., Siirand, V., Vedel, H., Waller, J. A., and Yang, X.: Collecting and utilising
crowdsourced data for numerical weather prediction: Propositions from the
meeting held in Copenhagen, 4–5 December 2018, Atmos. Sci. Lett.,
20, e921, https://doi.org/10.1002/asl.921, 2019. a
Lanfranchi, V., Wrigley, S. N., Ireson, N., Wehn, U., and Ciravegna, F.:
Citizens' observatories for situation awareness in flooding, in: ISCRAM 2014
Conference Proceedings-11th International Conference on Information Systems
for Crisis Response and Management, Sheffield, 145–154, 2014. a
Le Boursicaud, R., Pénard, L., Hauet, A., Thollet, F., and Le Coz, J.:
Gauging extreme floods on YouTube: application of LSPIV to home movies for
the post-event determination of stream discharges, Hydrol. Process.,
30, 90–105, https://doi.org/10.1002/hyp.10532, 2016. a
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444,
2015. a
Lo, S.-W., Wu, J.-H., Lin, F.-P., and Hsu, C.-H.: Visual sensing for urban
flood monitoring, Sensors, 15, 20006–20029, https://doi.org/10.3390/s150820006,
2015. a
Lopez-Fuentes, L., Rossi, C., and Skinnemoen, H.: River segmentation for flood
monitoring, in: Proceedings of the IEEE International Conference on Big Data
(Big Data), IEEE, 3746–3749, https://doi.org/10.1109/BigData.2017.8258373, 2017. a, b, c, d
Lowry, C. S., Fienen, M. N., Hall, D. M., and Stepenuck, K. F.: Growing Pains
of Crowdsourced Stream Stage Monitoring Using Mobile Phones: The Development
of CrowdHydrology, Front. Earth Sci., 7, 128,
https://doi.org/10.3389/feart.2019.00128, 2019. a
Mason, D., Schumann, G.-P., Neal, J., Garcia-Pintado, J., and Bates, P.:
Automatic near real-time selection of flood water levels from high resolution
Synthetic Aperture Radar images for assimilation into hydraulic models: A
case study, Remote Sens. Environ., 124, 705–716,
https://doi.org/10.1016/j.rse.2012.06.017, 2012. a
Mason, D. C., Dance, S. L., Vetra-Carvalho, S., and Cloke, H. L.: Robust
algorithm for detecting floodwater in urban areas using synthetic aperture
radar images, J. Appl. Remote Sens., 12, 045011,
https://doi.org/10.1117/1.JRS.12.045011, 2018. a
Mettes, P., Tan, R. T., and Veltkamp, R.: On the segmentation and
classification of water in videos, in: 2014 International Conference on
Computer Vision Theory and Applications (VISAPP), IEEE, vol. 1, 283–292,
https://doi.org/10.13140/2.1.2141.2809, 2014. a
Mishra, A. K. and Coulibaly, P.: Developments in hydrometric network design: A
review, Rev. Geophys., 47, 2007RG000243, https://doi.org/10.1029/2007RG000243, 2009. a
Muste, M., Fujita, I., and Hauet, A.: Large-scale particle image velocimetry
for measurements in riverine environments, Water Resour. Res., 44, W00D19,
https://doi.org/10.1029/2008WR006950, 2008. a
Nair, V. and Hinton, G. E.: Rectified linear units improve restricted boltzmann machines, in: Proceedings of the 27th International Conference on Machine Learning (ICML'10), Omnipress, 807–814, 2010. a
Neal, J., Schumann, G., Bates, P., Buytaert, W., Matgen, P., and Pappenberger,
F.: A data assimilation approach to discharge estimation from space,
Hydrol. Process., 23, 3641–3649,
https://doi.org/10.1002/hyp.7518, 2009. a
Pan, J., Yin, Y., Xiong, J., Luo, W., Gui, G., and Sari, H.: Deep
learning-based unmanned surveillance systems for observing water levels, IEEE
Access, 6, 73561–73571, https://doi.org/10.1109/ACCESS.2018.2883702, 2018. a
Pan, S. J. and Yang, Q.: A survey on transfer learning, IEEE T.
Knowledge Data En., 22, 1345–1359, https://doi.org/10.1109/TKDE.2009.191,
2009. a
Perks, M. T., Russell, A. J., and Large, A. R. G.: Technical Note: Advances in flash flood monitoring using unmanned aerial vehicles (UAVs), Hydrol. Earth Syst. Sci., 20, 4005–4015, https://doi.org/10.5194/hess-20-4005-2016, 2016. a
Perks, M. T., Dal Sasso, S. F., Hauet, A., Jamieson, E., Le Coz, J., Pearce, S., Peña-Haro, S., Pizarro, A., Strelnikova, D., Tauro, F., Bomhof, J., Grimaldi, S., Goulet, A., Hortobágyi, B., Jodeau, M., Käfer, S., Ljubičić, R., Maddock, I., Mayr, P., Paulus, G., Pénard, L., Sinclair, L., and Manfreda, S.: Towards harmonisation of image velocimetry techniques for river surface velocity observations, Earth Syst. Sci. Data, 12, 1545–1559, https://doi.org/10.5194/essd-12-1545-2020, 2020. a, b
Ricci, S., Piacentini, A., Thual, O., Le Pape, E., and Jonville, G.: Correction of upstream flow and hydraulic state with data assimilation in the context of flood forecasting, Hydrol. Earth Syst. Sci., 15, 3555–3575, https://doi.org/10.5194/hess-15-3555-2011, 2011. a
Royem, A., Mui, C., Fuka, D., and Walter, M.: Proposing a low-tech, affordable,
accurate stream stage monitoring system, T. ASABE, 55,
2237–2242, https://doi.org/10.13031/2013.42512, 2012. a, b
Sabatelli, M., Kestemont, M., Daelemans, W., and Geurts, P.: Deep transfer
learning for art classification problems, in: Proceedings of the European
Conference on Computer Vision (ECCV), https://doi.org/10.1007/978-3-030-11012-3_48,
2018. a, b
Salehi, S. S. M., Erdogmus, D., and Gholipour, A.: Tversky loss function for
image segmentation using 3D fully convolutional deep networks, in:
International Workshop on Machine Learning in Medical Imaging,
Springer, 379–387, https://doi.org/10.1007/978-3-319-67389-9_44, 2017. a
Schoener, G.: Time-lapse photography: Low-cost, low-tech alternative for
monitoring flow depth, J. Hydrol. Eng., 23, 06017007,
https://doi.org/10.1061/(ASCE)HE.1943-5584.0001616, 2018. a
Seibert, J. and Vis, M. J.: How informative are stream level observations in
different geographic regions?, Hydrol. Process., 30, 2498–2508, 2016. a
Speight, L., Cole, S. J., Moore, R. J., Pierce, C., Wright, B., Golding, B.,
Cranston, M., Tavendale, A., Dhondia, J., and Ghimire, S.: Developing surface
water flood forecasting capabilities in Scotland: An operational pilot for
the 2014 Commonwealth Games in Glasgow, J. Flood Risk Manag., 11,
S884–S901, https://doi.org/10.1111/jfr3.12281, 2018. a
Steccanella, L., Bloisi, D., Blum, J., and Farinelli, A.: Deep Learning
Waterline Detection for Low-Cost Autonomous Boats, in: International
Conference on Intelligent Autonomous Systems (ICIAS), Springer, 613–625,
https://doi.org/10.1007/978-3-030-01370-7_48, 2018. a
Stephens, E., Schumann, G., and Bates, P.: Problems with binary pattern
measures for flood model evaluation, Hydrol. Process., 28, 4928–4937,
https://doi.org/10.1002/hyp.9979, 2014. a
Strang, G.: Linear algebra and learning from data, Wellesley-Cambridge Press, Cambridge,
2019. a
Szeliski, R.: Computer vision: algorithms and applications, Springer Science & Business Media, London, 2010. a
Tanguy, M., Chokmani, K., Bernier, M., Poulin, J., and Raymond, S.: River flood
mapping in urban areas combining Radarsat-2 data and flood return period
data, Remote Sens. Environ., 198, 442–459,
https://doi.org/10.1016/j.rse.2017.06.042, 2017. a
Tauro, F., Selker, J., Van De Giesen, N., Abrate, T., Uijlenhoet, R., Porfiri,
M., Manfreda, S., Caylor, K., Moramarco, T., Benveniste, J., et al.:
Measurements and Observations in the XXI century (MOXXI): innovation and
multi-disciplinarity to sense the hydrological cycle, Hydrolog. Sci.
J., 63, 169–196, https://doi.org/10.1080/02626667.2017.1420191, 2018. a
The Ad Hoc Group, Vörösmarty, C., Askew, A., Grabs, W., Barry, R. G., Birkett, C., Döll, P., Goodison, B., Hall, A., Jenne, R., Kitaev, L., Landwehr, J., Keeler, M., Leavesley, G., Schaake, J., Strzepek, K., Sundarvel, S. S, Takeuchi, K., and Webster, F.: Global water data: A newly
endangered species, EOS T. Am. Geophys. Un., 82, 54–58,
https://doi.org/10.1029/01EO00031, 2001. a
van Meerveld, H. J. I., Vis, M. J. P., and Seibert, J.: Information content of stream level class data for hydrological model calibration, Hydrol. Earth Syst. Sci., 21, 4895–4905, https://doi.org/10.5194/hess-21-4895-2017, 2017. a
Vandaele, R., Aceto, J., Muller, M., Péronnet F., Debat, V., Wang, C.-W., Huang, C.-T., Jodogne, S., Martinive, P., Geurts, P., and Marée, M.: Landmark
detection in 2D bioimages for geometric morphometrics: a multi-resolution
tree-based approach, Sci. Rep.-UK, 8, 1–13,
https://doi.org/10.1038/s41598-017-18993-5, 2018. a
Vandaele, R., Dance, S. L., and Ojha, V.: Deep learning for the estimation of
water-levels using river cameras: networks and datasets, University of Reading [data set], https://doi.org/10.17864/1947.282, 2020. a
Vandaele, R., Dance, S. L., and Ojha, V.: Automated water segmentation and river level detection on camera images using transfer learning,
in: Pattern Recognition: 42nd DAGM German Conference, DAGM GCPR 2020, Tübingen, Germany, Proceedings 42, Springer, 232–245, https://doi.org/10.1007/978-3-030-71278-5_17, 2021. a, b, c, d, e, f, g
Vetra-Carvalho, S., Dance, S. L., Mason, D., Waller, J., Smith, P., Tabeart,
J., and Cooper, E.: River water level height measurements obtained from river
cameras near Tewkesbury, Mendeley Data [data set], https://doi.org/10.17632/769cyvdznp.1,
2020a. a
Vetra-Carvalho, S., Dance, S. L., Mason, D. C., Waller, J. A., Cooper, E. S.,
Smith, P. J., and Tabeart, J. M.: Collection and extraction of water level
information from a digital river camera image dataset, Data in Brief, 33,
106338, https://doi.org/10.1016/j.dib.2020.106338, 2020b. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Walker, D., Haile, A. T., Gowing, J., Legesse, Y., Gebrehawariat, G., Hundie, H., Berhanu, D., and Parkin, G.: Guideline: Community-based hydroclimate monitoring, REACH Working Paper 5, University of Oxford, Oxford, UK, 2019. a
Werner, M., Blazkova, S., and Petr, J.: Spatially distributed observations in
constraining inundation modelling uncertainties, Hydrol. Process., 19, 3081–3096, https://doi.org/10.1002/hyp.5833, 2005. a
Yan, K., Di Baldassarre, G., Solomatine, D. P., and Schumann, G. J.-P.: A
review of low-cost space-borne data for flood modelling: topography, flood
extent and water level, Hydrol. Process., 29, 3368–3387,
https://doi.org/10.1002/hyp.10449, 2015. a
Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., and Torralba, A.: Scene
parsing through ADE20k dataset, in: Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition (CVPR), 633–641,
https://doi.org/10.1109/CVPR.2017.544, 2017. a, b
Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A., and Torralba,
A.: Semantic understanding of scenes through the ADE20k dataset,
International Journal on Computer Vision, https://doi.org/10.1007/s11263-018-1140-0,
2018. a, b
Zhou, S., Kan, P., Silbernagel, J., and Jin, J.: Application of image
segmentation in surface water extraction of freshwater lakes using radar
data, ISPRS Int. J. Geo-Inf., 9, 424,
https://doi.org/10.3390/ijgi9070424, 2020. a
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 applying 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.
The acquisition of river-level data is a critical task for the understanding of flood events but...