{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T08:35:39Z","timestamp":1773390939794,"version":"3.50.1"},"reference-count":91,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T00:00:00Z","timestamp":1716163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"INRS from service revenues"},{"name":"the balances of Professor Karem Chokmani\u2019s completed projects"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2013Sutcliffe 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.<\/jats:p>","DOI":"10.3390\/rs16101808","type":"journal-article","created":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T06:31:58Z","timestamp":1716186718000},"page":"1808","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Deep Learning-Based Automatic River Flow Estimation Using RADARSAT Imagery"],"prefix":"10.3390","volume":"16","author":[{"given":"Samar","family":"Ziadi","sequence":"first","affiliation":[{"name":"Water Earth Environment Center, National Institute of Scientific Research (INRS), 490 de la Couronne Street, Quebec, QC G1K 9A9, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0018-0761","authenticated-orcid":false,"given":"Karem","family":"Chokmani","sequence":"additional","affiliation":[{"name":"Water Earth Environment Center, National Institute of Scientific Research (INRS), 490 de la Couronne Street, Quebec, QC G1K 9A9, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3891-3748","authenticated-orcid":false,"given":"Chayma","family":"Chaabani","sequence":"additional","affiliation":[{"name":"Water Earth Environment Center, National Institute of Scientific Research (INRS), 490 de la Couronne Street, Quebec, QC G1K 9A9, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8570-2110","authenticated-orcid":false,"given":"Anas","family":"El Alem","sequence":"additional","affiliation":[{"name":"Water Earth Environment Center, National Institute of Scientific Research (INRS), 490 de la Couronne Street, Quebec, QC G1K 9A9, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.jhydrol.2012.07.027","article-title":"Enhancement and comprehensive evaluation of the Rating Curve Model for different river sites","volume":"464\u2013465","author":"Barbetta","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"847","DOI":"10.5194\/hess-13-847-2009","article-title":"A dynamic rating curve approach to indirect discharge measurement","volume":"13","author":"Dottori","year":"2009","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.jhydrol.2012.04.031","article-title":"Temporal variability in stage-discharge relationships","volume":"446\u2013447","author":"Guerrero","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"W03427","DOI":"10.1029\/2007WR006133","article-title":"Estimation of river discharge, propagation speed, and hydraulic geometry from space: Lena River, Siberia","volume":"44","author":"Smith","year":"2008","journal-title":"Water Resour. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1175\/1525-7541(2002)003<0660:EOFDFC>2.0.CO;2","article-title":"Estimates of Freshwater Discharge from Continents: Latitudinal and Seasonal Variations","volume":"3","author":"Dai","year":"2002","journal-title":"J. Hydrometeorol."},{"key":"ref_6","unstructured":"Chokmani, K., Perreault, S., Jacome, A., Bernier, M., Poulin, J., and Gauthier, Y. (2015). D\u00e9veloppement d\u2019une M\u00e9thodologie D\u2019estimation du D\u00e9bit en Rivi\u00e8re pour les Sites Non-Jaug\u00e9s \u00e0 l'aide de L'imagerie RADARSAT dans l'est du Canada, Institut National de la Recherche Scientifique. Rapport Technique R1683."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/S0022-1694(03)00129-X","article-title":"Evaluating the potential for measuring river discharge from space","volume":"278","author":"Bjerklie","year":"2003","journal-title":"J. Hydrol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1002\/hyp.8226","article-title":"Is the Current Flood of Data Enough? A Treatise on Research Needs for the Improvement of Flood Modelling","volume":"26","author":"Uhlenbrook","year":"2012","journal-title":"Hydrol. Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7753","DOI":"10.1029\/2019WR025599","article-title":"Comparing Discharge Estimates Made via the BAM Algorithm in High-Order Arctic Rivers Derived Solely from Optical CubeSat, Landsat, and Sentinel-2 Data","volume":"55","author":"Feng","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1002\/2017MS000986","article-title":"A Hybrid of Optical Remote Sensing and Hydrological Modeling Improves Water Balance Estimation","volume":"10","author":"Gleason","year":"2018","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1109\/JSTARS.2013.2283402","article-title":"Proof of Concept of an Altimeter-Based River Forecasting System for Transboundary Flow Inside Bangladesh","volume":"7","author":"Hossain","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.jhydrol.2017.01.009","article-title":"River discharge estimation at daily resolution from satellite altimetry over an entire river basin","volume":"546","author":"Tourian","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.jhydrol.2009.11.006","article-title":"Regional estimation of extreme suspended sediment concentrations using watershed characteristics","volume":"380","author":"Tramblay","year":"2010","journal-title":"J. Hydrol."},{"key":"ref_14","first-page":"101930","article-title":"Estimation of flow in various sizes of streams using the Sentinel-1 Synthetic Aperture Radar (SAR) data in Han River Basin, Korea","volume":"83","author":"Ahmad","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Alsdorf, D.E., Rodr\u00edguez, E., and Lettenmaier, D.P. (2007). Measuring surface water from space. Rev. Geophys., 45.","DOI":"10.1029\/2006RG000197"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1126\/science.1089802","article-title":"Tracking Fresh Water from Space","volume":"301","author":"Alsdorf","year":"2003","journal-title":"Science"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rse.2018.10.008","article-title":"Discharge estimation in high-mountain regions with improved methods using multisource remote sensing: A case study of the Upper Brahmaputra River","volume":"219","author":"Huang","year":"2018","journal-title":"Remote. Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Koblinsky, C.J., Clarke, R.T., Brenner, A.C., and Frey, H. (1993). Measurement of River Level Variations with Satellite Altimetry, Wiley Online Library.","DOI":"10.1029\/93WR00542"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"17916","DOI":"10.1073\/pnas.1003292107","article-title":"Satellite-based global-ocean mass balance estimates of interannual variability and emerging trends in continental freshwater discharge","volume":"107","author":"Syed","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1002\/(SICI)1099-1085(199708)11:10<1427::AID-HYP473>3.0.CO;2-S","article-title":"Satellite Remote Sensing of River Inundation Area, Stage, and Discharge: A Review","volume":"11","author":"Smith","year":"1997","journal-title":"Hydrol. Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1016\/j.jhydrol.2016.06.024","article-title":"Constructing river stage-discharge rating curves using remotely sensed river cross-sectional inundation areas and river bathymetry","volume":"540","author":"Pan","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3596","DOI":"10.1002\/hyp.9469","article-title":"Remote sensing of river stage using the cross-sectional inundation area-river stage relationship (IARSR) constructed from digital elevation model data","volume":"27","author":"Pan","year":"2013","journal-title":"Hydrol. Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.rse.2016.03.019","article-title":"Estimating continental river basin discharges using multiple remote sensing data sets","volume":"179","author":"Sichangi","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Sichangi, A.W., Wang, L., and Hu, Z. (2018). Estimation of River Discharge Solely from Remote-Sensing Derived Data: An Initial Study Over the Yangtze River. Remote. Sens., 10.","DOI":"10.3390\/rs10091385"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"LBA 26-1","DOI":"10.1029\/2001JD000609","article-title":"Surface water dynamics in the Amazon Basin: Application of satellite radar altimetry","volume":"107","author":"Birkett","year":"2002","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.rse.2004.07.007","article-title":"Ob\u2019 river discharge from TOPEX\/Poseidon satellite altimetry (1992\u20132002)","volume":"93","author":"Kouraev","year":"2004","journal-title":"Remote. Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1002\/hyp.9225","article-title":"Modelling rating curves using remotely sensed LiDAR data","volume":"26","author":"Nathanson","year":"2012","journal-title":"Hydrol. Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3787","DOI":"10.1002\/2014WR016618","article-title":"Stage-discharge rating curves based on satellite altimetry and modeled discharge in the Amazon basin","volume":"52","author":"Paris","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1516","DOI":"10.1016\/j.jhydrol.2014.08.044","article-title":"Assessing the potential global extent of SWOT river discharge observations","volume":"519","author":"Pavelsky","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"751","DOI":"10.5194\/hess-21-751-2017","article-title":"Application of CryoSat-2 altimetry data for river analysis and modelling","volume":"21","author":"Schneider","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/JSTARS.2009.2034614","article-title":"Preliminary Characterization of SWOT Hydrology Error Budget and Global Capabilities","volume":"3","author":"Biancamaria","year":"2009","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gehring, J., Duvvuri, B., and Beighley, E. (2022). Deriving River Discharge Using Remotely Sensed Water Surface Characteristics and Satellite Altimetry in the Mississippi River Basin. Remote. Sens., 14.","DOI":"10.3390\/rs14153541"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Amaral, F.R.D., Pellarin, T., Trung, T.N., Tu, T.A., and Gratiot, N. (2024). Enhancing discharge estimation from SWOT satellite data in a tropical tidal river environment. PLoS Water, 3.","DOI":"10.1371\/journal.pwat.0000226"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"113030","DOI":"10.1016\/j.rse.2022.113030","article-title":"High-resolution satellite images combined with hydrological modeling derive river discharge for headwaters: A step toward discharge estimation in ungauged basins","volume":"277","author":"Huang","year":"2022","journal-title":"Remote. Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e2023GL105839","DOI":"10.1029\/2023GL105839","article-title":"Satellite Video Remote Sensing for Estimation of River Discharge","volume":"50","author":"Masafu","year":"2023","journal-title":"Geophys. Res. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1002\/hyp.9647","article-title":"Daily discharge estimation at ungauged river sites using remote sensing","volume":"28","author":"Birkinshaw","year":"2014","journal-title":"Hydrol. Process"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4527","DOI":"10.1002\/2015WR018434","article-title":"An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope","volume":"52","author":"Durand","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Fulton, J., Anderson, I., Chiu, C.-L., Sommer, W., Adams, J., Moramarco, T., Bjerklie, D., Fulford, J., Sloan, J., and Best, H. (2020). QCam: sUAS-Based Doppler Radar for Measuring River Discharge. Remote Sens., 12.","DOI":"10.3390\/rs12203317"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.1029\/96WR00752","article-title":"Estimation of Discharge From Three Braided Rivers Using Synthetic Aperture Radar Satellite Imagery: Potential Application to Ungaged Basins","volume":"32","author":"Smith","year":"1996","journal-title":"Water Resour. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1016\/j.jhydrol.2015.10.038","article-title":"Artificial intelligence based models for stream-flow forecasting: 2000\u20132015","volume":"530","author":"Yaseen","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"836","DOI":"10.1016\/j.jhydrol.2014.06.013","article-title":"Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control","volume":"517","author":"Chang","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"\u00dcne\u015f, F., Demirci, M., Zelenakova, M., \u00c7al\u0131\u015f\u0131c\u0131, M., Ta\u015far, B., Vranay, F., and Kaya, Y.Z. (2020). River Flow Estimation Using Artificial Intelligence and Fuzzy Techniques. Water, 12.","DOI":"10.3390\/w12092427"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.jhydrol.2016.11.033","article-title":"An Emotional ANN (EANN) approach to modeling rainfall-runoff process","volume":"544","author":"Nourani","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"e2021WR031841","DOI":"10.1029\/2021WR031841","article-title":"RivQNet: Deep Learning Based River Discharge Estimation Using Close-Range Water Surface Imagery","volume":"59","author":"Ansari","year":"2023","journal-title":"Water Resour. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"8558","DOI":"10.1029\/2018WR022643","article-title":"A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists","volume":"54","author":"Shen","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2231","DOI":"10.2166\/ws.2019.105","article-title":"Detection of multiple leakage points in water distribution networks based on convolutional neural networks","volume":"19","author":"Fang","year":"2019","journal-title":"Water Supply"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"115058","DOI":"10.1016\/j.watres.2019.115058","article-title":"Deep learning identifies accurate burst locations in water distribution networks","volume":"166","author":"Zhou","year":"2019","journal-title":"Water Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s00477-020-01776-2","article-title":"Short-term water quality variable prediction using a hybrid CNN-LSTM deep learning model","volume":"34","author":"Barzegar","year":"2020","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Liu, P., Wang, J., Sangaiah, A.K., Xie, Y., and Yin, X. (2019). Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment. Sustainability, 11.","DOI":"10.3390\/su11072058"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"294","DOI":"10.18178\/ijesd.2019.10.10.1190","article-title":"Accurate Prediction of Streamflow Using Long Short-Term Memory Network: A Case Study in the Brazos River Basin in Texas","volume":"10","author":"Damavandi","year":"2019","journal-title":"Int. J. Environ. Sci. Dev."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"11344","DOI":"10.1029\/2019WR026065","article-title":"Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning","volume":"55","author":"Kratzert","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2471","DOI":"10.1007\/s11269-019-02255-2","article-title":"Lake Level Prediction using Feed Forward and Recurrent Neural Networks","volume":"33","author":"Hrnjica","year":"2019","journal-title":"Water Resour. Manag."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5631","DOI":"10.1029\/2018WR024136","article-title":"Measuring River Wetted Width From Remotely Sensed Imagery at the Subpixel Scale With a Deep Convolutional Neural Network","volume":"55","author":"Ling","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_55","first-page":"84","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_56","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep Learning, MIT Press Cambridge."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.ejrad.2019.02.038","article-title":"The present and future of deep learning in radiology","volume":"114","author":"Saba","year":"2019","journal-title":"Eur. J. Radiol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"105596","DOI":"10.1016\/j.knosys.2020.105596","article-title":"A review of deep learning with special emphasis on architectures, applications and recent trends","volume":"194","author":"Sengupta","year":"2020","journal-title":"Knowl. Based Syst."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1002\/mp.13264","article-title":"Deep learning in medical imaging and radiation therapy","volume":"46","author":"Sahiner","year":"2019","journal-title":"Med. Phys."},{"key":"ref_60","unstructured":"Eamer, J. (2024, May 17). Boreal Shield and Newfoundland Boreal Ecozones+ Evidence for Key Findings Summary. Available online: https:\/\/policycommons.net\/artifacts\/1933280\/boreal-shield-and-newfoundland-boreal-ecozones-evidence-for-key-findings-summary\/2685049\/."},{"key":"ref_61","unstructured":"Colombo, S.J., Cherry, M.L., Graham, C., Greifenhagen, S., McAlpine, R.S., Papadopol, C.S., Parker, W.C., Scarr, T., Ter-Mikaelian, M.T., and Flannigan, M.D. (1998). The Impacts of Climate Change on Ontarios Forests, Ontario Ministry of Natural Resources, Ontario Forest Research Institute."},{"key":"ref_62","unstructured":"Lowe, J.J., Power, K., and Marsan, M.W. (2024, May 17). Available online: https:\/\/ostrnrcan-dostrncan.canada.ca\/handle\/1845\/232105."},{"key":"ref_63","unstructured":"Les gouvernements f\u00e9d\u00e9ral, provinciaux et territoriaux du C (2010). Biodiversit\u00e9 Canadienne: \u00c9tat et Tendances Des \u00c9cosyst\u00e8mes En 2010, Conseils Canadiens des Ministres des Ressources. Available online: https:\/\/www.biodivcanada.ca\/rapports\/biodiversite-canadienne-etat-et-tendances-des-ecosystemes-en-2010."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Huang, C., Nguyen, B.D., Zhang, S., Cao, S., and Wagner, W. (2017). A Comparison of Terrain Indices toward Their Ability in Assisting Surface Water Mapping from Sentinel-1 Data. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6050140"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.jhydrol.2011.03.051","article-title":"Height Above the Nearest Drainage\u2014A hydrologically relevant new terrain model","volume":"404","author":"Nobre","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_66","unstructured":"Simard, P.Y., Steinkraus, D., and Platt, J.C. (2003, January 3\u20136). Best practices for convolutional neural networks applied to visual document analysis. Proceedings of the 7th International Conference on Document Analysis and Recognition, ICDAR, Edinburgh, UK."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1985). Learning Internal Representations by Error Propagation, California Univ San Diego La Jolla Inst for Cognitive Science.","DOI":"10.21236\/ADA164453"},{"key":"ref_68","unstructured":"Kingma, D.P., and Ba, J. (2017). Adam: A Method for Stochastic Optimization 2017. arXiv."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3005348","article-title":"Structured Pruning of Deep Convolutional Neural Networks","volume":"13","author":"Anwar","year":"2017","journal-title":"ACM J. Emerg. Technol. Comput. Syst. (JETC)"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Nagi, J., Ducatelle, F., Di Caro, G.A., Cire\u015fan, D., Meier, U., Giusti, A., Nagi, F., Schmidhuber, J., and Gambardella, L.M. (2011, January 16\u201318). Max-Pooling Convolutional Neural Networks for Vision-Based Hand Gesture Recognition. Proceedings of the 2011 IEEE international conference on signal and image processing applications (ICSIPA), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICSIPA.2011.6144164"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Wu, J.-N. (2016, January 14\u201316). Compression of Fully-Connected Layer in Neural Network by Kronecker Product. Proceedings of the 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI), Chiang Mai, Thailand.","DOI":"10.1109\/ICACI.2016.7449822"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1123\/jab.2016-0355","article-title":"A Comparison of Self-Selected Walking Speeds and Walking Speed Variability When Data Are Collected During Repeated Discrete Trials and During Continuous Walking","volume":"33","author":"Brown","year":"2017","journal-title":"J. Appl. Biomech."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Al-Sabaawi, A., Ibrahim, H.M., Arkah, Z.M., Al-Amidie, M., and Alzubaidi, L. (2021, January 13\u201315). Amended Convolutional Neural Network with Global Average Pooling for Image Classification. Proceedings of the International Conference on Intelligent Systems Design and Applications, Online.","DOI":"10.1007\/978-3-030-71187-0_16"},{"key":"ref_74","first-page":"1","article-title":"A Guide to Convolutional Neural Networks for Computer Vision","volume":"8","author":"Khan","year":"2018","journal-title":"Synth. Lect. Comput. Vis."},{"key":"ref_75","unstructured":"Sewak, M., Karim, M.R., and Pujari, P. (2018). Practical Convolutional Neural Networks: Implement Advanced Deep Learning Models Using Python, Packt Publishing Ltd."},{"key":"ref_76","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_77","unstructured":"Smith, L.N. (2018). A Disciplined Approach to Neural Network Hyper-Parameters: Part 1\u2014Learning Rate, Batch Size, Momentum, and Weight Decay. arXiv."},{"key":"ref_78","unstructured":"Gulli, A., Kapoor, A., and Pal, S. (2019). Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and More with TensorFlow 2 and the Keras API, Packt Publishing Ltd."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/0022-1694(70)90255-6","article-title":"River Flow Forecasting through Conceptual Models Part I\u2014A Discussion of Principles","volume":"10","author":"Nash","year":"1970","journal-title":"J. Hydrol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1029\/95WR00145","article-title":"Estimation of discharge from braided glacial rivers using ERS-1 Synthetic\u2014Aperture Radar\u2014First results","volume":"31","author":"Smith","year":"1995","journal-title":"Water Resour. Res."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1002\/esp.403","article-title":"Characterization of the Spatial Variability of Channel Morphology","volume":"27","author":"Moody","year":"2002","journal-title":"Earth Surf. Process. Landf. J. Br. Geomorphol. Res. Group"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"74","DOI":"10.3389\/feart.2015.00074","article-title":"Metric-Resolution 2D River Modeling at the Macroscale: Computational Methods and Applications in a Braided River","volume":"3","author":"Schubert","year":"2015","journal-title":"Front. Earth Sci."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/S0955-5986(02)00047-X","article-title":"The uncertainty in a current meter measurement","volume":"13","author":"Herschy","year":"2002","journal-title":"Flow Meas. Instrum."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1080\/02626660009492374","article-title":"Rating curve modelling with Manning\u2019s equation to manage instability and improve extrapolation","volume":"45","author":"Leonard","year":"2000","journal-title":"Hydrol. Sci. J."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1139\/l88-109","article-title":"Uncertainties in the single determination of river discharge: A literature review","volume":"15","author":"Pelletier","year":"1988","journal-title":"Can. J. Civ. Eng."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.envsoft.2014.09.011","article-title":"Streamflow rating uncertainty: Characterisation and impacts on model calibration and performance","volume":"63","author":"Zhang","year":"2015","journal-title":"Environ. Model. Softw."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1002\/hyp.9567","article-title":"Uncertainty in streamflow rating curves: Methods, controls and consequences","volume":"28","author":"Tomkins","year":"2012","journal-title":"Hydrol. Process."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Pristyanto, Y., Adi, S., and Sunyoto, A. (2019, January 24\u201325). The Effect of Feature Selection on Classification Algorithms in Credit Approval. Proceedings of the 2019 International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, Indonesia.","DOI":"10.1109\/ICOIACT46704.2019.8938523"},{"key":"ref_89","first-page":"1","article-title":"Feature Selection: A Data Perspective","volume":"50","author":"Li","year":"2017","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Mengen, D., Ottinger, M., Leinenkugel, P., and Ribbe, L. (2020). Modeling River Discharge Using Automated River Width Measurements Derived from Sentinel-1 Time Series. Remote. Sens., 12.","DOI":"10.3390\/rs12193236"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1061\/(ASCE)1084-0699(2002)7:2(100)","article-title":"Creating a Terrain Model for Floodplain Mapping","volume":"7","author":"Tate","year":"2002","journal-title":"J. Hydrol. Eng."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/10\/1808\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:45:08Z","timestamp":1760107508000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/10\/1808"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,20]]},"references-count":91,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16101808"],"URL":"https:\/\/doi.org\/10.3390\/rs16101808","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,20]]}}}