Continuous monitoring of suspended sediment concentrations using image analytics and deriving inherent correlations by machine learning

The barriers for the development of continuous monitoring of Suspended Sediment Concentration (SSC) in channels/rivers include costs and technological gaps but this paper shows that a solution is feasible by: (i) using readily available high-resolution images; (ii) transforming the images into image...

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Bibliographic Details

Published in:	Scientific reports Vol. 10; no. 1; p. 8589
Main Authors:	Ghorbani, Mohammad Ali, Khatibi, Rahman, Singh, Vijay P., Kahya, Ercan, Ruskeepää, Heikki, Saggi, Mandeep Kaur, Sivakumar, Bellie, Kim, Sungwon, Salmasi, Farzin, Hasanpour Kashani, Mahsa, Samadianfard, Saeed, Shahabi, Mahmood, Jani, Rasoul
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 22.05.2020 Nature Publishing Group
Subjects:	639/166/986 704/242 Environmental monitoring Humanities and Social Sciences Learning algorithms Machine learning multidisciplinary Observational learning Prediction models Rivers Science Science (multidisciplinary) Sediment concentration Suspended sediments
ISSN:	2045-2322, 2045-2322
Online Access:	Get full text
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