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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Vydané v:	Scientific reports Ročník 10; číslo 1; s. 8589
Hlavní autori:	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
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 22.05.2020 Nature Publishing Group
Predmet:	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
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