A1 Refereed original research article in a scientific journal
Continuous monitoring of suspended sediment concentrations using image analytics and deriving inherent correlations by machine learning
Authors: Mohammad Ali Ghorbani, Rahman Khatibi, Vijay P. Singh, Ercan Kahya, Heikki Ruskeepää, Mandeep Kaur Saggi, Bellie Sivakumar, Sungwon Kim, Farzin Salmasi, Mahsa Hasanpour Kashani, Saeed Samadianfard, Mahmood Shahabi, Rasoul Jani
Publisher: NATURE PUBLISHING GROUP
Publication year: 2020
Journal: Scientific Reports
Journal name in source: SCIENTIFIC REPORTS
Journal acronym: SCI REP-UK
Article number: 8589
Volume: 10
Issue: 1
Number of pages: 9
ISSN: 2045-2322
eISSN: 2045-2322
DOI: https://doi.org/10.1038/s41598-020-64707-9
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/48812600
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 analytics to form a modelling dataset; and (iii) constructing predictive models by learning inherent correlation between observed SSC values and their image analytics. High-resolution images were taken of water containing a series of SSC values using an exploratory flume. Machine learning is processed by dividing the dataset into training and testing sets and the paper uses the following models: Generalized Linear Machine (GLM) and Distributed Random Forest (DRF). Results show that each model is capable of reliable predictions but the errors at higher SSC are not fully explained by modelling alone. Here we offer sufficient evidence for the feasibility of a continuous SSC monitoring capability in channels before the next phase of the study with the goal of producing practice guidelines.
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