NDLI: Estimation of suspended sediment concentration in the Saint John River using rating curves and a machine learning approach

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Estimation of suspended sediment concentration in the Saint John River using rating curves and a machine learning approach

Content Provider	Scilit
Author	Ouellet-Proulx, S. St-Hilaire, A. Courtenay, S. C. Haralampides, K. A.
Copyright Year	2016
Description	Sedimentation in navigable waterways and harbours is of concern for many water and port managers. One potential source of variability in sedimentation is the annual sediment load of the river that empties in the harbour. The main objective of this study was to use some of the regularly monitored hydro-meteorological variables to compare estimates of hourly suspended sediment concentration in the Saint John River using a sediment rating curve and a model tree (M5ʹ) with different combinations of predictors. Estimated suspended sediment concentrations were multiplied by measured flows to estimate suspended sediment loads. Best results were obtained using M5ʹ with four predictors, returning an $R^{2}$ of 0.72 on calibration data and an $R^{2}$ of 0.46 on validation data. Total load was underestimated by 1.41% for the calibration period and overestimated by 2.38% for the validation period. Overall, the model tree approach is suggested for its relative ease of implementation and constant performance.
Related Links	https://www.tandfonline.com/doi/pdf/10.1080/02626667.2015.1051982?needAccess=true
ISSN	02626667
e-ISSN	21503435
DOI	10.1080/02626667.2015.1051982
Journal	Hydrological Sciences Journal
Language	English
Publisher	Informa UK Limited
Publisher Date	2016-05-04
Access Restriction	Open
Subject Keyword	Journal: Hydrological Sciences Journal Marine and Freshwater Biology Suspended Sediment Model Model Tree Machine Learning Regression Sediment Rating Curve
Content Type	Text
Resource Type	Article
Subject	Water Science and Technology

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