Estimation of suspended sediment concentration in the Saint John River using rating curves and a machine learning approach |
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| Authors: | S Ouellet-Proulx A St-Hilaire SC Courtenay KA Haralampides |
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| Institution: | 1. Institut National de la Recherche Scienti?que, Quebec City, Quebec, Canada;2. Canadian Rivers Institute, Fredericton, New Brunswick, Canadasebastien.ouellet-proulx@ete.inrs.ca;4. Canadian Rivers Institute, Fredericton, New Brunswick, Canada;5. Department of Environment and Resource Studies, University of Waterloo, Waterloo, Ontario, Canada;6. Canadian Water Network, Waterloo, Ontario, Canada;7. Department of Civil Engineering, University of New Brunswick, New Brunswick, Canada |
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| Abstract: | ABSTRACTSedimentation 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 (M5?) with different combinations of predictors. Estimated suspended sediment concentrations were multiplied by measured flows to estimate suspended sediment loads. Best results were obtained using M5? with four predictors, returning an R2 of 0.72 on calibration data and an R2 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. EDITOR M.C. Acreman; ASSOCIATE EDITOR B. Touaibia |
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| Keywords: | suspended sediment model model tree machine learning regression sediment rating curve |
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