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

Ouellet-Proulx, Sébastien; St-Hilaire, André; Courtenay, Simon Charles; Haralampides, Katy A. (2016). Estimation of suspended sediment concentration in the Saint John River using rating curves and a machine learning approach. Hydrological Sciences Journal . DOI: 10.1080/02626667.2015.1051982.

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Résumé

AbstractSedimentation in navigable waterways and harbours is of concern for a number of 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.

Type de document:
Mots-clés libres: suspended sediment; model tree; machine learning; regression; sediment rating curve; Saint John river
Centre: Centre Eau Terre Environnement
Date de dépôt: 20 déc. 2016 15:05
Dernière modification: 21 déc. 2016 15:03
URI: http://espace.inrs.ca/id/eprint/3897

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