Suspended sediment estimation using neuro-fuzzy and neural network approaches/Estimation des matières en suspension par des approches neurofloues et à base de réseau de neurones

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Generalized regression neural networks for evapotranspiration modelling (2006, background): “Studies on ANN application in the area of hydrology include rainfall–runoff modelling (Minns & Hall, 1996; Dawson & Wilby, 1998; streamflow prediction (Cigizoğlu, 2003; Sahoo & Ray, 2006) and suspended sediment modelling (Tayfur, 2002; Kisi, 2005).”
Prediction of Suspended Sediment Load Using Data-Driven Models (2019, result): “Basically, in this study, the input pattern was slightly different than the previous study by Kisi [30], as the input included not only the sediment concentration and streamflow but also include the water level.”
Estimation of suspended sediment concentration and yield using linear models, random forests and quantile regression forests (2008, background): “Kisi (2005) demonstrated the inclusion of multiple predictors (discharge and SSC of preceding time step) with fuzzy logic and artificial neural networks and obtained a slightly better performance with fuzzy logic, which was also reported by Lohani et al. (2007).”