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Estimation of Suspended Sediment Concentration and Yield Using Linear Models, Random Forests and Quantile Regression Forests by T. Francke; J.A. López‐Tarazón; B. Schröder is a Environmental Science article available to read on EtoBox.

What is Estimation of Suspended Sediment Concentration and Yield Using Linear Models, Random Forests and Quantile Regression Forests about?

## Abstract For sediment yield estimation, intermittent measurements of suspended sediment concentration (SSC) have to be interpolated to derive a continuous sedigraph. Traditionally, sediment rating curves (SRCs) based on univariate linear regression of discharge and SSC (or the logarithms thereof) are used but alternative approaches (e.g. fuzzy logic, artificial neural networks, etc.) exist. This paper presents a comparison of the applicability of traditional SRCs, generalized linear models (GLMs) and non‐parametric regression using Random Forests (RF) and Quantile Regression Forests (QRF) applied to a dataset of SSC obtained for four subcatchments (0·08, 41, 145 and 445 km^2^) in the Central Spanish Pyrenees. The observed SSCs are highly variable and range over six orders of magnitude. For these data, traditional SRCs performed inadequately due to the over‐simplification of relating SSC solely to discharge. Instead, the multitude of acting processes required more flexibility to model these nonlinear relationships. Thus, alternative advanced machine learning techniques that have been successfully applied in other disciplines were tested. GLMs provide the option of including other

Who reads Estimation of Suspended Sediment Concentration and Yield Using Linear Models, Random Forests and Quantile Regression Forests?

Author

T. Francke; J.A. López‐Tarazón; B. Schröder

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0885-6087)

Published

2008

Language

EN

Field

Environmental Science (Physical Sciences)