Extending the Applicability of the Meyer–Peter and Müller Bed Load Transport Formula
| Autor: | Thomas Papalaskaris, Konstantinos Vantas, Epaminondas Sidiropoulos, Vlassios Hrissanthou |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
bed load transport
random forests Water supply for domestic and industrial purposes Geography Planning and Development Hydraulic engineering Aquatic Science Biochemistry Regression Gaussian processes regression k-nearest neighbors algorithm Random forest sediment transport symbols.namesake symbols Calibration Meyer–Peter and Müller formula Applied mathematics TC1-978 Gaussian process Nonlinear regression TD201-500 Smoothing Water Science and Technology Bed load Mathematics |
| Zdroj: | Water, Vol 13, Iss 2817, p 2817 (2021) Water Volume 13 Issue 20 |
| ISSN: | 2073-4441 |
| Popis: | The present paper deals with the applicability of the Meyer–Peter and Müller (MPM) bed load transport formula. The performance of the formula is examined on data collected in a particular location of Nestos River in Thrace, Greece, in comparison to a proposed Εnhanced MPM (EMPM) formula and to two typical machine learning methods, namely Random Forests (RF) and Gaussian Processes Regression (GPR). The EMPM contains new adjustment parameters allowing calibration. The EMPM clearly outperforms MPM and, also, it turns out to be quite competitive in comparison to the machine learning schemes. Calibrations are repeated with suitably smoothed measurement data and, in this case, EMPM outperforms MPM, RF and GPR. Data smoothing for the present problem is discussed in view of a special nearest neighbor smoothing process, which is introduced in combination with nonlinear regression. |
| Databáze: | OpenAIRE |
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