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Application of Machine Learning Approaches in Particle Tracking Model to Estimate Sediment Transport in Natural Streams.
- Source :
- Water Resources Management; Jun2024, Vol. 38 Issue 8, p2905-2934, 30p
- Publication Year :
- 2024
-
Abstract
- Numerous empirical equations and machine learning (ML) techniques have emerged to forecast dispersion coefficients in open channels. However, the efficacy of certain learning-based models in predicting these coefficients remains unstudied. Also, the direct application of machine learning-derived dispersion coefficients to Lagrangian sediment transport models has not been investigated. The present study utilizes data from prior research to assess the performance of ensemble ML-based models, specifically, random forest regression (RFR) and gradient boosting regression (GBR) inn estimating longitudinal and transverse dispersion in natural streams. The optimal hyper-parameters of these ensemble models were fine-tuned using grid-search cross-validation. The ML-based dispersion models were then integrated into a Lagrangian particle tracking model (PTM) to simulate suspended sediment concentration in natural streams. Suspended sediment concentration distribution maps generated from developed PTM with ML-based dispersion coefficients were compared with field data. The findings indicated that the GBR model, with a coefficient of determination (R<superscript>2</superscript>) of 0.95, outperformed the RFR model, which had an R<superscript>2</superscript> of 0.9, in predicting longitudinal dispersion coefficients in a natural stream across both training and testing stages. However, during the testing phase, the RFR model with an R<superscript>2</superscript> of 0.94 performed better than the GBR model with an R<superscript>2</superscript> of 0.91 in predicting transverse dispersion. Both models consistently underestimated dispersion coefficients in both training and testing stages. Comparisons between the PTM with ensemble dispersion coefficients and empirical-based dispersion relationships revealed the superior performance of the GBR model compared to the other two methods. [ABSTRACT FROM AUTHOR]
- Subjects :
- MACHINE learning
SEDIMENT transport
SUSPENDED sediments
RANDOM forest algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 09204741
- Volume :
- 38
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- Water Resources Management
- Publication Type :
- Academic Journal
- Accession number :
- 177371408
- Full Text :
- https://doi.org/10.1007/s11269-024-03798-9