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Predicting stable gravel-bed river hydraulic geometry: A test of novel, advanced, hybrid data mining algorithms

  • Accurate prediction of stable alluvial hydraulic geometry, in which erosion and sedimentation are in equilibrium, is one of the most difficult but critical topics in the field of river engineering. Data mining algorithms have been gaining more attention in this field due to their high performance and flexibility. However, an understanding of the potential for these algorithms to provide fast,Accurate prediction of stable alluvial hydraulic geometry, in which erosion and sedimentation are in equilibrium, is one of the most difficult but critical topics in the field of river engineering. Data mining algorithms have been gaining more attention in this field due to their high performance and flexibility. However, an understanding of the potential for these algorithms to provide fast, cheap, and accurate predictions of hydraulic geometry is lacking. This study provides the first quantification of this potential. Using at-a-station field data, predictions of flow depth, water-surface width and longitudinal water surface slope are made using three standalone data mining techniques -, Instance-based Learning (IBK), KStar, Locally Weighted Learning (LWL) - along with four types of novel hybrid algorithms in which the standalone models are trained with Vote, Attribute Selected Classifier (ASC), Regression by Discretization (RBD), and Cross-validation Parameter Selection (CVPS) algorithms (Vote-IBK, Vote-Kstar, Vote-LWL, ASC-IBK, ASC-Kstar, ASC-LWL, RBD-IBK, RBD-Kstar, RBD-LWL, CVPSIBK, CVPS-Kstar, CVPS-LWL). Through a comparison of their predictive performance and a sensitivity analysis of the driving variables, the results reveal: (1) Shield stress was the most effective parameter in the prediction of all geometry dimensions; (2) hybrid models had a higher prediction power than standalone data mining models, empirical equations and traditional machine learning algorithms; (3) Vote-Kstar model had the highest performance in predicting depth and width, and ASC-Kstar in estimating slope, each providing very good prediction performance. Through these algorithms, the hydraulic geometry of any river can potentially be predicted accurately and with ease using just a few, readily available flow and channel parameters. Thus, the results reveal that these models have great potential for use in stable channel design in data poor catchments, especially in developing nations where technical modelling skills and understanding of the hydraulic and sediment processes occurring in the river system may be lacking.show moreshow less

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Metadaten
Document Type:Preprint
Author: Khabat Khosravi, Zohreh Sheikh KhozaniORCiD, James R. Cooper
DOI (Cite-Link):https://doi.org/10.25643/bauhaus-universitaet.4499Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20211004-44998Cite-Link
Parent Title (English):Environmental Modelling & Software
Publisher:Elsevier Science
Place of publication:Oxford
Language:English
Date of Publication (online):2021/08/13
Year of first Publication:2021
Release Date:2021/10/04
Publishing Institution:Bauhaus-Universität Weimar
Institutes and partner institutions:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2021
Issue:volume 144, October 2021, artikel 105165
Tag:Gravel-bed rivers; Hydraulic geometry
GND Keyword:Maschinelles Lernen; Künstliche Intelligenz; Data Mining
Dewey Decimal Classification:600 Technik, Medizin, angewandte Wissenschaften
BKL-Classification:56 Bauwesen
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)
Note:
This is the pre-peer reviewed version of the following article: https://www.sciencedirect.com/science/article/abs/pii/S1364815221002085 ; https://doi.org/10.1016/j.envsoft.2021.105165