Abstract

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, CVPS-IBK, 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.

Highlights

  • Alluvial rivers form their own geometry in plan and cross-section, adjusting according to flow and sediment transport conditions

  • This state of dynamic equilibrium occurs if the sediment transport rate is approximately equal to the upstream sediment supply, meaning that channel dimensions/geometry are maintained over this time period

  • This geometry is specified in terms of river flow width, depth, velocity and slope, and understanding how these hydraulic parameters vary with other variables, such as discharge, shear stress and median bed grain-size, is of paramount importance in stable channel design

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Summary

Introduction

Alluvial rivers form their own geometry in plan and cross-section, adjusting according to flow and sediment transport conditions. Channel stability analysis involves analyzing how a channel adjusts its hydraulic geometry in response to changes in water and sediment discharge using river channel adjustment approaches (Gholami et al 2017). This geometry is specified in terms of river flow width, depth, velocity and slope, and understanding how these hydraulic parameters vary with other variables, such as discharge, shear stress and median bed grain-size, is of paramount importance in stable channel design. To design a stable geometry, accurate prediction of channel form in relation to the temporal and spatial variation in river hydraulics and sediment transport dynamics, is required

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