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

• Published in: Environmental modelling & software : with environment data news Vol. 144; p. 105165
• Main Authors: Khosravi, Khabat, Sheikh Khozani, Zohreh, Cooper, James R.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2021; Elsevier Science Ltd
• Subjects: Algorithms; Artificial intelligence; Catchments; Classifiers (sedimentation); computer software; Data mining; Developing countries; Empirical equations; Geometry; Gravel; Gravel-bed rivers; Hydraulic geometry; Hydraulics; LDCs; Learning algorithms; Machine learning; Mathematical models; Modelling; Parameters; Performance prediction; prediction; Predictions; River engineering; Rivers; sediments; Sensitivity analysis; Stable channels; Water depth; Modelling; Gravel-bed rivers; Data mining; Artificial intelligence; Hydraulic geometry; Machine learning
• ISSN: 1364-8152; 1873-6726; 1873-6726
• DOI: 10.1016/j.envsoft.2021.105165

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. •The data mining algorithms is provided to predict stable channel geometry.•Predictions were made using standalone and novel hybrid datamining models.•A comparison was made of the predictive power of these data-driven models, and a sensitivity analysis of three driving variables was performed.•The developed hybrid models had a higher performance than empirical and traditional models extended by other researchers.