GEP- and MLR-based equations for stable channel analysis

• Published in: Journal of hydroinformatics Vol. 23; no. 6; pp. 1247 - 1270
• Main Authors: Harun, Mohd Afiq, Ab. Ghani, Aminuddin, Mohammadpour, Reza, Chan, Ngai Weng
• Format: Journal Article
• Language: English
• Published: London IWA Publishing 01.11.2021
• Subjects: Accuracy; Bed material; Chromosomes; Depth; Errors; Gene expression; Geometric accuracy; Geometry; Geomorphology; gep; Hydraulic geometry; Hydraulics; Learning algorithms; Machine learning; Methods; mlr; Model accuracy; Parameters; Predictions; Researchers; River discharge; River flow; Rivers; Sediment transport; Sedimentation & deposition; Soil erosion; stable channel; Stable channels; Statistical analysis; Variables; Water discharge; Width
• ISSN: 1464-7141; 1465-1734
• DOI: 10.2166/hydro.2021.047

Abstract For decades, research on stable channel hydraulic geometry was based on the following parameters: river discharge, dimensionless discharge, the median size of bed material and the slope. Although significant research has been conducted in this area, including applied machine learning to increase the geometry model prediction accuracy, there has been no remarkable improvement as the variables used to describe the geometry relationship remain the same. The novelty of this study is demonstrated by the parameters used in the stable channel geometry equations that outperform the existing equation's accuracy. In this research, sediment transport parameters are introduced and analysed by applying the multiple linear regression (MLR) and gene expression programming (GEP) methods. The new equation of the width, depth and bed slope can give much-improved results in efficiency and lower errors. Furthermore, a new parameter B/y is introduced in this study to solve the restriction issue, either in width or depth prediction. The results from MLR and GEP show that in addition to the existing hydraulic geometry parameter, the B/y parameter is also able to give high accuracy results for width and depth predictions. Both calibration and validation for the B/y parameter yield high R2 and NSE values with low mean squared errors and mean absolute errors.