Title: Application of machine learning models to side-weir discharge coefficient estimations in trapezoidal and rectangular open channels
Book: Water Resource Modeling and Computational Technologies
Series: Current Directions in Water Scarcity Research (Volume 7)
Year: 2022
Chapter: 26
Pages: 467-479
Publisher: Elsevier
DOI: 10.1016/B978-0-323-91910-4.00026-1
Abstract: Side weirs are a type of hydraulic structures used to divert or measure flows in open channels. In this chapter, two machine learning (ML) methods, named artificial neural network (ANN) and multigene genetic programming (MGGP), were applied to develop ML-based methods for estimating discharge coefficients of side weirs installed in rectangular and trapezoidal canals for subcritical flow. According to the literature, the latter ML method is the first time to be utilized for this purpose. For the comparative analysis, a large experimental dataset was collected from previous studies available in the literature. Unlike ANN, MGGP provides an explicit equation that incorporates all parameters that affect discharge coefficients of a side weir. Although the results indicate that ANN performs slightly better than the MGGP-based model for this application, the explicit estimation model developed by MGGP can be further used in numerical modeling in river engineering and open channel designs.
Keywords: Open channel, Side weir, Coefficient discharge, Artificial neural network, Multigene genetic programming, Machine learning methods