Modeling open channel flow resistance with dune bedform via heuristic and nonlinear approaches

Abstract

Flow resistance in open channels with dune bedform is a substantial issue due to the influence of dunes on the hydraulic roughness, which can affect the performance of hydraulic constructions.There are a number of nonlinear approaches that have been developed to predict the roughness coefficient in alluvial channels, such as developed equations based on the mean velocity or shear stresses. However, due to the multitude of factors influencing roughness, establishing an accurate determination of the roughness coefficient is difficult. This study applies gene expression programing (GEP) and nonlinear approaches to predict the Manning's coefficient in dune bedform channels. Four different experimental data series were used for modeling. In order to develop the models, three scenarios with different input combinations were considered: scenario 1 considers only flow characteristics, scenario 2 considers flow and bedform characteristics, and scenario 3 considers flow and sediment characteristics. The results proved that GEP is capable of predicting the Manning's coefficient. It was found that for estimation of the roughness coefficient in dune bedform channels, scenario 3 performed more successfully than others. Sensitivity analysis showed that the Reynolds number plays a key role in the modeling process. Comparisons between GEP models and existing equations indicated that GEP models yield better results. Figure 7 | Scatter plot of predicted Manning's coefficient vs. observed values; flow and bedform characteristics.367 K. Roushangar et al. | Prediction of flow resistance in open channel with dune bedform

Modern problems in assessment of hydraulic resistance

“…In the structure of river systems, the dependence of morphometric characteristics on the orders of the N flow is established, with their increase from sources to the mouth, and the E3S Web of Conferences 258, 02006 (2021) UESF-2021 https://doi.org/10.1051/e3sconf/202125802006 orders themselves are determined by the water content of the river. If the runoff has not been studied, these characteristics, ultimately, can be put in accordance with the watershed area A (km2) [5].…”

“…At the present stage of development of science and technology, the need to improve the reliability of such calculations is of particular relevance due to the increasing scale of landscape ecology control and the implementation of a monitoring system for water bodies [1], including using autonomous vessels [2]. Increasingly, digital models of the seabed relief are being built [3], including using satellite altimetric observations [4], modeling of complex underwater landscapes in order to more accurately predict the erosion and destruction of hydraulic structures [5], and the degree of influence of plant roots [6][7][8] on morphodynamics is estimated. Comparison of two-dimensional and three-dimensional flow models [9][10] is carried out.…”

Flow Discharge Prediction Study Using a CFD-Based Numerical Model and Gene Expression Programming

“…The GEP is an artificial intelligence technique that utilizes key principles of genetic algorithms (GA) and genetic programming (GP) to create a calculation algorithm for forecasting a certain phenomenon. It mimics biological evolution [38]. Genetic programming is based on evolutionary principles developed for mathematical modeling.…”

Insights into the prediction capability of roughness coefficient in current ripple bedforms under varied hydraulic conditions

“…Compared with semi-empirical equations, their hybrid model enjoyed higher performance when it came to predicting the Manning and Darcy-Weisbach roughness coefficients in open channels with dune bedforms. In another investigation into the application of AI methods on the modeling characteristics of dune bedforms, Roushangar et al (2018aRoushangar et al ( , 2018b developed GEP-based equations for the prediction of the Manning roughness coefficient and relative dune height. Javadi et al (2015) found that SVM surpasses ANN in terms of predicting dune bedform dimension.…”

“…Qaderi et al (2017) used a combination of GMDH with shuffled complex evolution (SCE) and harmony search (HS) in simulating bedform dimensions, and concluded that the developed hybrid models outperform all other empirical approaches for predicting bedform dimensions. Roushangar et al (2018aRoushangar et al ( , 2018b applied extreme learning machine (ELM) in order to find the nonlinear interaction among different input variables for the prediction of coefficient of friction of overland flows. More recently, Saghebian et al (2020) presented the applicability of Gaussian process regression (GPR) for the prediction of total and bedform resistance of dune bed channels.…”