Optimum Design of Straight Circular Channels Incorporating Constant and Variable Roughness Scenarios: Assessment of Machine Learning Models

In this study, two machine learning (ML) models named as artificial neural network (ANN) and genetic programming (GP) were applied to design optimum canals with circular shapes. In this application, the earthwork and lining costs were considered as the objective function, while Manning’s equation was utilized as the hydraulic constraint. In this design problem, two different scenarios were considered for Manning’s coefficient: (1) constant Manning’s coefficient and (2) the experimentally proved variation of Manning’s coefficient with water depth. The defined design problem was solved for a wide range of different dimensionless variables involved to produce a large enough database. The first part of these data was used to train the ML models, while the second part was utilized to compare the performances of ANN and GP in optimum design of circular channels with those of explicit design relations available in the literature. The comparison obviously indicated that the ML models improved the accuracy of the circular channel design from 55% to 91% based on two performance evaluation criteria. Finally, application of the ML models to optimum design of circular channels demonstrates a considerable improvement over the explicit design equations available in the literature.

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

Ubiquitous flow bedforms such as ripples in rivers and coastal environments can affect transport conditions as they constitute the bed roughness elements. The roughness coefficient needs to be adequately quantified owing to its significant influence on the performance of hydraulic structures and river management. This work intended to evaluate the sensitivity and robustness of three machine learning (ML) methods, namely, Gaussian process regression (GPR), artificial neural network (ANN), and support vector machine (SVM) for the prediction of the Manning's roughness coefficient of channels with ripple bedforms. To this end, 840 experimental data points considering various hydraulic conditions were prepared. According to the obtained results, GPR was found to accurately predict the Manning's coefficient with input parameters of Reynolds number (Re), depth to width ratio (y/b), the ratio of the hydraulic radius to the median grain diameter (R/D50), and grain Froude number (). Moreover, sensitivity analysis was implemented with proposed ML approaches which indicated that the ratio of the hydraulic radius to the median grain diameter has a considerable role in modeling the Manning's coefficient in channels with ripple bedforms.

Boundary Influence and Flow Characteristics of Orisa and Ala Rivers in Kwara and Ondo States, Nigeria

In this study, the effects of boundary influence on flow characteristics of Rivers Ala and Orisa were investigated. Soil samples were collected from three points each from both rivers namely Glory Parish Area (GPA), Olusola Oke Area (OOA) and Fiwasaye; Aran-Orin Area (AOA), Rore and Omu-Aran on Rivers Ala and Orisa Respectively. A MGG/KL-DCB Portable Electromagnetic Velocity Meter was used to measure the in-situ readings of discharge and velocity at the various points. The sediments collected from the sampling points were placed in the Flume in the Hydraulic Laboratory of Civil Engineering Department, Landmark University. The Flume was then set with the measured parameter (Discharge) for each sampling point to apply the Flume to determine the Velocity for both Rivers at slopes of 0.008 to 0.056 respectively. The velocities obtained at the different slopes were inserted into Manning’s Coefficient equation to obtain the flow characteristics. The relationship between the velocity and Manning’s Coefficient was determined using the Spearman’s rank correlation coefficient. All analyses were done at P-value less than 0.05 level of significance. The velocity varied between 0.0237± 0.0004 and 0.0587± 0.0004 m/s; 0.0542± 0.0004 and 0.0701± 0.0003m/s and 0.0789± 0.0005 and 0.0172± 0.1323m/s for GPA, OOA and Fiwasaye for Ala River and between 0.0751± 0.0007 and 0.1008± 0.0006m/s; 0.0628± 0.0007 and 0.0839± 0.0004m/s and 0.0421± 0.0005 and 0.1076± 0.0004m/s for AOA, Rore and Omu-Aran for Orisa River. The results of the effects of soil boundary influence showed that the velocity was inversely proportional with the Mannings’ coefficient at all sampling points of both rivers if the geometry of the river channel is kept constant. This indicates that areas with high velocities are prone to flooding. Further studies should be carried out on more sampling points on the rivers to confirm flow characteristics of the rivers.

Identification of Manning’s Coefficient Using HEC-RAS Model: Upstream Al-Amarah Barrage

In understanding the hydraulic characteristics of river system flow, the hydraulic simulation models are essential tools. This study submits the results of the proposition of a hydraulic model in order to determine the roughness coefficient (Manning’s coefficient n) of the Tigris River along 3.5 km within the Maysan Governorate, south of Iraq. HEC-RAS software was the simulation tool used in this study. The HEC-RAS model was adopted, calibrated, and validated in adopting two sets of observed water levels. Graphical and statistical approaches were used for model calibration and verification. Results from this investigation showed that a value of Manning’s coefficient of 0.025 gave an acceptable agreement between observed and simulated values of water levels.

New Sensitivity Indices of a 2D Flood Inundation Model Using Gauss Quadrature Sampling

A new method for sensitivity analysis of water depths is presented based on a two-dimensional hydraulic model as a convenient and cost-effective alternative to Monte Carlo simulations. The method involves perturbation of the probability distribution of input variables. A relative sensitivity index is calculated for each variable, using the Gauss quadrature sampling, thus limiting the number of runs of the hydraulic model. The variable-related highest variation of the expected water depths is considered to be the most influential. The proposed method proved particularly efficient, requiring less information to describe model inputs and fewer model executions to calculate the sensitivity index. It was tested over a 45 km long reach of the Richelieu River, Canada. A 2D hydraulic model was used to solve the shallow water equations (SWE). Three input variables were considered: Flow rate, Manning’s coefficient, and topography of a shoal within the considered reach. Four flow scenarios were simulated with discharge rates of 759, 824, 936, and 1113 m 3 / s . The results show that the predicted water depths were most sensitive to the topography of the shoal, whereas the sensitivity indices of Manning’s coefficient and the flow rate were comparatively lower. These results are important for making better hydraulic models, taking into account the sensitivity analysis.

Experimental Analysis of the Influence of Aeration in the Energy Dissipation of Supercritical Channel Flows

Energy dissipation structures play an important role in flood risk management. Many variables need to be considered for the design of these structures. Aeration has been one of the more studied phenomena over the last years, due to its influence in the performance of hydraulic structures. The purpose of the work presented in this article is to experimentally characterize the effects of aeration on boundary friction in supercritical and fully turbulent flows. The physical model used to analyze the aeration effects consists of a spillway chute 6.5 m high and a stilling basin of 10 m length and 2 m high. A pump and compressor supply the water-air mixture and are controlled at the entrance by valves and flowmeters. The ensuing channel is monitored to determine the velocity profile and air concentration of the flow into the stilling basin. The average values of both variables and Manning’s coefficient along the channel are used to determine the relation between air concentration and energy dissipation by friction. A velocity increase with greater air entrainment has been found in all scenarios since friction is the main energy dissipation mechanism in open channels flow. Finally, an equation is proposed to characterize this evolution based on the results obtained.