This study aims to evaluate the learning ability and performance of five meta-heuristic optimization algorithms in training forward and recurrent fuzzy-based machine learning models, such as adaptive neuro-fuzzy inference system (ANFIS) and RANFIS (recurrent ANFIS), to predict hydraulic jump characteristics, i.e., downstream flow depth (h2) and jump length (Lj). To meet this end, the firefly algorithm (FA), particle swarm algorithm (PSO), whale optimization algorithm (WOA), genetic algorithm (GA), and moth-flame optimization algorithm (MFO) are embedded with the fuzzy-based models, which represent the main contribution of this study. The analysis of the results of predicting hydraulic jump characteristics shows that the embedded ANFIS and RANFIS models are more accurate than the empirical relations proposed by the previous researchers. Comparing the performance of the embedded RANFISs and ANFISs methods in predicting Lj represents the superiority of the RANFIS models to the ANFISs. The results of the sensitivity analysis show that among the input independent parameters, flow discharge (Q) is the most important factor in predicting downstream flow depth in weak, oscillating, and steady hydraulic jumps (1.7< Froude number< 9), while the upstream flow depth (h1) is more important than the other input parameters in strong hydraulic jumps (Froude number> 9).

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Embedded fuzzy-based models in hydraulic jump prediction

This article aims to provide a comprehensive appraisal on the current status of three-dimensional (3D) numerical modeling of local scour around instream hydraulic structures. Many computer models have been developed or are in development to simulate riverbed morphology. Local scour, however, is a special category that requires in general a 3D model based on some averaged/filtered form of the Navier-Stokes (NS) equations and a 3D sediment transport model. Only limited number of 3D scour codes are available for practical usage; there is also a lack of literature on the limitations of the models in capturing the scour-producing flow and the sediment transport solvers embedded within. For 3D local scour modeling, the main intrinsic uncertainty lies primarily in the sediment part. In this article, the basic theory and the state of the art of current 3D local scour models are reviewed, focusing primarily on the sediment solver, and future research needs are discussed. 

Three-Dimensional Numerical Modeling of Local Scour: A State-of-the-Art Review and Perspective

In this study, for the first time, the scour pattern around cross-vane structures with I, U, and J shapes in bending channels is simulated by a new artificial intelligence method called “generalized structures group method of data handling” (GSGMDH). Compared to the group method of data handling (GMDH), the GSGMDH method has more flexibility and exactness so that nodes can derive inputs from non-adjacent layers. Initially, using the input parameters, six different models are defined for each of the GMDH and GSGMDH methods. Also, the superior model forecasts objective function values with acceptable accuracy. For example, the correlation coefficient (R), the scatter index (SI), and the Nash-Sutcliffe efficiency coefficient (NSC) for the GSGMDH superior model in the training mode are approximated to be 0.913, 0.214, and 0.800, respectively. Based on the sensitivity analysis, the shape factor of cross-vane structures, the ratio of the difference between the upstream and downstream flow depths to the height of the structure, and the densimetric Froude number are introduced as the most effective input parameters. Subsequently, for the superior model, a relationship is proposed for the estimation and calculation of the scour depth. Finally, a partial derivative sensitivity analysis (PDSA) is conducted for the superior model.

Generalized structure of group method of data handling to prognosticate scour around various cross-vane structures

https://link.springer.com/content/pdf/10.1007/s12046-021-01758-2.pdf

Demonstration of structure-from-motion (SfM) and multi-view stereo (MVS) close range photogrammetry technique for scour hole analysis

Scour downstream of stepped-baffle weirs in wide rivers

Due to the vital role of rivers and canals, the protection of their banks and beds is critically important. There are various methods for protecting beds and banks of rivers and canals in which “cross-vane structures” is one of them. In this paper, the scour hole depth at the downstream of cross-vane structures with different shapes (i.e., J, I, U, and W) is simulated utilizing a modern artificial intelligence method entitled “Outlier Robust Extreme Learning Machine (ORELM)”. The observational data are divided into two groups: training (70%) and test (30%). After that, the most optimal activation function for simulating the scour depth at the downstream of cross-vane structures is selected. Then, using the input parameters including the ratio of the structure length to the channel width (b/B), the densimetric Froude number (Fd), the ratio of the difference between the downstream and upstream depths to the structure height (Δy/hst) and the structure shape factor $$ \left( \phi \right) $$
 , eleven different ORELM models are developed for estimating the scour depth. Subsequently, the suitable model and also the most effective input parameters are identified through the conduction of an uncertainty analysis. The suitable model simulates the scour values by the dimensionless parameters b/B, Fd, Δy/hst. For this model, the values of the correlation coefficient (R), Variance accounted for (VAF) and the Nash-Sutcliffe efficiency (NSC) for the suitable model in the test mode are obtained 0.956, 91.378 and 0.908, respectively. Also, the dimensionless parameters b/B, Δy/hst. are detected as the most effective input parameters. Furthermore, the results of the suitable model are compared with the extreme learning machine model and it is concluded that the ORELM model is more accurate. Moreover, an uncertainty analysis exhibits that the ORELM model has an overestimated performance. Besides, a partial derivative sensitivity analysis (PDSA) model is performed for the suitable model.

Estimation of scour depth around cross-vane structures using a novel non-tuned high-accuracy machine learning approach

In this paper, the hydraulic jump length on a slope rough floor is simulated through the extreme learning machine (ELM). Then, the parameters affecting the hydraulic jump on the slope rough bed are detected. After that, five different ELM model are developed so as to determine the influenced factor. Next, the results obtained from different ELM models are analyzed. The comparison of the results with the experimental data proves the acceptable accuracy of the mentioned numerical models. Regarding the results from the numerical method, the superior ELM model estimates the hydraulic jump length in terms of the flow Froude number, the ratio of bed roughness, the ratio of sequent depths and bed slope. The values of the root mean square error (RMSE), mean absolute percent error (MAPE), scatter index (SI) and correlation coefficient (R) for the superior model are respectively obtained 0.657, 3.507, 0.052 and 0.985. Based on the simulation, the flow Froude number at upstream is introduced as the most effective parameter in predicting the jump length on the sloping rough floor.

https://arww.razi.ac.ir/article_1349_eff46572cbab57c22442ce84af7ad63d.pdf

Amir Hosein Azimi

Papers

Estimation of scour depth around cross-vane structures using a novel non-tuned high-accuracy machine learning approach

Simulation of hydraulic jump length on sloping coarse floors adopting extreme learning machine