Performance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy models

Sharafati, Ahmad; Haghbin, Masoud; Tiwari, N. K.; Bhagat, Suraj Kumar; Al‐Ansari, Nadhir; Chau, Kwok-wing; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1080/19942060.2021.1893224

Cited by 19 publications

(11 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To get rid of the weaknesses of NNs and FIS, ANFIS techniques have already been successfully utilized as a reliable estimating tool for OTE, sediment trapping efficiency, a discharge correction factor of Parshall flume and plunging hollow jet penetration depth, and many more problems related to the discipline of water resources, environmental, etc. of civil engineering (Tiwari et al 2019;Saran & Tiwari 2020;Sharafati et al 2021;). The ANFIS technique creates an association between input and output variables by applying the linguistic terminologies.…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Estimating gabion weir oxygen transfer with data mining

Tiwari

Luxmi

Ranjan

2022

Water Quality Research Journal

Self Cite

View full text Add to dashboard Cite

Conventionally, impermeable weirs are employed for retaining, measuring, and regulating the water in the river. Still now, alternative devices are more predominantly in vogue, which are made of locally available materials called gabion weirs chosen because the latter can better fulfill ecological needs due to their porous nature. Dissolved oxygen (D.O.) is one of the significant determinants for assessing the character of water bodies. This study mainly focuses on improving the estimation of the gabion oxygen transfer efficiency (OTE20) to enhance its efficacy. The backpropagation neural network (BPNN), adaptive neuro-fuzzy inference system (ANFIS), and multi-variant linear and nonlinear regression (MVLR and MVNLR) are developed with experimental data to estimate the OTE20 and their results are compared. In terms of statistical metrics, the BPNN has proved to be the best-performing model. At the same time, triangular membership function (mf)-based ANFIS is the second-best performing model. Nevertheless, other applied mf-based ANFIS, MVLR, and MVNLR are giving a comparable performance. Input variable discharge per unit width (q) is the most crucial parameter in the computation of the OTE20, followed by the gabion mean size (d50). Major challenges are found in computing porosity of the gabion materials and optimal parameters of proposed data mining techniques.

show abstract

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Estimating gabion weir oxygen transfer with data mining

Tiwari

Luxmi

Ranjan

2022

Water Quality Research Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…They have been widely applied to find solutions for different complex problems in computer science and engineering. They are essentially utilized to obtain the optimal solution by getting different optimal val-ues for producing a candidate value to completely solve the underlying issue [7], [8]. Generally, meta-heuristic optimization methods consider the optimal value by decreasing or increasing the objective function to get the optimal decision [9].…”

Section: Introductionmentioning

confidence: 99%

Grasshopper Optimization Algorithm With Crossover Operators for Feature Selection and Solving Engineering Problems

Ewees

Gaheen

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

Feature selection (FS) is an irreplaceable phase that makes data mining more efficient. It effectively enhances the implementation and decreases the computational problem of learning models. The comprehensive and greedy algorithms are not suitable for the present growing number of features when detecting the optimal subset. Thus, swarm intelligence algorithms (SI) are becoming more common in dealing with FS problems. The grasshopper optimizer algorithm (GOA) represents a new SI; it showed good performance in different fields. Another promising nature-inspired algorithm is a salp swarm algorithm, denoted as SSA, an SI used to tackle optimization issues. In this paper, two phases are applied to propose cSG method. In this method, the crossover operators are used to maintain the population of the SSA then the improved SSA is used as a local search to boost the exploration phase of the GOA. Subsequently, this improvement prevents the cSG from premature convergence, high computation time, and being trapped in local minimum. To confirm the effectiveness of proposed cSG method, it is evaluated in different optimizations problems. Eventually, the obtained results are compared to a number of well-known algorithms over global optimization, feature selection datasets, and six real-engineering problems. Experimental results point out that the cSG is superior in solving different optimization problems due to the integration of crossover operators and SSA which enhances its performance and flexibility.

show abstract

“…The trapping effectiveness of vortex tube ejectors has been investigated using physical and numerical models (Atkinson 1994). Singh et al (2021) employed ANFIS, GPR, M5P, RF, and MLR models to predict the trapping efficiency of the vortex tube sediment ejector, while Sharafati et al (2021) used hybrid models of neuro-fuzzy to estimate tunnel sediment ejector efficiency. Asareh & Kamanbedast (2018) did the experimental investigation for a vortex tube orifice for sediment trapping.…”

Section: Introductionmentioning

confidence: 99%

Applicability of several machine learning models in estimation of vortex tube trapping efficiency

2022

View full text Add to dashboard Cite

A vortex tube ejector comprises a tube with a slitted crown that lies flushed across the entire width of the channel bed surface. The bed and suspended loads are ejected with minimal flushing water through the slit with the same efficacy as any other alternative extractors. The whirling flow phenomena through the vortex duct are very complex, so ordinary classical models have resulted contrary to required design guidelines. So, machine learning (ML) models; artificial neural network (ANN), deep neural network (DNN), gradient boosting machine (GBM), stacked ensemble (SE), and adaptive neuro-fuzzy inference system (ANFIS) are used to predict vortex tube trapping efficiency (VTE). The input dataset takes the size of the sediment (Sz), intensity (I) of the sediment, the ratio of slit thickness to diameter of the tube (th/dia), and extraction ratio (Extro) while trapping efficiency (T.E.) is taken as output. Based on statistical assessments, GBM appears to be better than all-proposed models. However, other proposed ML models give comparable performance. The classical models, multivariate linear, and nonlinear regression techniques are also comparatively providing good results. According to sensitivity analyses, the extraction ratio is the most relevant parameter in evaluating the VTE.

show abstract

Performance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy models

Cited by 19 publications

References 33 publications

Estimating gabion weir oxygen transfer with data mining

Estimating gabion weir oxygen transfer with data mining

Grasshopper Optimization Algorithm With Crossover Operators for Feature Selection and Solving Engineering Problems

Applicability of several machine learning models in estimation of vortex tube trapping efficiency

Contact Info

Product

Resources

About