A methodological approach of predicting threshold channel bank profile by multi-objective evolutionary optimization of ANFIS

Gholami, Azadeh; Bonakdari, Hossein; Ebtehaj, Isa; Gharabaghi, Bahram; Khodashenas, Saeed Reza; Talesh, Seyed Hamed Ashraf; Jamali, Ali

doi:10.1016/j.enggeo.2018.03.030

Cited by 44 publications

(15 citation statements)

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“…Following Cao and Knight’s [ 28 ] brief study, no other study has been based on the entropy concept to predict the

and hence the bank profile shape of stable channels. Gholami et al [ 30 , 31 , 32 , 33 , 34 ] assessed the ability of different artificial intelligence (AI) methods in the estimation of bank profile shapes of threshold channels. They referred to high efficiency in these methods in estimation and the necessity of further researches about on forming stable shape of bank profiles.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Comprehensive Evaluation Method for Estimating the Bank Profile Shape and Dimensions of Stable Channels Using the Maximum Entropy Principle

Bonakdari

Gholami

Mosavi

et al. 2020

Entropy

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This paper presents an extensive and practical study of the estimation of stable channel bank shape and dimensions using the maximum entropy principle. The transverse slope (St) distribution of threshold channel bank cross-sections satisfies the properties of the probability space. The entropy of St is subject to two constraint conditions, and the principle of maximum entropy must be applied to find the least biased probability distribution. Accordingly, the Lagrange multiplier (λ) as a critical parameter in the entropy equation is calculated numerically based on the maximum entropy principle. The main goal of the present paper is the investigation of the hydraulic parameters influence governing the mean transverse slope [...]

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“…Following Cao and Knight’s [ 28 ] brief study, no other study has been based on the entropy concept to predict the

Section: Literature Reviewmentioning

confidence: 99%

A Novel Comprehensive Evaluation Method for Estimating the Bank Profile Shape and Dimensions of Stable Channels Using the Maximum Entropy Principle

Bonakdari

Gholami

Mosavi

et al. 2020

Entropy

Self Cite

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“…where x*, y* and T* are dimensionless values of x, y, and T. Gholami et al (2018a) applied DE and SVD model in hybrid with ANFIS model. They expressed the rules of these methods extensively and detailed.…”

Section: Experimental Workmentioning

confidence: 99%

“…Coefficients set that should be optimized in this function to model the thresholds channel bank profile with the least amount of errors are as {c, σ}. An overview of the algorithm DE and operators in this algorithm was expressed in Gholami et al (2018a).…”

Section: Application Of De In Optimize Design Of Anfismentioning

confidence: 99%

“…This method to solve the least-square problem that singularity is there in the governing equations is used ). SVD of the matrix P ∈ R M×S is consist of the factorization of it and produces three different matrices including a diagonal matrix that has nonnegative components to prevent of singular values Q ∈ R S×S , a column-orthogonal matrix U ∈ R M×S and orthogonal matrix V ∈ R S×S is (refer to Gholami et al 2018a for more explanations).…”

Section: Application Of Svd In Anfis Designmentioning

confidence: 99%

“…Their results showed the ANFIS-PSO/GA has an acceptable agreement with observed data with the relative error index of 0.1557 in prediction of vertical boundary level of stable channel shape profile. Gholami et al (2018a) evaluated the ability of ANFIS-DE/SVD in prediction of the vertical boundary level of the stable channel. They referred to the high accuracy of this type of evolutionary algorithms combined with ANFIS model.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing bank profile shape of alluvial stable channels using robust optimization and evolutionary ANFIS methods

et al. 2019

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In natural rivers and artificial channels in addition to the channel dimensions (widening, reduction in slope and depth in the channel banks), formed shape profile in the case that the sediment on the banks with no movement (thresholds state) is of considerable importance for engineers. To determine the bank shape profiles, various theoretical, empirical and statistical relations have been provided based on physical and numerical models by different researchers. In this study, a simple model of adaptive neuro-fuzzy inference systems (ANFIS) is combined with two algorithms of differential evolution (DE) and singular value decomposition value (SVD) and the performance of these models to predict the stable shape profiles of the channels is evaluated and compared. In this paper, the main goal is to assess extensively the effect of hybrid models based on optimized algorithms (ANFIS-DE) and multi-objective evolutionary algorithm (ANFIS-DE/SVD) in improvement of performance of ANFIS and ANFIS-DE models, respectively. Accordingly, the results assessment show that all three ANFIS, ANFIS-DE and ANFIS-DE/SVD models are perfectly able to predict shape profiles in accordance with the observed profiles for the threshold channels. Using optimized and evolutionary algorithms has a positive impact on the performance of simple model of ANFIS. As compared to the simple ANFIS model, ANFIS-DE approximately 10.1% and ANFIS-DE/SVD model 7.2% is improved compared to the ANFIS-DE model. The accuracy of ANFIS-DE/SVD model showed better performance as well about 18.6% compared to the simple ANFIS model. Therefore, it can be said that not only DE optimization algorithms have a significant impact on increasing the performance of a simple ANFIS model but also using evolutionary algorithms (ANFIS-DE/SVD) reduce the ANFIS-DE model error accordingly. Polynomial equations of bank profiles proposed by hybrid ANFIS models in the present study can be used in design and implementation of cross section of stable channels.

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Modelling Stable Alluvial River Profiles Using Back Propagation-Based Multilayer Neural Networks

Bonakdari

Gholami

2019

Advances in Intelligent Systems and Computing

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A methodological approach of predicting threshold channel bank profile by multi-objective evolutionary optimization of ANFIS

Cited by 44 publications

References 39 publications

A Novel Comprehensive Evaluation Method for Estimating the Bank Profile Shape and Dimensions of Stable Channels Using the Maximum Entropy Principle

A Novel Comprehensive Evaluation Method for Estimating the Bank Profile Shape and Dimensions of Stable Channels Using the Maximum Entropy Principle

Analyzing bank profile shape of alluvial stable channels using robust optimization and evolutionary ANFIS methods

Modelling Stable Alluvial River Profiles Using Back Propagation-Based Multilayer Neural Networks

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