NF-GMDH-Based self-organized systems to predict bridge pier scour depth under debris flow effects

Najafzadeh, Mohammad; Saberi-Movahed, Farid; Sarkamaryan, Saeed

doi:10.1080/1064119x.2017.1355944

Cited by 96 publications

(33 citation statements)

References 44 publications

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“…There are also many research studies on GMDH. Some studies have combined GMDH with other algorithms to improve the prediction accuracy, such as in a certain past paper [32], in which an neuro-fuzzy group method of data handling (NF-GMDH) network was combined with particle swarm optimization (PSO), the gravitational search algorithm (GSA), and a genetic algorithm (GA) to improve the forecasting accuracy. There are also studies on improving the structure of GMDH to better adapt to the characteristics of the model, such as in a past paper [33], in which five diversity metrics were used as external criteria to construct a new type of GMDH forecasting model called diversity group method of data handling (D-GMDH).…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Based on Elastic Net Improved GMDH and Difference Degree Weighting Optimization

et al. 2018

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As objects of load prediction are becoming increasingly diversified and complicated, it is extremely important to improve the accuracy of load forecasting under complex systems. When using the group method of data handling (GMDH), it is easy for the load forecasting to suffer from overfitting and be unable to deal with multicollinearity under complex systems. To solve this problem, this paper proposes a GMDH algorithm based on elastic net regression, that is, group method of data handling based on elastic net (EN-GMDH), as a short-term load forecasting model. The algorithm uses an elastic net to compress and punish the coefficients of the Kolmogorov–Gabor (K–G) polynomial and select variables. Meanwhile, based on the difference degree of historical data, this paper carries out variable weight processing on the input data of load forecasting, so as to solve the impact brought by the abrupt change of load law. Ten characteristic variables, including meteorological factors, meteorological accumulation factors, and holiday factors, are taken as input variables. Then, EN-GMDH is used to establish the relationship between the characteristic variables and the load, and a short-term load forecasting model is established. The results demonstrate that, compared with other algorithms, the evaluation index of EN-GMDH is significantly better than that of the rest algorithm models in short-term load forecasting, and the accuracy of prediction is obviously improved.

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Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Based on Elastic Net Improved GMDH and Difference Degree Weighting Optimization

et al. 2018

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“…In Table 1 some of the most important of these equations have been presented. Recently, several studies used artificial intelligence for modeling complicated hydraulic phenomenon in water resource management and civil-environmental engineering (Chang, Azamathulla, Zakaria, & Ab Ghani, 2012;Dehghani, Saghafian, Nasiri Saleh, Farokhnia, & Noori, 2014;Dehghani et al, 2019;Juahir, Zain, Aris, Yusoff, & Mokhtar, 2010;Khan, Azamathulla, & Tufail, 2012;Najah, El-Shafie, Karim, & El-Shafie, 2013;Najafzadeh, Saberi-Movahed, & Sarkamaryan, 2018;Pandey, Zakwan, Sharma, & Ahmad, 2019;Seifi & Riahi-Madvar, 2019). In this study the MATLAB 2014 environment is used for developing soft computing models and SPSS 8 is used for linear and non-linear model developments.…”

Section: Theoretical and Dimensional Analysismentioning

confidence: 99%

Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry

Riahi-Madvar

Dehghani

Seifi

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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The main aims and contributions of the present paper are to use new soft computing methods for the simulation of scour geometry (depth/height and locations) in a comparative framework. Five models were used for the prediction of the dimension and location of the scour pit. The five developed models in this study are multilayer perceptron (MLP) neural network, radial basis functions (RBF) neural network, adaptive neuro fuzzy inference systems (ANFIS), multiple linear regression (MLR), and multiple non-linear regression (MNLR) in comparison with empirical equations. Four non-dimensional geometry parameters of scour hole shape are predicted by these models including the maximum scour depth (S), the distance of S from the weir (XS), the maximum height of downstream deposited sediments (hd), and distance of hd from the weir (XD). The best results over train data derived for XS/Z and hd/Z by the MLP model with R 2 are 0.95 and 0.96 respectively; the best predictions for S/Z and XD/Z are from the ANFIS model with R 2 0.91 and 0.96 respectively. The results indicate that the application of MLP and ANFIS results in the accurate prediction of scour geometry for the designing of stable grade control structures in alluvial irrigation channels.

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“…After collision, velocity of the bodies in each group is obtained using Eqs. (16) and (17). The velocity of each moving CB after the collision is calculated by Eq.…”

Section: Fig 2 Collision Of Bodiesmentioning

confidence: 99%

“…The results showed that the most important parameters affecting relative scour depth (d s /y) included ratio of pier width to flow depth (D/y) and ratio of pier length to flow depth (L/y). In addition, other researchers have performed a number of studies in this field as well, such as references [10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Meta-heuristic Optimization Algorithms for Predicting the Scouring Depth Around Bridge Piers

Hassanzadeh¹,

Jafari-Bavil-Olyaei²,

Taghi-Aalami³

et al. 2019

Period. Polytech. Civil Eng.

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An accurate estimation of bridge pier scour has been considered as one of the important parameters in designing of bridges. However, due to the numerous involved parameters and convolution of this phenomenon, many existing approaches cannot predict scour depth with an acceptable accuracy. Obtained results from the empirical relationships show that these relationships have low accuracy in determining the maximum scour depth and they need a high safety factor for many cases, which leads to uneconomic designs of bridges. To cover these disadvantages, three new models are provided to estimate the bridge pier scour using an adaptive networkbased fuzzy inference system. The parameters of the system are optimized by using the colliding bodies optimization, enhanced colliding bodies optimization and vibrating particles system methods. To evaluate the efficiency of the proposed methods, their results were compared with those of simple adaptive network-based fuzzy inference system and its improved versions by using the particle swarm optimization and genetic algorithm as well as the empirical equations. Comparison of results showed that the new vibrating particles system based algorithm could find better results than other two ones. In addition, comparison of the results obtained by the proposed methods with those of the empirical relations confirmed the high performance of the new methods.

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NF-GMDH-Based self-organized systems to predict bridge pier scour depth under debris flow effects

Cited by 96 publications

References 44 publications

Short-Term Load Forecasting Based on Elastic Net Improved GMDH and Difference Degree Weighting Optimization

Short-Term Load Forecasting Based on Elastic Net Improved GMDH and Difference Degree Weighting Optimization

Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry

Meta-heuristic Optimization Algorithms for Predicting the Scouring Depth Around Bridge Piers

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