Uncertainty analysis in bed load transport prediction of gravel bed rivers by ANN and ANFIS

Riahi-Madvar, Hossien; Seifi, Akram

doi:10.1007/s12517-018-3968-6

Cited by 41 publications

(12 citation statements)

References 54 publications

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“…Fuzzy inference system is a rule-based structure with three conceptual mechanisms, including a rule-based structure, a database of models and an inference system. The first component is based on if-then rules, while the second defines the membership function, and the third is the combination of the rules and producing the results [79], [80]. An automatic procedure for the optimization of membership function features and adjusting parameters is needed [79], [81], [82].…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

“…In this paper, eight statistical evaluation criteria, including the root mean square error (RMSE), the coefficient of determination (R 2 ), mean absolute error (MAE), the Nash-Sutcliffe Efficiency (NSE), index of agreement (d), persistence index (PI), confidence index (CI) and relative absolute error (RAE) were utilized for model evaluation. The explanation of these criteria was presented elsewhere [80], [91], [92]. Also, visualize approaches, including scatter plot, Taylor diagram [93], ellipse confidence bounds, and the probability distribution of revised discrepancy ratio (RDR) are used to evaluate the model results.…”

Section: E Evaluation Criteriamentioning

confidence: 99%

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Improvements in the Explicit Estimation of Pollutant Dispersion Coefficient in Rivers by Subset Selection of Maximum Dissimilarity Hybridized With ANFIS-Firefly Algorithm (FFA)

et al. 2020

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In this paper, a new hybrid model is proposed using Subset Selection by Maximum Dissimilarity (SSMD) and adaptive neuro-fuzzy inference system (ANFIS) hybridized with the firefly algorithm (FFA) to predict the longitudinal dispersion coefficient (K x). The proposed framework (ANFIS-FFA), combines the specific structures and strengths of both ANFIS and FFA approaches. The FFA is used to derive the optimum ANFIS parameters. The K x data set includes 503 cross-sectional data point from small to large rivers. For pre-processing of the data set, the SSMD method is used, which is superior to the classical trial and error method. The database covers a wide range of river width (0.2-867m), and depths (0.034-19.9 m). Fifteen different combinations of river width (B), depth (H), flow velocity (U) and shear velocity (U *) are implemented as inputs to create fifteen estimative models. The output of the ANFIS-FFA model is compared with the ANFIS and previously published equations to check the performance of the proposed model. The results show that the highest accuracy is attained by the M1 model, with all geometric and hydrodynamic parameters as input variables in comparison with ANFIS and previous equations. The R 2 value, RMSE, MAE and NSE for ANFIS-FFA model are 0.67, 113.14 m 2 /s, 48 m 2 /s, and 0.63 for proposed dimensional model, and 0.35, 874.5, 520.8, and 0.1 in non-dimensional ANFIS-FFA model, respectively. These values were 0.37, 463.34 m 2 /s, 85.69 m 2 /s, and −5.19 for dimensional ANFIS model, and 0.11, 3269.88, 1932.09 and −11.54 for non-dimensional ANFIS model, respectively. Overall, hybridization caused 81%, 75%, 76% improvement in R 2 , RMSE and MAE. In another contribution of the paper, by using the matrix form of developed ANFIS-FFA optimized parameters, a novel explicit calculation procedure for estimation of K x is derived. Based on the results, the proposed ANFIS-FFA model exhibits significant improvements than the classical ANFIS and highlights that optimizing by nature-inspired optimization algorithms plays a critical role in strengthening the ANFIS estimations generality. INDEX TERMS Longitudinal dispersion coefficient, ANFIS-FFA, maximum dissimilarity method, natural rivers, adaptive neuro-fuzzy inference system.

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Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Section: E Evaluation Criteriamentioning

confidence: 99%

Improvements in the Explicit Estimation of Pollutant Dispersion Coefficient in Rivers by Subset Selection of Maximum Dissimilarity Hybridized With ANFIS-Firefly Algorithm (FFA)

et al. 2020

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“…Soft computing techniques work as a black-box model in which the process of a phenomenon is not considered in modeling, and the governing relationship is just based on the input-output data without providing explicit estimation equation [32,33]. The ANN is the most widely used method in water resources modeling [4,20,34,35,36]. Multilayer perceptron (MLP) with a feed-forward back-propagation algorithm is one of the most popular types of ANN, which was used for forecasting hydrological variables such as drought, streamflow, evaporation, etc [37,38,39,40,41,42].…”

Section: Introductionmentioning

confidence: 99%

Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

et al. 2020

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In this paper, a new hybrid model is developed to improve the accuracy in the prediction of the longitudinal dispersion coefficient (Kx) and the derivation of novel optimized explicit equations for natural streams. For this purpose, an artificial neural network (ANN) is hybridized with particle swarm optimization (PSO) and cat swarm optimization (CSO) algorithms. The CSO and PSO are used to find the optimum values of biases and weights in ANN structure and formulate the results as novel explicit predictive equations than the classical black-box methods. The hydraulic parameters of the natural stream and some geometric parameters were utilized for the model developments. Eight different input combinations are used as the input vectors to ANN, ANN-PSO, and ANN-CSO models, whereas the dispersion coefficient (Kx) is the target model output. The developed models are trained and tested bya comprehensive reference data sets measured on streams in the United States, that were used previously by Tayfur and Singh (2005) in ANN models. The main aims, novelty, and contributions of the present study are 1) improving the accuracy of classical ANN-based Kx predictions by hybridizing with CSO and PSO. 2) Performing sensitive analysis of ANN, ANN-CSO, and ANN-PSO based on input combinations 3) derivation of novel explicit optimized ANN-CSO, ANN-PSO, equations for predicting Kx rather than the classical ANN black-box methods. The results depicted that the highest accuracy and superiority were attained by the ANN-PSO model, with input variables of B, H, U, U*, followed by ANN-CSO and ANN. By using the optimized trained black box ANN models, two novel explicit predictive equations are derived, and their results are compared with the empirical equations. Comparative assessments confirmed significant improvements in the hybrid equations' results than the classical ANN and previously published equations. The developed novel equations can be used to estimate the Kx in one-dimensional pollutant transfer models that are essential for the pollution studies in environmental river engineering practices.

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“…Neural networks (NNs) are stimulated by biological neurons to accomplish brain-like calculations by enormously direct connective artificial neurons (Riahi-Madvar & Seifi, 2018). A remarkable development in the importance of this computational framework has happened since an accurately laborious theoretical background for NNs, i.e.…”

Section: Multi-layer Perceptronmentioning

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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Uncertainty analysis in bed load transport prediction of gravel bed rivers by ANN and ANFIS

Cited by 41 publications

References 54 publications

Improvements in the Explicit Estimation of Pollutant Dispersion Coefficient in Rivers by Subset Selection of Maximum Dissimilarity Hybridized With ANFIS-Firefly Algorithm (FFA)

Improvements in the Explicit Estimation of Pollutant Dispersion Coefficient in Rivers by Subset Selection of Maximum Dissimilarity Hybridized With ANFIS-Firefly Algorithm (FFA)

Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

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

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