Design of an adaptive neuro-fuzzy computing technique for predicting flow variables in a 90° sharp bend
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TLDR
The adaptive neuro-fuzzy inference system (ANFIS) is applied to predict axial velocity and flow depth in a 90° sharp bend and results indicate that ANFIS-GP-Hybrid predicts velocity best followed by flow depth.Abstract:
Investigating flow patterns in sharp bends is more essential than in mild bends due to the complex behaviour exhibited by sharp bends. Flow variable prediction in bends is among several concerns of hydraulics scientists. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is applied to predict axial velocity and flow depth in a 90° sharp bend. The experimental velocity and flow depth data for five discharge rates of 5, 7.8, 13.6, 19.1 and 25.3 L/s are used for training and testing the models. In ANFIS training, the two algorithms employed are back propagation (BP) and a hybrid of BP and least squares. In model design, the grid partitioning (GP) and sub-clustering methods are used for fuzzy inference system generation. The results indicate that ANFIS-GP-Hybrid predicts velocity best followed by flow depth.read more
Citations
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Journal ArticleDOI
Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model
Zaher Mundher Yaseen,Zaher Mundher Yaseen,Isa Ebtehaj,Hossein Bonakdari,Ravinesh C. Deo,Ali Danandeh Mehr,Wan Hanna Melini Wan Mohtar,Lamine Diop,Lamine Diop,Ahmed El-Shafie,Vijay P. Singh +10 more
TL;DR: In this paper, a hybrid adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach was proposed for monthly streamflow forecasting. But the results of the ANFIS-FFA model are compared with the classical ANFis model, which utilizes the fuzzy c-means (FCM) clustering method in the Fuzzy inference system (FIS).
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Comparative analysis of GMDH neural network based on genetic algorithm and particle swarm optimization in stable channel design
TL;DR: The genetic algorithm (GA) is employed to improve the multi-objective Pareto optimal design of group method of data handling (GMDH) neural network results and shows that GS-G MDH is more efficient than GMDH-PSO, with a high difference between predicted values.
Journal ArticleDOI
Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series
Babak Mohammadi,Nguyen Thi Thuy Linh,Nguyen Thi Thuy Linh,Quoc Bao Pham,Ali Najah Ahmed,Jana Vojteková,Yiqing Guan,Sani Isah Abba,Ahmed El-Shafie,Ahmed El-Shafie +9 more
TL;DR: The novel combination of the adaptive neuro-fuzzy inference system (ANFIS) model with the shuffled frog-leaping algorithm (SFLA) is proposed and significantly improved the forecasting accuracy and outperformed the classic ANFIS model.
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Uncertainty analysis of intelligent model of hybrid genetic algorithm and particle swarm optimization with ANFIS to predict threshold bank profile shape based on digital laser approach sensing
Azadeh Gholami,Hossein Bonakdari,Isa Ebtehaj,Majid Mohammadian,Bahram Gharabaghi,Saeed Reza Khodashenas +5 more
TL;DR: The results show that the ANFIS-PSO/GA model has less uncertainty in different hydraulic conditions of channels for predicting the vertical level of threshold bank profile and can predict satisfactorily the channel bank profiles.
Journal ArticleDOI
A methodological approach of predicting threshold channel bank profile by multi-objective evolutionary optimization of ANFIS
Azadeh Gholami,Hossein Bonakdari,Isa Ebtehaj,Bahram Gharabaghi,Saeed Reza Khodashenas,Seyed Hamed Ashraf Talesh,Ali Jamali +6 more
TL;DR: This study introduces a new hybrid method that combines an adaptive neuro-fuzzy inference system (ANFIS), Differential Evolution (DE) algorithm and Singular Value Decomposition (SVD) to predict the bank profile of a threshold channel.
References
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ANFIS: adaptive-network-based fuzzy inference system
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Journal ArticleDOI
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Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition
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A hybrid model coupled with singular spectrum analysis for daily rainfall prediction
Kwok Wing Chau,C.L. Wu +1 more
TL;DR: A hybrid model integrating artificial neural networks and support vector regression was developed for daily rainfall prediction and exhibited considerable accuracy in rainfall forecasting.