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Hybrid ANFIS–PSO approach for predicting optimum parameters of a protective spur dike

Hossein Basser, Hojat Karami, Shahaboddin Shamshirband, Shatirah Akib, Mohsen Amirmojahedi, Rodina Ahmad, Afshin Jahangirzadeh, Hossein Javidnia
2015 Applied Soft Computing  
In prediction phase, a novel hybrid approach was developed, combining adaptive-networkbased fuzzy inference system and particle swarm optimization (ANFIS-PSO) to predict protective spur dike's parameters  ...  In this study a new approach was proposed to determine optimum parameters of a protective spur dike to mitigate scouring depth amount around existing main spur dikes.  ...  Also authors would like to thank the porous media laboratory of Amirkabir University of Technology for the experimental facilities.  ... 
doi:10.1016/j.asoc.2015.02.011 fatcat:3t3mngtvjzfwnk4gz6ilxjcfpi

Determination of industrial energy demand in Turkey using MLR, ANFIS and PSO-ANFIS

2021 Journal of Artificial Intelligence and Systems  
Consequently, parameters tuned PSO-ANFIS is able to predict the industrial energy demand in Turkey with high accuracy.  ...  The coefficient of determination (R 2 ) for PSO-ANFIS, MLR, and ANFIS models are 0.9951, 0.9889, and 0.9932 in the training stage, and 0.9423, 0.9181, and 0.8776 in the testing stage, respectively.  ...  A new hybrid approach has been developed in the prediction phase that combines the ANFIS and PSO (ANFIS-PSO) to estimate the optimum parameters.  ... 
doi:10.33969/ais.2021.31002 fatcat:3rs4iuesa5flhgnzsoxrmczf2q

Development of a sustainable groundwater management strategy and sequential compliance monitoring to control saltwater intrusion in coastal aquifers

Dilip Kumar Roy
2018
ResearchOnline@JCU This file is part of the following work: Roy, Dilip Kumar (2018) Development of a sustainable groundwater management strategy and sequential compliance monitoring to control saltwater  ...  , 2009) , in determining optimum parameters of a protective spur dike (Basser et al., 2015) and in determining spatiotemporal groundwater quality parameters (Jalalkamali, 2015) .  ...  The parameters of the ANFIS model were tuned using a hybrid lowest variance in RMSE values between training and test datasets was checked in order to protect against model overfitting.  ... 
doi:10.25903/5be278a6d3ad1 fatcat:rbnb7di7svcozezblwyuy2fdpa