Browsing by Author "Bonakdari, Hossein"

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Citation - WoS: 47
Citation - Scopus: 53
Combination of sensitivity and uncertainty analyses for sediment transport modeling in sewer pipes
(IRTCES, 2020) Isa Ebtehaj; Hossein Bonakdari; Mir Jafar Sadegh Safari; Bahram Gharabaghi; Amir Hossein Zaji; Hossien Riahi Madavar; Zohreh Sheikh Khozani; Mohammad Sadegh Es-haghi; Aydin Shishegaran; Ali Danandeh Mehr; Bonakdari, Hossein; Mehr, Ali Danandeh; Safari, Mir Jafar Sadegh; Zaji, Amir Hossein; Gharabaghi, Bahram; Madavar, Hossien Riahi; Riahi Madavar, Hossien; Danandeh Mehr, Ali; Ebtehaj, Isa
Mitigation of sediment deposition in lined open channels is an essential issue in hydraulic engineering practice. Hence the limiting velocity should be determined to keep the channel bottom clean from sediment deposits. Recently sediment transport modeling using various artificial intelligence (AI) techniques has attracted the interest of many researchers. The current integrated study highlights unique insight for modeling of sediment transport in sewer and urban drainage systems. A novel methodology based on the combination of sensitivity and uncertainty analyses with a machine learning technique is proposed as a tool for selection of the best input combination for modeling process at non-deposition conditions of sediment transport. Utilizing one to seven dimensionless parameters 127 models are developed in the current study. In order to evaluate the different parameter combinations and select the training and testing data four strategies are considered. Considering the densimetric Froude number (Fr) as the dependent parameter a model with independent parameters of volumetric sediment concentration (C-V) and relative particle size (d/R) gave the best results with a mean absolute relative error (MARE) of 0.1 and a root means square error (RMSE) of 0.67. Uncertainty analysis is applied with a machine learning technique to investigate the credibility of the proposed methods. The percentage of the observed sample data bracketed by 95% predicted uncertainty bound (95PPU) is computed to assess the uncertainty of the best models. (C) 2019 International Research and Training Centre on Erosion and Sedimentation/the World Association for Sedimentation and Erosion Research. Published by Elsevier B.V. All rights reserved.
Citation - WoS: 50
Citation - Scopus: 57
Sediment transport modeling in rigid boundary open channels using generalize structure of group method of data handling
(Elsevier B.V., 2019) Mir Jafar Sadegh Safari; Isa Ebtehaj; Hossein Bonakdari; Mohammad Sadegh Es-haghi; Es-haghi, Mohammad Sadegh; Bonakdari, Hossein; Safari, Mir Jafar Sadegh; Ebtehaj, Isa
Sediment transport in open channels has complicated nature and finding the analytical models applicable for channel design in practice is a quite difficult task. To this end behind theoretical consideration of the open channel sediment transport through incorporating of four fundamental characteristics of fluid flow sediment and channel recently machine learning techniques are used for modeling of sediment transport in open channels. However most of the studies in the literature used limited number of data for model development neglecting some effective parameters involved which may affect their performances. Moreover most of this studies had not provided a comprehensive explicit equation for future use. Accordingly this study applied four machine learning techniques of Gene Expression Programming (GEP) Extreme Learning Machine (ELM) Generalized Structure Group Method of Data Handling (GS-GMDH) and Fuzzy c-means based Adaptive Neuro-Fuzzy Inference System (FCM-ANFIS) to model sediment transport in open channels. Four existing data sets in the literature with wide ranges of pipe size sediment size sediment volumetric concentration channel bed slope and flow depth are used for the model development. The recommended models are compared with their corresponding conventional regression models taken from the literature in terms of different statistical performance indices. Results indicate superiority of the machine leaning techniques to the conventional multiple non-linear regression models. Although developed GEP ELM GS-GMDH and FCM-ANFIS models have almost same performances GS-GMDH gives slightly better performance which can be linked to the generalized structure of this approach. A MATLAB code is provided to calculate the sediment transport in open channel for practical engineering. © 2019 Elsevier B.V. All rights reserved.