PubMed İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://gcris.yasar.edu.tr/handle/123456789/11288

Browse

Now showing 1 - 2 of 2

Citation - WoS: 5
Hybrid Generalized Regularized Extreme Learning Machine Through Gradient-Based Optimizer Model for Self-Cleansing Nondeposition with Clean Bed Mode of Sediment Transport
(MARY ANN LIEBERT INC, 2024) Enes Gul; Mir Jafar Sadegh Safari; Gul, Enes; Safari, Mir Jafar Sadegh
Sediment transport modeling is an important problem to minimize sedimentation in open channels that could lead to unexpected operation expenses. From an engineering perspective the development of accurate models based on effective variables involved for flow velocity computation could provide a reliable solution in channel design. Furthermore validity of sediment transport models is linked to the range of data used for the model development. Existing design models were established on the limited data ranges. Thus the present study aimed to utilize all experimental data available in the literature including recently published datasets that covered an extensive range of hydraulic properties. Extreme learning machine (ELM) algorithm and generalized regularized extreme learning machine (GRELM) were implemented for the modeling and then particle swarm optimization (PSO) and gradient-based optimizer (GBO) were utilized for the hybridization of ELM and GRELM. GRELM-PSO and GRELM-GBO findings were compared to the standalone ELM GRELM and existing regression models to determine their accurate computations. The analysis of the models demonstrated the robustness of the models that incorporate channel parameter. The poor results of some existing regression models seem to be linked to the disregarding of the channel parameter. Statistical analysis of the model outcomes illustrated the outperformance of GRELM-GBO in contrast to the ELM GRELM GRELM-PSO and regression models although GRELM-GBO performed slightly better when compared to the GRELM-PSO counterpart. It was found that the mean accuracy of GRELM-GBO was 18.5% better when compared to the best regression model. The promising findings of the current study not only may encourage the use of recommended algorithms for channel design in practice but also may further the application of novel ELM-based methods in alternative environmental problems.
Citation - WoS: 3
Citation - Scopus: 3
Vertical and Horizontal Water Penetration Velocity Modeling in Nonhomogenous Soil Using Fast Multi-Output Relevance Vector Regression
(Mary Ann Liebert Inc., 2024) Babak Vaheddoost; Shervin Rahimzadeh Arashloo; Mir Jafar Sadegh Safari; Arashloo, Shervin Rahimzadeh; Vaheddoost, Babak; Safari, Mir Jafar Sadegh
A joint determination of horizontal and vertical movement of water through porous medium is addressed in this study through fast multi-output relevance vector regression (FMRVR). To do this an experimental data set conducted in a sand box with 300 · 300 · 150 mm dimensions made of Plexiglas is used. A random mixture of sand having size of 0.5–1 mm is used to simulate the porous medium. Within the experiments 2 3 7 and 12 cm walls are used together with different injection locations as 130.7 91.3 and 51.8 mm measured from the cutoff wall at the upstream. Then the Cartesian coordinated of the tracer time interval length of the wall in each setup and two dummy variables for determination of the initial point are considered as independent variables for joint estimation of horizontal and vertical velocity of water movement in the porous medium. Alternatively the multi-linear regression random forest and the support vector regression approaches are used to alternate the results obtained by the FMRVR method. It was concluded that the FMRVR outperforms the other models while the uncertainty in estimation of horizontal penetration is larger than the vertical one. © 2024 Elsevier B.V. All rights reserved.

Browse

Browsing PubMed İndeksli Yayınlar Koleksiyonu by Journal "Big Data"