FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3189352443> ?p ?o ?g. }

• W3189352443 endingPage "126757" @default.
• W3189352443 startingPage "126757" @default.
• W3189352443 abstract "• The s-FADE parameters were simultaneously estimated using inverse problem method. • Four inverse models were developed based on PSO, CSO, BA, and TLBO algorithms. • Among the algorithms, TLBO was the most efficient to estimate the s-FADE parameters. • TLBO was markedly superior to FracFit, a toolbox for obtaining the s-FADE parameters. • TLBO can be used as a highly efficient approach to estimate the s-FADE parameters. This work developed four inverse models based on Particle Swarm Optimization (PSO), Chicken Swarm Optimization (CSO), Bees Algorithm (BA), and Teaching Learning Based Optimization (TLBO), to identify parameters of space fractional advection–dispersion equation (s-FADE). The s-FADE has four parameters, including average pore-water velocity ( v ), fractional dispersion coefficient ( D f ), fractional derivative order (α), and skewness (β). A sensitivity analysis indicated that the v is the most effective parameter on the s-FADE results, followed by α, D f , and β, respectively. The experimental data required were measured at different transport distances of homogeneous and heterogeneous soil columns. Five criteria, namely, convergence trend, objective function value, runtime, repeatability of results, and modeling complexity were used to evaluate algorithm performances and to rank them using a technique for order of preference by similarity to ideal solution (TOPSIS). Based on the obtained results, all four algorithms acquired the global optimal values for the v and α parameters using a maximum iteration of 1000 as a stopping criterion and an initial population of 10, while they obtained the relatively different values for the D f , and β parameters. The PSO and TLBO algorithms successfully found the global minimum values of the objective functions for both the homogeneous and heterogeneous soils. Among the four algorithms, the TLBO algorithm was the best one in terms of convergence trend, repeatability of results, and modeling complexity, and it was the worst algorithm in term of runtime. Among the PSO, CSO, and BA algorithms, the BA algorithm was superior over the PSO and CSO algorithms in terms of runtime and repeatability of results, while the PSO algorithm was superior over the BA and CSO algorithms in term of converge speed. Overall, according to the results of the TOPSIS method, the TLBO algorithm was the best alternative to estimate the s-FADE parameters, followed by BA, PSO, and CSO algorithms. Also, the comparison of the s-FADE parameters estimated by the TLBO algorithm, as the best one among the four algorithms, with those estimated by FracFit toolbox revealed that both the techniques obtained the relatively identical and admissible values for the v and α parameters, while the TLBO algorithm acquired the more precise values for the D f and β parameters. A detailed analyses demonstrated that the TLBO algorithm was markedly superior to the FracFit toolbox in terms of the aforementioned criteria. In a nutshell, the TLBO algorithm can be used as a highly efficient optimization method to estimate the s-FADE parameters in both the homogeneous and the heterogeneous soils." @default.
• W3189352443 created "2021-08-16" @default.
• W3189352443 creator A5005921803 @default.
• W3189352443 creator A5027589396 @default.
• W3189352443 date "2021-11-01" @default.
• W3189352443 modified "2023-09-30" @default.
• W3189352443 title "A comparative study on using metaheuristic algorithms for simultaneously estimating parameters of space fractional advection-dispersion equation" @default.
• W3189352443 cites W1966627623 @default.
• W3189352443 cites W1977757419 @default.
• W3189352443 cites W1984538950 @default.
• W3189352443 cites W2004798461 @default.
• W3189352443 cites W2008265321 @default.
• W3189352443 cites W2024614787 @default.
• W3189352443 cites W2025159382 @default.
• W3189352443 cites W2037973974 @default.
• W3189352443 cites W2043922553 @default.
• W3189352443 cites W2060383632 @default.
• W3189352443 cites W2062174566 @default.
• W3189352443 cites W2074851072 @default.
• W3189352443 cites W2081653580 @default.
• W3189352443 cites W2090992715 @default.
• W3189352443 cites W2091457241 @default.
• W3189352443 cites W2099111135 @default.
• W3189352443 cites W2117733661 @default.
• W3189352443 cites W2127769310 @default.
• W3189352443 cites W2148932306 @default.
• W3189352443 cites W2158356426 @default.
• W3189352443 cites W2162673614 @default.
• W3189352443 cites W2168087114 @default.
• W3189352443 cites W2290883490 @default.
• W3189352443 cites W2592461921 @default.
• W3189352443 cites W2650791901 @default.
• W3189352443 cites W2735447725 @default.
• W3189352443 cites W2759812176 @default.
• W3189352443 cites W2883787252 @default.
• W3189352443 cites W2946065095 @default.
• W3189352443 cites W2991192488 @default.
• W3189352443 cites W2996206634 @default.
• W3189352443 cites W2996796639 @default.
• W3189352443 cites W3010271344 @default.
• W3189352443 cites W3022100458 @default.
• W3189352443 cites W3028772417 @default.
• W3189352443 cites W3036565793 @default.
• W3189352443 cites W4250501155 @default.
• W3189352443 doi "https://doi.org/10.1016/j.jhydrol.2021.126757" @default.
• W3189352443 hasPublicationYear "2021" @default.
• W3189352443 type Work @default.
• W3189352443 sameAs 3189352443 @default.
• W3189352443 citedByCount "10" @default.
• W3189352443 countsByYear W31893524432022 @default.
• W3189352443 countsByYear W31893524432023 @default.
• W3189352443 crossrefType "journal-article" @default.
• W3189352443 hasAuthorship W3189352443A5005921803 @default.
• W3189352443 hasAuthorship W3189352443A5027589396 @default.
• W3189352443 hasConcept C111919701 @default.
• W3189352443 hasConcept C11413529 @default.
• W3189352443 hasConcept C126255220 @default.
• W3189352443 hasConcept C154249771 @default.
• W3189352443 hasConcept C181335050 @default.
• W3189352443 hasConcept C207467116 @default.
• W3189352443 hasConcept C2524010 @default.
• W3189352443 hasConcept C2778518048 @default.
• W3189352443 hasConcept C28826006 @default.
• W3189352443 hasConcept C33923547 @default.
• W3189352443 hasConcept C41008148 @default.
• W3189352443 hasConcept C85617194 @default.
• W3189352443 hasConceptScore W3189352443C111919701 @default.
• W3189352443 hasConceptScore W3189352443C11413529 @default.
• W3189352443 hasConceptScore W3189352443C126255220 @default.
• W3189352443 hasConceptScore W3189352443C154249771 @default.
• W3189352443 hasConceptScore W3189352443C181335050 @default.
• W3189352443 hasConceptScore W3189352443C207467116 @default.
• W3189352443 hasConceptScore W3189352443C2524010 @default.
• W3189352443 hasConceptScore W3189352443C2778518048 @default.
• W3189352443 hasConceptScore W3189352443C28826006 @default.
• W3189352443 hasConceptScore W3189352443C33923547 @default.
• W3189352443 hasConceptScore W3189352443C41008148 @default.
• W3189352443 hasConceptScore W3189352443C85617194 @default.
• W3189352443 hasLocation W31893524431 @default.
• W3189352443 hasOpenAccess W3189352443 @default.
• W3189352443 hasPrimaryLocation W31893524431 @default.
• W3189352443 hasRelatedWork W14189022 @default.
• W3189352443 hasRelatedWork W1601689643 @default.
• W3189352443 hasRelatedWork W2028175972 @default.
• W3189352443 hasRelatedWork W2111857118 @default.
• W3189352443 hasRelatedWork W2380594721 @default.
• W3189352443 hasRelatedWork W2491247131 @default.
• W3189352443 hasRelatedWork W2754869408 @default.
• W3189352443 hasRelatedWork W3175773280 @default.
• W3189352443 hasRelatedWork W4311367510 @default.
• W3189352443 hasRelatedWork W58882117 @default.
• W3189352443 hasVolume "602" @default.
• W3189352443 isParatext "false" @default.
• W3189352443 isRetracted "false" @default.
• W3189352443 magId "3189352443" @default.
• W3189352443 workType "article" @default.