FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385383452> ?p ?o ?g. }

• W4385383452 abstract "Uncontrolled sediment deposition in drainage and sewer systems raises unexpected maintenance expenditures. To this end, implementation of an accurate model relying on effective parameters involved is a reliable benchmark. In this study, three machine learning techniques, namely extreme learning machine (ELM), multilayer perceptron neural network (MLPNN), and M5P model tree (M5PMT); and three optimization approaches of Runge Kutta (RUN), genetic algorithm (GA), and particle swarm optimization (PSO) are applied for modeling. The optimization and ensemble hybridization approaches are applied in the modeling procedure. For the case of hybrid optimized models, the ELM and MLPNN models are hybridized with RUN, GA, and PSO algorithms to develop six hybrid models of ELM-RUN, ELM-GA, ELM-PSO, MLPNN-RUN, MLPNN-GA, and MLPNN-PSO. Ensemble hybrid models are developed through coupling the ELM and MLPNN models with the M5PMT algorithm. The data pre-processing approach is applied to find the best randomness characteristic of the utilized data. Results illustrate that the RUN-based hybrid models outperform the GA- and PSO-based counterparts. Although the MLPNN-RUN and MLPNN-M5PMT hybrid models generate better results than their alternatives, MLPNN-M5PMT slightly outperforms MLPNN-RUN model with a coefficient of determination of 0.84 and a root mean square error of 0.88. The current study shows the superiority of the ensemble-based approach to the optimization techniques. Further investigation is needed by considering alternative optimization techniques to enhance sediment transport modeling." @default.
• W4385383452 created "2023-07-30" @default.
• W4385383452 creator A5011828401 @default.
• W4385383452 creator A5073094974 @default.
• W4385383452 creator A5089211167 @default.
• W4385383452 creator A5092567087 @default.
• W4385383452 date "2023-07-01" @default.
• W4385383452 modified "2023-10-18" @default.
• W4385383452 title "Ensemble and optimized hybrid algorithms through Runge Kutta optimizer for sewer sediment transport modeling using a data pre-processing approach" @default.
• W4385383452 cites W1974138876 @default.
• W4385383452 cites W1995438682 @default.
• W4385383452 cites W1996656176 @default.
• W4385383452 cites W1998583908 @default.
• W4385383452 cites W2001239957 @default.
• W4385383452 cites W2015577873 @default.
• W4385383452 cites W2025404282 @default.
• W4385383452 cites W2045751937 @default.
• W4385383452 cites W2052437577 @default.
• W4385383452 cites W2057309892 @default.
• W4385383452 cites W2058621438 @default.
• W4385383452 cites W2061316809 @default.
• W4385383452 cites W2077967845 @default.
• W4385383452 cites W2082156006 @default.
• W4385383452 cites W2111072639 @default.
• W4385383452 cites W2133701585 @default.
• W4385383452 cites W2158054309 @default.
• W4385383452 cites W2179759512 @default.
• W4385383452 cites W2199348279 @default.
• W4385383452 cites W2320655252 @default.
• W4385383452 cites W2338364858 @default.
• W4385383452 cites W2473006291 @default.
• W4385383452 cites W2512028259 @default.
• W4385383452 cites W2521959714 @default.
• W4385383452 cites W2534814456 @default.
• W4385383452 cites W2550441664 @default.
• W4385383452 cites W2608079944 @default.
• W4385383452 cites W2615467640 @default.
• W4385383452 cites W2617552559 @default.
• W4385383452 cites W2763383283 @default.
• W4385383452 cites W2802405801 @default.
• W4385383452 cites W2811478008 @default.
• W4385383452 cites W2899328875 @default.
• W4385383452 cites W2901162987 @default.
• W4385383452 cites W2904171083 @default.
• W4385383452 cites W2905485021 @default.
• W4385383452 cites W2922036177 @default.
• W4385383452 cites W2925296862 @default.
• W4385383452 cites W2955470352 @default.
• W4385383452 cites W2956383698 @default.
• W4385383452 cites W2972778482 @default.
• W4385383452 cites W2980653736 @default.
• W4385383452 cites W2991192488 @default.
• W4385383452 cites W3000279472 @default.
• W4385383452 cites W3005185122 @default.
• W4385383452 cites W3015046602 @default.
• W4385383452 cites W3036869571 @default.
• W4385383452 cites W3038102268 @default.
• W4385383452 cites W3080470029 @default.
• W4385383452 cites W3112197966 @default.
• W4385383452 cites W3120293017 @default.
• W4385383452 cites W3129951222 @default.
• W4385383452 cites W3161470247 @default.
• W4385383452 cites W3165708600 @default.
• W4385383452 cites W3202095040 @default.
• W4385383452 cites W4210901740 @default.
• W4385383452 cites W4251169056 @default.
• W4385383452 cites W4253219449 @default.
• W4385383452 cites W4281670283 @default.
• W4385383452 cites W4293136856 @default.
• W4385383452 cites W4313455559 @default.
• W4385383452 cites W4323353084 @default.
• W4385383452 cites W3042160676 @default.
• W4385383452 doi "https://doi.org/10.1016/j.ijsrc.2023.07.003" @default.
• W4385383452 hasPublicationYear "2023" @default.
• W4385383452 type Work @default.
• W4385383452 citedByCount "0" @default.
• W4385383452 crossrefType "journal-article" @default.
• W4385383452 hasAuthorship W4385383452A5011828401 @default.
• W4385383452 hasAuthorship W4385383452A5073094974 @default.
• W4385383452 hasAuthorship W4385383452A5089211167 @default.
• W4385383452 hasAuthorship W4385383452A5092567087 @default.
• W4385383452 hasConcept C11413529 @default.
• W4385383452 hasConcept C119857082 @default.
• W4385383452 hasConcept C119898033 @default.
• W4385383452 hasConcept C13280743 @default.
• W4385383452 hasConcept C154945302 @default.
• W4385383452 hasConcept C185798385 @default.
• W4385383452 hasConcept C205649164 @default.
• W4385383452 hasConcept C2780150128 @default.
• W4385383452 hasConcept C41008148 @default.
• W4385383452 hasConcept C50644808 @default.
• W4385383452 hasConcept C85617194 @default.
• W4385383452 hasConcept C8880873 @default.
• W4385383452 hasConceptScore W4385383452C11413529 @default.
• W4385383452 hasConceptScore W4385383452C119857082 @default.
• W4385383452 hasConceptScore W4385383452C119898033 @default.
• W4385383452 hasConceptScore W4385383452C13280743 @default.
• W4385383452 hasConceptScore W4385383452C154945302 @default.
• W4385383452 hasConceptScore W4385383452C185798385 @default.
• W4385383452 hasConceptScore W4385383452C205649164 @default.

• next