Matches in SemOpenAlex for { <https://semopenalex.org/work/W2893630142> ?p ?o ?g. }
- W2893630142 endingPage "54" @default.
- W2893630142 startingPage "54" @default.
- W2893630142 abstract "Accurate water demand forecasting is essential to operate urban water supply facilities efficiently and ensure water demands for urban residents. This study proposes an extreme learning machine (ELM) coupled with variational mode decomposition (VMD) for short-term water demand forecasting in six cities (Anseong-si, Hwaseong-si, Pyeongtaek-si, Osan-si, Suwon-si, and Yongin-si), South Korea. The performance of VMD-ELM model is investigated based on performance indices and graphical analysis and compared with that of artificial neural network (ANN), ELM, and VMD-ANN models. VMD is employed for multi-scale time series decomposition and ANN and ELM models are used for sub-time series forecasting. As a result, ELM model outperforms ANN model. VMD-ANN and VMD-ELM models outperform ANN and ELM models, and the VMD-ELM model produces the best performance among all the models. The results obtained from this study reveal that the coupling of VMD and ELM can be an effective forecasting tool for short-term water demands with strong nonlinearity and non-stationarity and contribute to operating urban water supply facilities efficiently." @default.
- W2893630142 created "2018-10-05" @default.
- W2893630142 creator A5030586684 @default.
- W2893630142 creator A5054347354 @default.
- W2893630142 creator A5060494991 @default.
- W2893630142 date "2018-09-27" @default.
- W2893630142 modified "2023-10-09" @default.
- W2893630142 title "Short-Term Water Demand Forecasting Model Combining Variational Mode Decomposition and Extreme Learning Machine" @default.
- W2893630142 cites W1498436455 @default.
- W2893630142 cites W1854912902 @default.
- W2893630142 cites W1974853958 @default.
- W2893630142 cites W1978127050 @default.
- W2893630142 cites W1979919415 @default.
- W2893630142 cites W1985479415 @default.
- W2893630142 cites W2000982976 @default.
- W2893630142 cites W2042985051 @default.
- W2893630142 cites W2065902166 @default.
- W2893630142 cites W2073378589 @default.
- W2893630142 cites W2077831453 @default.
- W2893630142 cites W2081670007 @default.
- W2893630142 cites W2107878631 @default.
- W2893630142 cites W2111072639 @default.
- W2893630142 cites W2112602938 @default.
- W2893630142 cites W2114824684 @default.
- W2893630142 cites W2116094477 @default.
- W2893630142 cites W2134603844 @default.
- W2893630142 cites W2136217856 @default.
- W2893630142 cites W2147746661 @default.
- W2893630142 cites W2195290284 @default.
- W2893630142 cites W2250153062 @default.
- W2893630142 cites W2262639697 @default.
- W2893630142 cites W2267965433 @default.
- W2893630142 cites W2279630689 @default.
- W2893630142 cites W2302101267 @default.
- W2893630142 cites W2518675717 @default.
- W2893630142 cites W2531670876 @default.
- W2893630142 cites W2593952792 @default.
- W2893630142 cites W2736041446 @default.
- W2893630142 cites W2751583757 @default.
- W2893630142 cites W2768409874 @default.
- W2893630142 cites W2788895239 @default.
- W2893630142 cites W2797615260 @default.
- W2893630142 cites W2808709491 @default.
- W2893630142 cites W2808869902 @default.
- W2893630142 cites W2810751608 @default.
- W2893630142 cites W2888201619 @default.
- W2893630142 cites W3017323153 @default.
- W2893630142 cites W388323479 @default.
- W2893630142 doi "https://doi.org/10.3390/hydrology5040054" @default.
- W2893630142 hasPublicationYear "2018" @default.
- W2893630142 type Work @default.
- W2893630142 sameAs 2893630142 @default.
- W2893630142 citedByCount "25" @default.
- W2893630142 countsByYear W28936301422019 @default.
- W2893630142 countsByYear W28936301422020 @default.
- W2893630142 countsByYear W28936301422021 @default.
- W2893630142 countsByYear W28936301422022 @default.
- W2893630142 countsByYear W28936301422023 @default.
- W2893630142 crossrefType "journal-article" @default.
- W2893630142 hasAuthorship W2893630142A5030586684 @default.
- W2893630142 hasAuthorship W2893630142A5054347354 @default.
- W2893630142 hasAuthorship W2893630142A5060494991 @default.
- W2893630142 hasBestOaLocation W28936301421 @default.
- W2893630142 hasConcept C111919701 @default.
- W2893630142 hasConcept C121332964 @default.
- W2893630142 hasConcept C124681953 @default.
- W2893630142 hasConcept C154945302 @default.
- W2893630142 hasConcept C18903297 @default.
- W2893630142 hasConcept C2780150128 @default.
- W2893630142 hasConcept C41008148 @default.
- W2893630142 hasConcept C48677424 @default.
- W2893630142 hasConcept C50644808 @default.
- W2893630142 hasConcept C61797465 @default.
- W2893630142 hasConcept C62520636 @default.
- W2893630142 hasConcept C86803240 @default.
- W2893630142 hasConceptScore W2893630142C111919701 @default.
- W2893630142 hasConceptScore W2893630142C121332964 @default.
- W2893630142 hasConceptScore W2893630142C124681953 @default.
- W2893630142 hasConceptScore W2893630142C154945302 @default.
- W2893630142 hasConceptScore W2893630142C18903297 @default.
- W2893630142 hasConceptScore W2893630142C2780150128 @default.
- W2893630142 hasConceptScore W2893630142C41008148 @default.
- W2893630142 hasConceptScore W2893630142C48677424 @default.
- W2893630142 hasConceptScore W2893630142C50644808 @default.
- W2893630142 hasConceptScore W2893630142C61797465 @default.
- W2893630142 hasConceptScore W2893630142C62520636 @default.
- W2893630142 hasConceptScore W2893630142C86803240 @default.
- W2893630142 hasIssue "4" @default.
- W2893630142 hasLocation W28936301421 @default.
- W2893630142 hasLocation W28936301422 @default.
- W2893630142 hasOpenAccess W2893630142 @default.
- W2893630142 hasPrimaryLocation W28936301421 @default.
- W2893630142 hasRelatedWork W1545807863 @default.
- W2893630142 hasRelatedWork W2098759308 @default.
- W2893630142 hasRelatedWork W2357396576 @default.
- W2893630142 hasRelatedWork W2372022541 @default.
- W2893630142 hasRelatedWork W2386387936 @default.
- W2893630142 hasRelatedWork W2891769814 @default.