Matches in SemOpenAlex for { <https://semopenalex.org/work/W2912116308> ?p ?o ?g. }
- W2912116308 endingPage "226" @default.
- W2912116308 startingPage "210" @default.
- W2912116308 abstract "Modelling time series of groundwater levels is investigated by three fuzzy logic (FL) models, Sugeno (SFL), Mamdani (MFL) and Larsen (LFL), using data from observation wells. One novelty in the study is the re-use of these three models as multiple models through the following strategies: (a) simple averaging, (b) weighted averaging and (c) committee machine techniques; these are implemented using artificial neural networks (ANN). These strategies provide some evidence that (i) multiple models improve on the performance of individual models and those using committee machines perform better than the other two options; and (ii) committee machine models produce defensible modelling results to develop management scenarios. The study investigates water table declines through management scenarios and shows that in this aquifer water use has higher impacts on water table variations than climatic variations. This provides evidence of the need for planned management in the study area." @default.
- W2912116308 created "2019-02-21" @default.
- W2912116308 creator A5000442615 @default.
- W2912116308 creator A5034983101 @default.
- W2912116308 creator A5051049157 @default.
- W2912116308 creator A5061754306 @default.
- W2912116308 date "2019-01-25" @default.
- W2912116308 modified "2023-09-29" @default.
- W2912116308 title "Modelling groundwater level variations by learning from multiple models using fuzzy logic" @default.
- W2912116308 cites W1502132756 @default.
- W2912116308 cites W1969868514 @default.
- W2912116308 cites W1973676661 @default.
- W2912116308 cites W1998863178 @default.
- W2912116308 cites W2003913953 @default.
- W2912116308 cites W2006059886 @default.
- W2912116308 cites W2008558681 @default.
- W2912116308 cites W2009636568 @default.
- W2912116308 cites W2012257195 @default.
- W2912116308 cites W2016381774 @default.
- W2912116308 cites W2021245834 @default.
- W2912116308 cites W2037460094 @default.
- W2912116308 cites W2038929774 @default.
- W2912116308 cites W2039351583 @default.
- W2912116308 cites W2039581585 @default.
- W2912116308 cites W2041534329 @default.
- W2912116308 cites W2045464761 @default.
- W2912116308 cites W2074987129 @default.
- W2912116308 cites W2094954761 @default.
- W2912116308 cites W2130915832 @default.
- W2912116308 cites W2142703368 @default.
- W2912116308 cites W2156415214 @default.
- W2912116308 cites W2158260560 @default.
- W2912116308 cites W2479630616 @default.
- W2912116308 cites W2531455364 @default.
- W2912116308 cites W2587824557 @default.
- W2912116308 cites W2602242084 @default.
- W2912116308 cites W2763720207 @default.
- W2912116308 cites W2792071392 @default.
- W2912116308 cites W2796816642 @default.
- W2912116308 cites W2886245647 @default.
- W2912116308 cites W4211007335 @default.
- W2912116308 cites W4232256040 @default.
- W2912116308 cites W4232786292 @default.
- W2912116308 cites W571971660 @default.
- W2912116308 doi "https://doi.org/10.1080/02626667.2018.1554940" @default.
- W2912116308 hasPublicationYear "2019" @default.
- W2912116308 type Work @default.
- W2912116308 sameAs 2912116308 @default.
- W2912116308 citedByCount "68" @default.
- W2912116308 countsByYear W29121163082019 @default.
- W2912116308 countsByYear W29121163082020 @default.
- W2912116308 countsByYear W29121163082021 @default.
- W2912116308 countsByYear W29121163082022 @default.
- W2912116308 countsByYear W29121163082023 @default.
- W2912116308 crossrefType "journal-article" @default.
- W2912116308 hasAuthorship W2912116308A5000442615 @default.
- W2912116308 hasAuthorship W2912116308A5034983101 @default.
- W2912116308 hasAuthorship W2912116308A5051049157 @default.
- W2912116308 hasAuthorship W2912116308A5061754306 @default.
- W2912116308 hasConcept C119857082 @default.
- W2912116308 hasConcept C124101348 @default.
- W2912116308 hasConcept C127413603 @default.
- W2912116308 hasConcept C138885662 @default.
- W2912116308 hasConcept C153823671 @default.
- W2912116308 hasConcept C154945302 @default.
- W2912116308 hasConcept C187320778 @default.
- W2912116308 hasConcept C18903297 @default.
- W2912116308 hasConcept C27206212 @default.
- W2912116308 hasConcept C2778738651 @default.
- W2912116308 hasConcept C39769621 @default.
- W2912116308 hasConcept C41008148 @default.
- W2912116308 hasConcept C42475967 @default.
- W2912116308 hasConcept C45235069 @default.
- W2912116308 hasConcept C50644808 @default.
- W2912116308 hasConcept C58166 @default.
- W2912116308 hasConcept C75622301 @default.
- W2912116308 hasConcept C76177295 @default.
- W2912116308 hasConcept C86803240 @default.
- W2912116308 hasConceptScore W2912116308C119857082 @default.
- W2912116308 hasConceptScore W2912116308C124101348 @default.
- W2912116308 hasConceptScore W2912116308C127413603 @default.
- W2912116308 hasConceptScore W2912116308C138885662 @default.
- W2912116308 hasConceptScore W2912116308C153823671 @default.
- W2912116308 hasConceptScore W2912116308C154945302 @default.
- W2912116308 hasConceptScore W2912116308C187320778 @default.
- W2912116308 hasConceptScore W2912116308C18903297 @default.
- W2912116308 hasConceptScore W2912116308C27206212 @default.
- W2912116308 hasConceptScore W2912116308C2778738651 @default.
- W2912116308 hasConceptScore W2912116308C39769621 @default.
- W2912116308 hasConceptScore W2912116308C41008148 @default.
- W2912116308 hasConceptScore W2912116308C42475967 @default.
- W2912116308 hasConceptScore W2912116308C45235069 @default.
- W2912116308 hasConceptScore W2912116308C50644808 @default.
- W2912116308 hasConceptScore W2912116308C58166 @default.
- W2912116308 hasConceptScore W2912116308C75622301 @default.
- W2912116308 hasConceptScore W2912116308C76177295 @default.
- W2912116308 hasConceptScore W2912116308C86803240 @default.
- W2912116308 hasFunder F4320324703 @default.