Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/69663
DC FieldValueLanguage
dc.contributor.authorAguasca-Colomo, Ricardoen_US
dc.contributor.authorCastellanos-Nieves, Dagobertoen_US
dc.contributor.authorMéndez, Máximoen_US
dc.date.accessioned2020-02-03T18:08:26Z-
dc.date.available2020-02-03T18:08:26Z-
dc.date.issued2019en_US
dc.identifier.issn2076-3417en_US
dc.identifier.otherWoS-
dc.identifier.urihttp://hdl.handle.net/10553/69663-
dc.description.abstractWe present a comparative study between predictive monthly rainfall models for islands of complex orography using machine learning techniques. The models have been developed for the island of Tenerife (Canary Islands). Weather forecasting is influenced both by the local geographic characteristics as well as by the time horizon comprised. Accuracy of mid-term rainfall prediction on islands with complex orography is generally low when carried out with atmospheric models. Predictive models based on algorithms such as Random Forest or Extreme Gradient Boosting among others were analyzed. The predictors used in the models include weather predictors measured in two main meteorological stations, reanalysis predictors from the National Oceanic and Atmospheric Administration, and the global predictor North Atlantic Oscillation, all of them obtained over a period of time of more than four decades. When comparing the proposed models, we evaluated accuracy, kappa and interpretability of the model obtained, as well as the relevance of the predictors used. The results show that global predictors such as the North Atlantic Oscillation Index (NAO) have a very low influence, while the local Geopotential Height (GPH) predictor is relatively more important. Machine learning prediction models are a relevant proposition for predicting medium-term precipitation in similar geographical regions.en_US
dc.languageengen_US
dc.relation.ispartofApplied Sciences (Basel)en_US
dc.sourceApplied Sciences (Basel) [ISSN 2076-3417], v. 9 (22), 4931en_US
dc.subject250909 Predicción numérica meteorológicaen_US
dc.subject120304 Inteligencia artificialen_US
dc.subject.otherClassification algorithmsen_US
dc.subject.otherData processingen_US
dc.subject.otherMachine learningen_US
dc.subject.otherComputational methodsen_US
dc.subject.otherPredictive modelsen_US
dc.subject.otherRainfall forecastingen_US
dc.subject.otherExtreme gradient boosting (XGBoost)en_US
dc.subject.otherRandom forest (rf)en_US
dc.titleComparative analysis of rainfall prediction models using machine learning in islands with complex orography: Tenerife islanden_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/app9224931en_US
dc.identifier.isi000502570800208-
dc.identifier.eissn2076-3417-
dc.identifier.issue22-
dc.description.firstpage4931en_US
dc.relation.volume9en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.daisngid32095488-
dc.contributor.daisngid15089018-
dc.contributor.daisngid9011687-
dc.description.numberofpages17en_US
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Aguasca-Colomo, R-
dc.contributor.wosstandardWOS:Castellanos-Nieves, D-
dc.contributor.wosstandardWOS:Mendez, M-
dc.date.coverdateNoviembre 2019en_US
dc.identifier.ulpgces
dc.description.sjr0,418
dc.description.jcr2,474
dc.description.sjrqQ1
dc.description.jcrqQ2
dc.description.scieSCIE
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptGIR SIANI: Computación Evolutiva y Aplicaciones-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Ingeniería Electrónica y Automática-
crisitem.author.deptGIR SIANI: Computación Evolutiva y Aplicaciones-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0003-2217-8005-
crisitem.author.orcid0000-0002-7133-7108-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.fullNameAguasca Colomo, Ricardo-
crisitem.author.fullNameMéndez Babey, Máximo-
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