Please use this identifier to cite or link to this item:
http://hdl.handle.net/10553/69663
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Aguasca-Colomo, Ricardo | en_US |
dc.contributor.author | Castellanos-Nieves, Dagoberto | en_US |
dc.contributor.author | Méndez, Máximo | en_US |
dc.date.accessioned | 2020-02-03T18:08:26Z | - |
dc.date.available | 2020-02-03T18:08:26Z | - |
dc.date.issued | 2019 | en_US |
dc.identifier.issn | 2076-3417 | en_US |
dc.identifier.other | WoS | - |
dc.identifier.uri | http://hdl.handle.net/10553/69663 | - |
dc.description.abstract | We 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.language | eng | en_US |
dc.relation.ispartof | Applied Sciences (Basel) | en_US |
dc.source | Applied Sciences (Basel) [ISSN 2076-3417], v. 9 (22), 4931 | en_US |
dc.subject | 250909 Predicción numérica meteorológica | en_US |
dc.subject | 120304 Inteligencia artificial | en_US |
dc.subject.other | Classification algorithms | en_US |
dc.subject.other | Data processing | en_US |
dc.subject.other | Machine learning | en_US |
dc.subject.other | Computational methods | en_US |
dc.subject.other | Predictive models | en_US |
dc.subject.other | Rainfall forecasting | en_US |
dc.subject.other | Extreme gradient boosting (XGBoost) | en_US |
dc.subject.other | Random forest (rf) | en_US |
dc.title | Comparative analysis of rainfall prediction models using machine learning in islands with complex orography: Tenerife island | en_US |
dc.type | info:eu-repo/semantics/article | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.3390/app9224931 | en_US |
dc.identifier.isi | 000502570800208 | - |
dc.identifier.eissn | 2076-3417 | - |
dc.identifier.issue | 22 | - |
dc.description.firstpage | 4931 | en_US |
dc.relation.volume | 9 | en_US |
dc.investigacion | Ingeniería y Arquitectura | en_US |
dc.type2 | Artículo | en_US |
dc.contributor.daisngid | 32095488 | - |
dc.contributor.daisngid | 15089018 | - |
dc.contributor.daisngid | 9011687 | - |
dc.description.numberofpages | 17 | en_US |
dc.utils.revision | Sí | en_US |
dc.contributor.wosstandard | WOS:Aguasca-Colomo, R | - |
dc.contributor.wosstandard | WOS:Castellanos-Nieves, D | - |
dc.contributor.wosstandard | WOS:Mendez, M | - |
dc.date.coverdate | Noviembre 2019 | en_US |
dc.identifier.ulpgc | Sí | es |
dc.description.sjr | 0,418 | |
dc.description.jcr | 2,474 | |
dc.description.sjrq | Q1 | |
dc.description.jcrq | Q2 | |
dc.description.scie | SCIE | |
item.grantfulltext | open | - |
item.fulltext | Con texto completo | - |
crisitem.author.dept | GIR SIANI: Computación Evolutiva y Aplicaciones | - |
crisitem.author.dept | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.dept | Departamento de Ingeniería Electrónica y Automática | - |
crisitem.author.dept | GIR SIANI: Computación Evolutiva y Aplicaciones | - |
crisitem.author.dept | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.dept | Departamento de Informática y Sistemas | - |
crisitem.author.orcid | 0000-0003-2217-8005 | - |
crisitem.author.orcid | 0000-0002-7133-7108 | - |
crisitem.author.parentorg | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.parentorg | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.fullName | Aguasca Colomo, Ricardo | - |
crisitem.author.fullName | Méndez Babey, Máximo | - |
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