Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/60048
Título: Data stream mininga applied to maximum wind forecasting in the Canary Islands
Autores/as: Sanchez-Medina, Javier J. 
Antonio Guerra-Montenegro, Juan
Sanchez-Rodriguez, David 
Alonso-González, Itziar G. 
Navarro-Mesa, Juan L. 
Clasificación UNESCO: 120304 Inteligencia artificial
Palabras clave: Artificial neural-networks
Speed
Model
Fecha de publicación: 2019
Publicación seriada: Sensors 
Resumen: The Canary Islands are a well known tourist destination with generally stable and clement weather conditions. However, occasionally extreme weather conditions occur, which although very unusual, may cause severe damage to the local economy. The ViMetRi-MAC EU funded project has among its goals, managing climate-change-associated risks. The Spanish National Meteorology Agency (AEMET) has a network of weather stations across the eight Canary Islands. Using data from those stations, we propose a novel methodology for the prediction of maximum wind speed in order to trigger an early alert for extreme weather conditions. The methodology proposed has the added value of using an innovative kind of machine learning that is based on the data stream mining paradigm. This type of machine learning system relies on two important features: models are learned incrementally and adaptively. That means the learner tunes the models gradually and endlessly as new observations are received and also modifies it when there is concept drift (statistical instability), in the modeled phenomenon. The results presented seem to prove that this data stream mining approach is a good fit for this kind of problem, clearly improving the results obtained with the accumulative non-adaptive version of the methodology.
URI: http://hdl.handle.net/10553/60048
ISSN: 1424-8220
DOI: 10.3390/s19102388
Fuente: Sensors [ISSN 1424-8220], v. 19 (10)
Colección:Artículos
miniatura
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