Data Stream Mining Applied to Maximum Wind Forecasting in the Canary Islands

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, mana...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-05, Vol.19 (10), p.2388
Main Authors: Sánchez-Medina, Javier J, Guerra-Montenegro, Juan Antonio, Sánchez-Rodríguez, David, Alonso-González, Itziar G, Navarro-Mesa, Juan L
Format: Article
Language:English
Subjects:
adaptive learning
Artificial intelligence
Big Data
Climate change
Cyclones
Data mining
data stream mining
Data transmission
Decision trees
extreme weather forecasting
Greenhouse gases
linear regression
Machine learning
Meteorology
Methodology
Rain
sensor network
Short term
short-term wind speed prediction
Tourism
touristic destinations
Weather forecasting
Wind speed
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19102388