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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| Format: | Article |
| Language: | English |
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MDPI AG
2019-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/10/2388 |
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| author | Javier J. Sánchez-Medina Juan Antonio Guerra-Montenegro David Sánchez-Rodríguez Itziar G. Alonso-González Juan L. Navarro-Mesa |
| author_facet | Javier J. Sánchez-Medina Juan Antonio Guerra-Montenegro David Sánchez-Rodríguez Itziar G. Alonso-González Juan L. Navarro-Mesa |
| author_sort | Javier J. Sánchez-Medina |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:04:12Z |
| format | Article |
| id | doaj.art-6fd1c346ecc4421084f5390c68a9e77c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:04:12Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6fd1c346ecc4421084f5390c68a9e77c2022-12-22T04:00:46ZengMDPI AGSensors1424-82202019-05-011910238810.3390/s19102388s19102388Data Stream Mining Applied to Maximum Wind Forecasting in the Canary IslandsJavier J. Sánchez-Medina0Juan Antonio Guerra-Montenegro1David Sánchez-Rodríguez2Itziar G. Alonso-González3Juan L. Navarro-Mesa4Centro de Innovación para la Sociedad de la Información (CICEI), Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, SpainCentro de Innovación para la Sociedad de la Información (CICEI), Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, SpainInstituto Universitario para el Desarrollo Tecnológico y la Innovación en Comunicaciones, Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, SpainInstituto Universitario para el Desarrollo Tecnológico y la Innovación en Comunicaciones, Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, SpainInstituto Universitario para el Desarrollo Tecnológico y la Innovación en Comunicaciones, Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, SpainThe 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.https://www.mdpi.com/1424-8220/19/10/2388short-term wind speed predictiondata stream miningextreme weather forecastingadaptive learninglinear regressionsensor networktouristic destinations |
| spellingShingle | Javier J. Sánchez-Medina Juan Antonio Guerra-Montenegro David Sánchez-Rodríguez Itziar G. Alonso-González Juan L. Navarro-Mesa Data Stream Mining Applied to Maximum Wind Forecasting in the Canary Islands Sensors short-term wind speed prediction data stream mining extreme weather forecasting adaptive learning linear regression sensor network touristic destinations |
| title | Data Stream Mining Applied to Maximum Wind Forecasting in the Canary Islands |
| title_full | Data Stream Mining Applied to Maximum Wind Forecasting in the Canary Islands |
| title_fullStr | Data Stream Mining Applied to Maximum Wind Forecasting in the Canary Islands |
| title_full_unstemmed | Data Stream Mining Applied to Maximum Wind Forecasting in the Canary Islands |
| title_short | Data Stream Mining Applied to Maximum Wind Forecasting in the Canary Islands |
| title_sort | data stream mining applied to maximum wind forecasting in the canary islands |
| topic | short-term wind speed prediction data stream mining extreme weather forecasting adaptive learning linear regression sensor network touristic destinations |
| url | https://www.mdpi.com/1424-8220/19/10/2388 |
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