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

Javier J Sánchez-Medina1, Juan Antonio Guerra-Montenegro2, David Sánchez-Rodríguez3

  • 1Centro 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, Spain. javier.sanchez@ulpgc.es.

Sensors (Basel, Switzerland)
|May 30, 2019
PubMed
Summary

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This summary is machine-generated.

This study introduces a new machine learning method for predicting extreme wind speeds in the Canary Islands. This approach uses adaptive data stream mining to improve early warning systems for severe weather events.

Area of Science:

  • Meteorology and Climatology
  • Machine Learning and Data Science

Background:

  • The Canary Islands, a popular tourist destination, experience occasional extreme weather events that can severely impact the local economy.
  • Climate change necessitates better management of associated risks, including extreme weather phenomena.

Purpose of the Study:

  • To develop a novel methodology for predicting maximum wind speed to enable early alerts for extreme weather conditions in the Canary Islands.
  • To leverage data stream mining for adaptive and incremental machine learning models.

Main Methods:

  • Utilized data from the Spanish National Meteorology Agency's (AEMET) weather station network across the Canary Islands.
  • Implemented a machine learning methodology based on the data stream mining paradigm, featuring incremental and adaptive model learning.
Keywords:
adaptive learningdata stream miningextreme weather forecastinglinear regressionsensor networkshort-term wind speed predictiontouristic destinations

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  • Addressed concept drift by continuously tuning and modifying models as new data becomes available.

    • Main Results:

    • The proposed data stream mining approach demonstrated suitability for predicting maximum wind speed.
    • This adaptive methodology significantly improved prediction results compared to non-adaptive methods.
    • The system effectively learns and adapts to changing weather patterns.

    Conclusions:

    • Data stream mining offers a robust and effective solution for early warning systems of extreme weather events.
    • Adaptive machine learning models are crucial for accurately predicting phenomena susceptible to statistical instability.
    • The developed methodology enhances climate-change-associated risk management in vulnerable regions like the Canary Islands.