Abstract
The research deals with an exploration of high dimensional environmental data using unsupervised learning algorithms and the concept of local fractality. The proposed methodology is applied to geospatial data used for the wind speed prediction in a complex mountainous region. It is shown, that the approach provides important additional information on data manifold useful in data analysis, data visualisation and predictive modelling.
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Acknowledgements
The research was partly supported by the Swiss National Research Program “Big Data” (PNR75), project “Hybrid Renewable Energy Potential for the Built Environment using Big Data: Forecasting and Uncertainty Estimation” (no. 4075-40_167285).
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Kanevski, M., Laib, M. (2021). Unsupervised Learning of High Dimensional Environmental Data Using Local Fractality Concept. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12666. Springer, Cham. https://doi.org/10.1007/978-3-030-68780-9_13
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DOI: https://doi.org/10.1007/978-3-030-68780-9_13
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