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Machine Learning, Big Data, and Spatial Tools: A Combination to Reveal Complex Facts That Impact Environmental Health

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Geospatial Technology for Human Well-Being and Health

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Abstract

By definition, human health and well-being are due to a complex interaction between a myriad of factors, a set of interactions for which we do not have a complete theoretical model, but often have an array of relevant data describing many aspects of this complex system. Machine learning is ideally suited for such situations. Over the last two decades, machine learning has found many uses in business and science and technology. As a broad subfield of artificial intelligence, machine learning is concerned with algorithms and techniques that allow computers to “learn” by example. The major focus of machine learning is to extract information from data automatically by computational and statistical methods. Over the last decade, there has been considerable progress in develo** a machine learning methodology for a variety of Environmental Health and Earth Science applications. In this chapter, we will review some examples of how machine learning has already been useful in environmental studies and some likely future applications.

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Change history

  • 14 June 2022

    In chapters 12 & 13, the names of one of the authors have been inadvertently published as ‘Lakitha Omal Harindha Wijerante’ which has been updated as ‘Lakitha Omal Harindha Wijeratne’.

Notes

  1. 1.

    http://scikit-learn.org/stable/.

  2. 2.

    https://www.tensorflow.org.

  3. 3.

    http://caffe.berkeleyvision.org.

  4. 4.

    http://spark.apache.org/mllib/.

  5. 5.

    https://cran.r-project.org.

  6. 6.

    https://julialang.org/#tab-math

  7. 7.

    https://www.python.org.

  8. 8.

    https://www.mathworks.com/solutions/machine-learning.html.

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Authors and Affiliations

  1. Hanson Center for Space Sciences, The University of Texas at Dallas, Richardson, TX, USA

    David J. Lary, Lakitha Omal Harindha Wijeratne, Gebreab K. Zewdie, Daniel Kiv, Daji Wu, Shawhin Talebi, **aohe Yu, Yichao Zhang, Estelle Levetin, Rebecca J. Allee, Nabin Malakar, Annette Walker & Hamse Mussa

  2. Department of Preventive Medicine, University of Mississippi Medical Center, Jackson, Mississippi, USA

    Fazlay S. Faruque

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    Fazlay S. Faruque

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Lary, D.J. et al. (2022). Machine Learning, Big Data, and Spatial Tools: A Combination to Reveal Complex Facts That Impact Environmental Health. In: Faruque, F.S. (eds) Geospatial Technology for Human Well-Being and Health. Springer, Cham. https://doi.org/10.1007/978-3-030-71377-5_12

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