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Decision support: using machine learning through MATLAB to analyze environmental data

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  • David W. Nadler

    (New York Institute of Technology)

Abstract

Machine learning is not a tool that is available for use by computer scientists, but one that can and should be used by all researchers in this technological era. Gone are the days of humans solely relying on older techniques for decision support. The age of information we live in is filled with countless pieces of data and we need to use the correct tools to help make sense of it all. Using MATLAB and its machine learning tools is an excellent resource for environmental scientists to conduct deep-dives into their data. We use this software title to demonstrate some of its capabilities to enhance our research projects. Regression learning examines the capability of developing the best linear regression model based upon the selected independent and dependent variables. Clustering analysis displays how data can be grouped by similar characteristics and how distant they are from one another. Classification analysis can predict future outcomes depending upon historical input data, a crucial tool in developing models for impending environmental events. It is suggested that environmental scientists who have not incorporated machine learning into their research to begin to add it to their data analyses.

Suggested Citation

  • David W. Nadler, 2019. "Decision support: using machine learning through MATLAB to analyze environmental data," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 9(4), pages 419-428, December.
    • Handle: RePEc:spr:jenvss:v:9:y:2019:i:4:d:10.1007_s13412-019-00558-9
    DOI: 10.1007/s13412-019-00558-9
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    References listed on IDEAS

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    1. Gregory Hill & Steven Kolmes & Michael Humphreys & Rebecca McLain & Eric T. Jones, 2019. "Using decision support tools in multistakeholder environmental planning: restorative justice and subbasin planning in the Columbia River Basin," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 9(2), pages 170-186, June.
    2. Tamim Younos & Juneseok Lee & Tammy Parece, 2019. "Twenty-first century urban water management: the imperative for holistic and cross-disciplinary approach," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 9(1), pages 90-95, March.
    3. Hari Bansha Dulal, 2019. "Cities in Asia: how are they adapting to climate change?," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 9(1), pages 13-24, March.
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