Advertisement

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

  • David W. NadlerEmail author
Research Brief
  • 26 Downloads

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.

Keywords

Decision support Cluster analysis Neural networks Regression Machine learning 

References

  1. Alpaydin E (2014) Introduction to machine learning. MIT Press, Cambridge MAGoogle Scholar
  2. Best ÜSN, Van der Wegen M, Dijkstra J, Willemsen PWJM, Borsje BW, Roelvink DJA (2018) Do salt marshes survive sea level rise? Modelling wave action, morphodynamics and vegetation dynamics. Environ Model Softw 109(November):152–166.  https://doi.org/10.1016/j.envsoft.2018.08.004 CrossRefGoogle Scholar
  3. Bishop CM (2016) Pattern recognition and machine learning. Springer, New YorkGoogle Scholar
  4. Collins MB, Munoz I, JaJa J (2016) Linking ‘toxic outliers’ to environmental justice communities. Environ Res Lett 11(1):015004.  https://doi.org/10.1088/1748-9326/11/1/015004 CrossRefGoogle Scholar
  5. Daniel G (2013) Principles of artificial neural networks, 3rd edn. World Scientific, SingaporeGoogle Scholar
  6. Dickson ME, Perry GLW (2016) Identifying the controls on coastal cliff landslides using machine-learning approaches. Environ Model Softw 76(February):117–127.  https://doi.org/10.1016/j.envsoft.2015.10.029 CrossRefGoogle Scholar
  7. Dulal HB (2019) Cities in Asia: how are they adapting to climate change? J Environ Stud Sci 9(1):13–24.  https://doi.org/10.1007/s13412-018-0534-1 CrossRefGoogle Scholar
  8. Feldman D, Contreras S, Karlin B, Basolo V, Matthew R, Sanders B, Houston D et al (2016) Communicating flood risk: looking back and forward at traditional and social media outlets. International Journal of Disaster Risk Reduction 15(March):43–51.  https://doi.org/10.1016/j.ijdrr.2015.12.004 CrossRefGoogle Scholar
  9. Fuchs S, Heiser M, Schlögl M, Zischg A, Papathoma-Köhle M, Keiler M (2019) Short communication: a model to predict flood loss in mountain areas. Environ Model Softw 117:176–180.  https://doi.org/10.1016/j.envsoft.2019.03.026 CrossRefGoogle Scholar
  10. Gilat A (2017) MATLAB: an introduction with applications. John Wiley & Sons, Incorporated, HobokenGoogle Scholar
  11. Gómez-Losada Á, Pires JCM, Pino-Mejías R (2018) Modelling background air pollution exposure in urban environments: implications for epidemiological research. Environ Model Softw, Special issue on environmental data science. Applications to air quality and water cycle 106(August):13–21.  https://doi.org/10.1016/j.envsoft.2018.02.011 CrossRefGoogle Scholar
  12. Griffin LP, Griffin CR, Finn JT, Prescott RL, Faherty M, Still BM, Danylchuk AJ (2019) Warming seas increase cold-stunning events for Kemp’s Ridley Sea turtles in the Northwest Atlantic. PLoS One 1Google Scholar
  13. Habans R, Clement MT, Pattison A (2019) Carbon emissions and climate policy support by local governments in California: a qualitative comparative analysis at the county level. J Environ Stud Sci.  https://doi.org/10.1007/s13412-019-00544-1
  14. Hahn B, Valentine D (2016) Essential MATLAB for engineers and scientists. Academic Press, Cambridge MAGoogle Scholar
  15. Hill G, Kolmes S, Humphreys M, McLain R, Jones ET (2019) Using decision support tools in multistakeholder environmental planning: restorative justice and subbasin planning in the Columbia River basin. J Environ Stud Sci 9:170–186.  https://doi.org/10.1007/s13412-019-00548-x CrossRefGoogle Scholar
  16. Hoos AB, Wang SH, Schwarz GE (2019) Adapting a regional water-quality model for local application: a case study for Tennessee, USA. Environ Model Softw 115(May):187–199.  https://doi.org/10.1016/j.envsoft.2019.01.001 CrossRefGoogle Scholar
  17. How efficient is twitter: predicting 2012 U.S. presidential elections using support vector machine via twitter and comparing against Iowa electronic markets. 2017. 2017 Intelligent Systems Conference (IntelliSys), Intelligent Systems Conference (IntelliSys), 2017, 646.  https://doi.org/10.1109/IntelliSys.2017.8324363
  18. Johnson RA, Bhattacharyya GK (2018) Statistics: principles and methods. John Wiley & Sons, HobokenGoogle Scholar
  19. Joshi R, Ahmed S (2016) Status and challenges of municipal solid waste Management in India: a review. Edited by Carla Aparecida Ng. Cogent Environ Sci 2(1):1139434.  https://doi.org/10.1080/23311843.2016.1139434 CrossRefGoogle Scholar
  20. Korup O, Stolle A (2014) Landslide prediction from machine learning. Geol Today 30(1):26–33.  https://doi.org/10.1111/gto.12034 CrossRefGoogle Scholar
  21. Lary DJ, Lary T, Sattler B (2015) Using machine learning to estimate global PM2.5 for environmental health studies. Environ Health Insights 9s1(January):EHI.S15664.  https://doi.org/10.4137/EHI.S15664 CrossRefGoogle Scholar
  22. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444.  https://doi.org/10.1038/nature14539 CrossRefGoogle Scholar
  23. Liao, K-H, Deng S, Tan PY (2017) Blue-green infrastructure: new frontier for sustainable urban stormwater management. In: Puay Yok Tan and Chi Yung Jim (eds) Greening cities: forms and functions. Advances in 21st Century Human Settlements. Springer Singapore, Singapore, pp 203–26.  https://doi.org/10.1007/978-981-10-4113-6_10
  24. Malaviya P, Sharma R, Sharma PK (2019) Rain gardens as Stormwater management tool. In: Shachi S, Venkatramanan V, Prasad R (eds) Sustainable green technologies for environmental management. Springer Singapore, Singapore, pp 141–166.  https://doi.org/10.1007/978-981-13-2772-8_7 CrossRefGoogle Scholar
  25. MATLAB (version 2019a). 2019. Mathworks. https://www.mathworks.com/help/stats/regressionlearner-app.html
  26. Mayr A, Klambauer G, Unterthiner T, Hochreiter S (2016) DeepTox: toxicity prediction using deep learning. Frontiers in Environmental Science 3.  https://doi.org/10.3389/fenvs.2015.00080
  27. Pearl J (2019) The seven tools of causal inference, with reflections on machine learning. Commun ACM 62(3):54–60.  https://doi.org/10.1145/3241036 CrossRefGoogle Scholar
  28. Prediction of human population responses to toxic compounds by a collaborative competition | Nature Biotechnology. 2015. 2015. https://www.nature.com/articles/nbt.3299
  29. Robinson OJ, Tewkesbury A, Kemp S, Williams ID (2018) Towards a universal carbon footprint standard: a case study of carbon management at universities. J Clean Prod 172(January):4435–4455.  https://doi.org/10.1016/j.jclepro.2017.02.147 CrossRefGoogle Scholar
  30. Samuel AL (1959) Some studies in machine learning using the game of checkers. IBM J Res Dev:71–105Google Scholar
  31. Smucker TA, Wisner B, Mascarenhas A, Munishi P, Wangui EE, Sinha G, Weiner D, Bwenge C, Lovell E (2015) Differentiated livelihoods, local institutions, and the adaptation imperative: assessing climate change adaptation policy in Tanzania. Geoforum 59(February):39–50.  https://doi.org/10.1016/j.geoforum.2014.11.018 CrossRefGoogle Scholar
  32. Stritih A, Bebi P, Grêt-Regamey A (2019) Quantifying uncertainties in earth observation-based ecosystem service assessments. Environ Model Softw 111(January):300–310.  https://doi.org/10.1016/j.envsoft.2018.09.005 CrossRefGoogle Scholar
  33. Sung Kyun Park, Zhao Z, Mukherjee B, Park SK, Zhao Z (2017) Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES. Environ Health Glob Access Sci Source 16(September):1–17.  https://doi.org/10.1186/s12940-017-0310-9 Google Scholar
  34. Tasdighi A, Arabi M, Harmel D, Line D (2018) A Bayesian Total uncertainty analysis framework for assessment of management practices using watershed models. Environ Model Softw 108(October):240–252.  https://doi.org/10.1016/j.envsoft.2018.08.006 CrossRefGoogle Scholar
  35. Taylor Kyla W, Joubert Bonnie R, Braun Joe M, Caroline D, Chris G, Russ H, Heindel Jerry J, Rider Cynthia V, Webster Thomas F, Carlin Danielle J (2016) Statistical approaches for assessing health effects of environmental chemical mixtures in epidemiology: lessons from an innovative workshop. Environ Health Perspect 124(12):A227–A229.  https://doi.org/10.1289/EHP547 Google Scholar
  36. The Social Dimensions of Climate Change (n.d.) In. World Health Organization. Accessed June 4, 2019. https://www.who.int/globalchange/mediacentre/events/2011/social-dimensions-of-climate-change.pdf
  37. Ting KM (2010) Confusion matrix. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer US, Boston, MA, pp 209–209.  https://doi.org/10.1007/978-0-387-30164-8_157 Google Scholar
  38. Yang Y, Ethan C, Wi S, Ray PA, Brown CM, Khalil AF (2016) The future Nexus of the Brahmaputra River basin: climate, water, energy and food trajectories. Glob Environ Chang 37(March):16–30.  https://doi.org/10.1016/j.gloenvcha.2016.01.002 CrossRefGoogle Scholar
  39. Younos T, Lee J, Parece T (2019) Twenty-first century urban water management: the imperative for holistic and cross-disciplinary approach. J Environ Stud Sci 9(1):90–95.  https://doi.org/10.1007/s13412-018-0524-3 CrossRefGoogle Scholar
  40. Zhou C, Yin K, Cao Y, Intrieri E, Ahmed B, Catani F (2018) Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method. Landslides 15(11):2211–2225.  https://doi.org/10.1007/s10346-018-1022-0 CrossRefGoogle Scholar

Copyright information

© AESS 2019

Authors and Affiliations

  1. 1.New York Institute of TechnologyOld WestburyUSA

Personalised recommendations