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Computational Intelligence and Data Mining Techniques Using the Fire Data Set

Storer, Jeremy J
http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1460129796

Abstract Details

2016, Master of Science (MS), Bowling Green State University, Computer Science.
Forest fires are a dangerous and devastating phenomenon. Being able to accurately predict the burned area of a forest fire could potentially limit biological damage as well as better prepare for ensuing economical and ecological damage. A data set from the Montesinho Natural Park in Portugal provides a difficult regression task regarding the prediction of forest fire burn area due to the limited amount of data entries and the imbalanced nature of the data set. This thesis focuses on improving these results through the use of a Backpropagation trained Artificial Neural Network which is systematically evaluated over a variety of configurations, activation functions, and input methodologies, resulting in approximately 30% improvements to regression error rates. A Particle Swarm Optimization (PSO) trained Artificial Neural Network is also evaluated in a variety of configurations providing approximately 75% improvement of regression error rates. Going further, the data is also clustered on both inputs and outputs using k-Means and Spectral algorithms in order to pursue the task of classification where near perfect classification is achieved when clustering on inputs is considered and an accuracy of roughly 60% is achieved when clustering on output values.
Robert Green, PhD. (Advisor)
Jong Kwan Lee, PhD. (Committee Member)
Robert Dyer, PhD. (Committee Member)
59 p.
Computer Science
Fire Dataset; Machine Learning; Computational Intelligence; Data Mining; Neural Networks; Particle Swarm Optimization; k-Means Clustering; Spectral Clustering

Recommended Citations

Citations

  • Storer, J. J. (2016). Computational Intelligence and Data Mining Techniques Using the Fire Data Set [Master's thesis, Bowling Green State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1460129796

    APA Style (7th edition)

  • Storer, Jeremy. Computational Intelligence and Data Mining Techniques Using the Fire Data Set. 2016. Bowling Green State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1460129796.

    MLA Style (8th edition)

  • Storer, Jeremy. "Computational Intelligence and Data Mining Techniques Using the Fire Data Set." Master's thesis, Bowling Green State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1460129796

    Chicago Manual of Style (17th edition)

bgsu1460129796
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© 2016, all rights reserved.
This open access ETD is published by Bowling Green State University and OhioLINK.