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The neural network model achieved 91.55% accuracy while the best performing traditional classifier, K-Nearest Neighbor, managed 74.61%. In addition, the neural network model performed 20.97% better than the past neural networks, which illustrates both advances in machine learning algorithms, as well as improved accuracy high enough to apply practically to forest management issues. Using the techniques outlined in this article, agencies can cost-efficiently and quickly predict tree cover type and expedite natural resource inventorying.<\/p>","DOI":"10.4018\/ijmdem.2018010101","type":"journal-article","created":{"date-parts":[[2017,12,26]],"date-time":"2017-12-26T10:40:23Z","timestamp":1514284823000},"page":"1-21","source":"Crossref","is-referenced-by-count":9,"title":["Machine Learning Classification of Tree Cover Type and Application to Forest Management"],"prefix":"10.4018","volume":"9","author":[{"given":"Duncan","family":"MacMichael","sequence":"first","affiliation":[{"name":"University of Washington, Bothell, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Si","sequence":"additional","affiliation":[{"name":"University of Washington, Bothell, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJMDEM.2018010101-0","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2015.2492363"},{"key":"IJMDEM.2018010101-1","unstructured":"Alavalapati, J., Matta, J., & Tanner, G. 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