{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/109618"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/109618","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Testing the accuracy of machine learning methods to predict deforestation","abstract":"Forest plays a crucial role in meeting climate change goals, given its emissions reduction effects through carbon dioxide capture. The study of deforestation becomes significantly relevant since the early prediction of forest under threat could lead to specific policy responses promoting conservation measures. Common deforestation patterns are fish-bone, radial, geometric, and diffuse. This thesis aims to explore the predictive power of machine learning techniques to predict spatial patterns of human activities and compare their accuracy of prediction with a traditional statistical method. Using Monte Carlo simulations, land cover data was generated, mimicking human settlement patterns related to underlying deforestation processes. This work tests how different machine learning methodologies perform, after various experiments with diverse sources of data. The main result indicates that decision tree-based methodologies provide better prediction performance than other methods including elastic net regression. Implications of this work go beyond the conservation literature and could be used in other agricultural and applied economic areas where spatial patterns play a significant role.","abstract_html":"Forest plays a crucial role in meeting climate change goals, given its emissions reduction effects through carbon dioxide capture. The study of deforestation becomes significantly relevant since the early prediction of forest under threat could lead to specific policy responses promoting conservation measures. Common deforestation patterns are fish-bone, radial, geometric, and diffuse. This thesis aims to explore the predictive power of machine learning techniques to predict spatial patterns of human activities and compare their accuracy of prediction with a traditional statistical method. Using Monte Carlo simulations, land cover data was generated, mimicking human settlement patterns related to underlying deforestation processes. This work tests how different machine learning methodologies perform, after various experiments with diverse sources of data. The main result indicates that decision tree-based methodologies provide better prediction performance than other methods including elastic net regression. Implications of this work go beyond the conservation literature and could be used in other agricultural and applied economic areas where spatial patterns play a significant role.","abstract_has_math":false,"creators":["Flores Caceres, Ivan Andres"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Agricultural & Applied Econ","degree_department":null,"school":null,"contributors":["Baylis, Kathy","Michelson, Hope","Christensen, Peter"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-03-05T21:45:43Z","date_published":"2021-03-05T21:45:43Z","updated_at":"2026-07-22T22:24:50Z","subjects":["Machine Learning, Deforestation"],"languages":["en"],"rights":["Copyright 2020 Ivan Flores Caceres"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/109618","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Baylis, Kathy","Michelson, Hope","Christensen, Peter"]},{"key":"dc:creator","label":"Author","values":["Flores Caceres, Ivan Andres"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-03-05T21:45:43Z","2023-03-05T21:47:41Z","2020-12-07","2020-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Agricultural & Applied Econ"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine Learning, Deforestation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Ivan Flores Caceres"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/109618"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Forest plays a crucial role in meeting climate change goals, given its emissions reduction effects through carbon dioxide capture. The study of deforestation becomes significantly relevant since the early prediction of forest under threat could lead to specific policy responses promoting conservation measures. Common deforestation patterns are fish-bone, radial, geometric, and diffuse. This thesis aims to explore the predictive power of machine learning techniques to predict spatial patterns of human activities and compare their accuracy of prediction with a traditional statistical method. Using Monte Carlo simulations, land cover data was generated, mimicking human settlement patterns related to underlying deforestation processes. This work tests how different machine learning methodologies perform, after various experiments with diverse sources of data. The main result indicates that decision tree-based methodologies provide better prediction performance than other methods including elastic net regression. Implications of this work go beyond the conservation literature and could be used in other agricultural and applied economic areas where spatial patterns play a significant role.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-12-01","The student, Ivan Flores Caceres, accepted the attached license on 2020-12-01 at 17:38.","The student, Ivan Flores Caceres, submitted this Thesis for approval on 2020-12-01 at 17:50.","This Thesis was approved for publication on 2020-12-07 at 13:21.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16020 on 2021-03-04 at 16:33:10","Made available in DSpace on 2021-03-05T21:45:43Z (GMT). No. of bitstreams: 2 FLORESCACERES-THESIS-2020.pdf: 2935196 bytes, checksum: 4101f730a1a1deb988b6c66aa42f3d9a (MD5) LICENSE.txt: 4216 bytes, checksum: aed9a512a3c6ef5fa5c19203f629fdbc (MD5) Previous issue date: 2020-12-07","Embargo set by: Seth Robbins for item 117323 Lift date: 2023-03-05T21:45:47Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 117323 Lift date: 2023-03-05T21:47:41Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Testing the accuracy of machine learning methods to predict deforestation"]}]}],"canonical_facts":{"dc:contributor":["Baylis, Kathy","Michelson, Hope","Christensen, Peter"],"dc:creator":["Flores Caceres, Ivan Andres"],"dc:date":["2021-03-05T21:45:43Z","2023-03-05T21:47:41Z","2020-12-07","2020-12"],"dc:description":["Forest plays a crucial role in meeting climate change goals, given its emissions reduction effects through carbon dioxide capture. The study of deforestation becomes significantly relevant since the early prediction of forest under threat could lead to specific policy responses promoting conservation measures. Common deforestation patterns are fish-bone, radial, geometric, and diffuse. This thesis aims to explore the predictive power of machine learning techniques to predict spatial patterns of human activities and compare their accuracy of prediction with a traditional statistical method. Using Monte Carlo simulations, land cover data was generated, mimicking human settlement patterns related to underlying deforestation processes. This work tests how different machine learning methodologies perform, after various experiments with diverse sources of data. The main result indicates that decision tree-based methodologies provide better prediction performance than other methods including elastic net regression. Implications of this work go beyond the conservation literature and could be used in other agricultural and applied economic areas where spatial patterns play a significant role.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-12-01","The student, Ivan Flores Caceres, accepted the attached license on 2020-12-01 at 17:38.","The student, Ivan Flores Caceres, submitted this Thesis for approval on 2020-12-01 at 17:50.","This Thesis was approved for publication on 2020-12-07 at 13:21.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16020 on 2021-03-04 at 16:33:10","Made available in DSpace on 2021-03-05T21:45:43Z (GMT). No. of bitstreams: 2 FLORESCACERES-THESIS-2020.pdf: 2935196 bytes, checksum: 4101f730a1a1deb988b6c66aa42f3d9a (MD5) LICENSE.txt: 4216 bytes, checksum: aed9a512a3c6ef5fa5c19203f629fdbc (MD5) Previous issue date: 2020-12-07","Embargo set by: Seth Robbins for item 117323 Lift date: 2023-03-05T21:45:47Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 117323 Lift date: 2023-03-05T21:47:41Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/109618"],"dc:language":["en"],"dc:rights":["Copyright 2020 Ivan Flores Caceres"],"dc:subject":["Machine Learning, Deforestation"],"dc:title":["Testing the accuracy of machine learning methods to predict deforestation"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Agricultural & Applied Econ"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:50Z"}