Spatial Conflict prediction using Machine Learning

Detalhes bibliográficos
Autor(a) principal: Guzzardo, Frank
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/134614
Resumo: Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
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spelling Spatial Conflict prediction using Machine LearningSahelConflictRandom ForestPredictionDissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesIn the last few decades there has been a steady increase in intrastate conflict around the globe. In response, there is a rising need for actionable information for national and international stakeholders to better forecast and mitigate the effects of intrastate conflict. The Sahel region is especially vulnerable to intrastate conflict suffering a multidimensional crisis that includes climate change, food insecurity, and the proliferation of armed conflict. This study seeks to explore the feasibility of producing a heuristic machine learning model utilizing open-source data to predict localized intrastate conflict events on a regional scale using the random forest regression algorithm. The model includes data from 2007 to 2020 selected from multiple sources to create 17 features representing real-world phenomena to predict conflict occurrence. A unified spatial data structure consisting of quadratic grid cells was used for local-level analysis. Implementing a 10-fold cross-validation method, the model performed well with an RMSE of 1.394 and an R2 of .95. There was an improvement of 76% from the baseline model.Pinheiro, Flávio Luís PortasTorres-Sospedra, JoaquínPainho, Marco Octávio TrindadeRUNGuzzardo, Frank2022-03-16T11:19:09Z2022-03-022022-03-02T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/134614TID:202965929enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:13:03Zoai:run.unl.pt:10362/134614Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:48:10.331815Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Spatial Conflict prediction using Machine Learning
title Spatial Conflict prediction using Machine Learning
spellingShingle Spatial Conflict prediction using Machine Learning
Guzzardo, Frank
Sahel
Conflict
Random Forest
Prediction
title_short Spatial Conflict prediction using Machine Learning
title_full Spatial Conflict prediction using Machine Learning
title_fullStr Spatial Conflict prediction using Machine Learning
title_full_unstemmed Spatial Conflict prediction using Machine Learning
title_sort Spatial Conflict prediction using Machine Learning
author Guzzardo, Frank
author_facet Guzzardo, Frank
author_role author
dc.contributor.none.fl_str_mv Pinheiro, Flávio Luís Portas
Torres-Sospedra, Joaquín
Painho, Marco Octávio Trindade
RUN
dc.contributor.author.fl_str_mv Guzzardo, Frank
dc.subject.por.fl_str_mv Sahel
Conflict
Random Forest
Prediction
topic Sahel
Conflict
Random Forest
Prediction
description Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
publishDate 2022
dc.date.none.fl_str_mv 2022-03-16T11:19:09Z
2022-03-02
2022-03-02T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/134614
TID:202965929
url http://hdl.handle.net/10362/134614
identifier_str_mv TID:202965929
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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