Publicação
Spatial Conflict prediction using Machine Learning
| dc.contributor.advisor | Pinheiro, Flávio Luís Portas | |
| dc.contributor.advisor | Torres-Sospedra, Joaquín | |
| dc.contributor.advisor | Painho, Marco Octávio Trindade | |
| dc.contributor.author | Guzzardo, Frank | |
| dc.date.accessioned | 2022-03-16T11:19:09Z | |
| dc.date.available | 2022-03-16T11:19:09Z | |
| dc.date.issued | 2022-03-02 | |
| dc.description | Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies | pt_PT |
| dc.description.abstract | In 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. | pt_PT |
| dc.identifier.tid | 202965929 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/134614 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | pt_PT |
| dc.subject | Sahel | pt_PT |
| dc.subject | Conflict | pt_PT |
| dc.subject | Random Forest | pt_PT |
| dc.subject | Prediction | pt_PT |
| dc.title | Spatial Conflict prediction using Machine Learning | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Tecnologias Geoespaciais | pt_PT |
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