Publicação
Predicting wildfire risks in Portugal: a machine learning approach under the Paris agreement climate targets
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | pt_PT |
| dc.contributor.advisor | Guha, Sreyaa | |
| dc.contributor.author | Jung, Frederik Michael | |
| dc.date.accessioned | 2025-03-19T11:07:48Z | |
| dc.date.available | 2025-03-19T11:07:48Z | |
| dc.date.issued | 2024-01-22 | |
| dc.date.submitted | 2023-12-19 | |
| dc.description.abstract | This study investigates machine learning in wildfire risk management in Portugal. Incorporating environmental, demographic, socio-economic, and policy data, it examines wildfire dynamics and the shortcomings of conventional approaches. The research evaluates Portugal's National Wildland Fire Management Plan and the effect of climate change on wildfire risk. Findings indicate machine learning models enhance risk assessment accuracy and that robust climate action, aligned with the Paris Agreement, could mitigate wildfire severity. The study advocates for an integrated management strategy, blending technology, policy analysis, and environmental knowledge, and offers recommendations for better wildfire mitigation. | pt_PT |
| dc.identifier.tid | 203605578 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/180907 | |
| dc.language.iso | eng | pt_PT |
| dc.relation | UID/ECO/00124/2013 | pt_PT |
| dc.subject | Machine learning | pt_PT |
| dc.subject | Climate change | pt_PT |
| dc.subject | Big data analytics | pt_PT |
| dc.subject | Wildfire management | pt_PT |
| dc.title | Predicting wildfire risks in Portugal: a machine learning approach under the Paris agreement climate targets | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | A Work Project, presented as part of the requirements for the Award of a Master’s degree in Business Analytics from the Nova School of Business and Economics | pt_PT |