The field of crisis management and humanitarian assistance has been one of the major fields of development for governmental and common best European practices in the last decades. The European Union as a major humanitarian stakeholder has taken great effort to strengthen the response in case of humanitarian disasters. This work addresses the feasibility and possible benefits of using machine learning in the prediction of the impact severity of a disaster as a model-driven data analysis in comparison to data-driven reference models for early response coordination and preparedness. In comparison to classical data analysis systems the feasibility of earthquake impact prediction based on machine learning models is evaluated and further debated.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-107819 |
| Date | January 2024 |
| Creators | Paglamidis, Konstantinos |
| Publisher | Luleå tekniska universitet, Institutionen för system- och rymdteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0019 seconds