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Crisis Impact Prediction: A Data-driven Approach

Magister-uppsats

Paglamidis, Konstantinos

Luleå tekniska universitet/Institutionen för system- och rymdteknik

Publicerad: 2024

Språk: Engelska

Nyckelord

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⌕Artificial Intelligence⌕Machine learning⌕Decision Tree⌕Deep Learning⌕Supervised Learning⌕crisis management⌕Neural Network⌕data analysis⌕Random Forest⌕data mining⌕impact prediction⌕humanitarian disasters⌕CRISP-DM⌕perceptron⌕K-nearest neighbors⌕hyper-parameter tuning⌕natural disaster

Sammanfattning

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.

Information

Författare
Paglamidis, Konstantinos
Lärosäte / institution
Luleå tekniska universitet/Institutionen för system- och rymdteknik
Publiceringsdatum
2024
Uppsatstyp
Magister-uppsats
Språk
Engelska
Nyckelord
⌕Artificial Intelligence⌕Machine learning⌕Decision Tree⌕Deep Learning⌕Supervised Learning⌕crisis management⌕Neural Network⌕data analysis⌕Random Forest⌕data mining⌕impact prediction⌕humanitarian disasters⌕CRISP-DM⌕perceptron⌕K-nearest neighbors⌕hyper-parameter tuning⌕natural disaster

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