Grace Gau1, Minerva Singh2,3,*
Revue Internationale de Géomatique, Vol.33, pp. 427-443, 2024, DOI:10.32604/rig.2024.055752
- 11 October 2024
Abstract This study examines how socio-economic characteristics predict flood risk in London, England, using machine learning algorithms. The socio-economic variables considered included race, employment, crime and poverty measures. A stacked generalization (SG) model combines random forest (RF), support vector machine (SVM), and XGBoost. Binary classification issues employ RF as the basis model and SVM as the meta-model. In multiclass classification problems, RF and SVM are base models while XGBoost is meta-model. The study utilizes flood risk labels for London areas and census data to train these models. This study found that SVM performs well in binary… More >
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