Gépi tanulás, statisztikus adatfeldolgozás, jelfeldolgozáson alapuló alkalmazások
(pl. beszéd, kép, videó)
Mesterséges intelligencia és döntéstámogatás
Műszaki és technológiai tudományok
Villamosmérnöki és informatikai tudományok
Urban flood hazard assessment using an ensemble approach can reduce the bias of individual
models and provide a more accurate picture of how flood risks may change in specific
locations over time. By incorporating different models, the ensemble approach can
produce a more accurate prediction of flooding events. In the current research, we
used an ensemble machine learning for flood hazard assessment. The results showed
that the ensemble model outperforms other methods, such as the classification and
regression tree (CART) method and random forest (RF). The results of the hazard mapping
verify the credibility of the obtained data for raising awareness and informing the
public about flood-prone areas.
