Machine learning, statistical data processing and applications using signal processing
(e.g. speech, image, video)
Artificial Intelligence & Decision support
ENGINEERING AND TECHNOLOGY
Electrical engineering, Electronic engineering, Information engineering
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.
