Ensemble learning for enhancing critical infrastructure resilience to urban flooding

The main objective of this study is to enhance road-network flood prediction using ensemble machine learning models trained on crowd-sourced flood datasets. Accurate road network flood prediction remains challenging due to complex flow dynamics, coarse-resolution traditional models, and limited data.

The impacts of extreme weather cascade through interdependent and interconnected urban systems, disrupting transportation networks, supply chains, and access to critical services, such as hospitals, schools and power stations, among others. The insights gained from this study can help improve urban flood prediction which is crucial for enhancing community resilience to extreme weather events.