Nature-based solutions (NBS) for hydro-meteorological risks (HMRs) reduction and
management are becoming increasingly popular, but challenges such as the lack of well …

In the past decade, urban pluvial flood-induced impacts on road traffic have been studied in
the context of global climate change and rapid urbanization. The main objective of this paper …

This study investigates how deep-learning can be configured to optimise the prediction of
2D maximum water depth maps in urban pluvial flood events. A neural network model is …

It is well known that rainwater harvesting (RWH) can augment water supply and reduce
stormwater pollutant discharges. Due to the lack of continuous 2D modelling of urban flood …

With the global climate change and the rapid urbanization process, there is an increase in
the risk of urban floods. Therefore, undertaking risk studies of urban floods, especially the …

Two-dimensional hydrodynamic models are computationally expensive. This drawback can
limit their application to solving problems requiring real-time predictions or several …

In this study, a convolutional neural network (CNN)-based approach is proposed to assess
flood susceptibility for urban catchment. Nine explanatory factors covering precipitation …

Computational complexity has been the bottleneck for applying physically based simulations
in large urban areas with high spatial resolution for efficient and systematic flooding …

Distributed green stormwater management infrastructure is increasingly applied worldwide
to counter the negative impacts of urbanisation and climate change, while providing a range …

In recent years, urban flood disasters caused by sudden heavy rains have become
increasingly severe, posing a serious threat to urban public infrastructure and the life and …