Deep Learning techniques have been increasingly used in flood management to overcome
the limitations of accurate, yet slow, numerical models, and to improve the results of …

Disaster management (DM) is one of the leading fields that deal with the humanitarian
aspects of emergencies. The field has attracted researchers because of its ever-increasing …

Identification of flood-prone sites in urban environments is necessary, but there is insufficient
hydraulic information and time series data on surface runoff. To date, several attempts have …

Multi-hazard susceptibility prediction is an important component of disasters risk
management plan. An effective multi-hazard risk mitigation strategy includes assessing …

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 …

Modelling flood susceptibility is an indirect way to reduce the loss from flood disaster. Now,
flood susceptibility modelling based on data driven model is state-of-the-art method such as …

Floods have been a major cause of destruction, instigating fatalities and massive damage to
the infrastructure and overall economy of the affected country. Flood-related devastation …

Due to a changing climate and increased urbanization, an escalation of urban flooding
occurrences and its aftereffects are ever more dire. Notably, the frequency of extreme storms …

Large damages and losses resulting from floods are widely reported across the globe. Thus,
the identification of the flood-prone zones on a flood susceptibility map is very essential. To …

Data-driven and machine learning models have recently received increasing interest to
resolve the computational speed challenge faced by various physically-based simulations. A …