As one of nature's most destructive calamities, floods cause fatalities, property destruction,
and infrastructure damage, affecting millions of people worldwide. Due to its ability to …

Machine learning (also called data-driven) methods have become popular in modeling flood
inundations across river basins. Among data-driven methods, traditional machine learning …

Urban pluvial floods induced by rainstorms can cause severe losses to human lives and
property. Fast and accurate simulation and prediction of urban pluvial flood are of …

A vital and rapidly growing application, remote sensing offers vast yet sparsely labeled,
spatially aligned multimodal data; this makes self-supervised learning algorithms invaluable …

Accurate flood map** is crucial for enhancing community resilience in the face of flooding.
Among the several factors that affect the flood extent, the effect of fluvial dynamics is not …

Floods are one of the most catastrophic natural disasters. Water level forecasting is an
essential method of avoiding floods and disaster preparedness. In recent years, models for …

Hydrodynamic models can accurately simulate flood inundation but are limited by their high
computational demand that scales non-linearly with model complexity, resolution, and …

Large-scale hydrodynamic models generally rely on fixed-resolution spatial grids and model
parameters as well as incurring a high computational cost. This limits their ability to …

Floods are among the most severe and impacting natural disasters. Their occurrence rate
and intensity have been significantly increasing worldwide in the last years due to climate …

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