The rise in urban flooding events poses a threat to public safety, property, and economic
stability. To prevent urban flooding and manage stormwater effectively, relying solely on …

As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric
sciences is increasingly adopting data-driven models, powered by progressive …

In response to growing concerns surrounding the relationship between climate change and
escalating flood risk, there is an increasing urgency to develop precise and rapid flood …

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 …

Accurate prediction of density-driven convection of CO 2 overlaying brine in porous media is
crucial to the applications of geological carbon sequestration. In this paper, we introduce the …

Accurate water level prediction is very important for coastal construction and flood
prevention in an estuarine area. However, it is challenging to represent the water level …

Nowadays, meteorological data plays a crucial role in various fields such as remote sensing,
weather forecasting, climate change, and agriculture. The regional and local studies call for …

The rise of machine learning (ML) has significantly advanced the field of coastal
oceanography. This review aims to examine the existing deficiencies in numerical …

Physics-based models are computationally time-consuming and infeasible for real-time
scenarios of urban drainage networks, and a surrogate model is needed to accelerate the …

Physics‐informed neural networks (PINNs) are gaining attention as an alternative approach
to solve scientific problems governed by differential equations. This work aims at assessing …