Rainfall nowcasting is critical for timely weather predictions and emergency responses,
particularly in flood-prone areas. Existing models, while accurate, often require substantial …

In this paper, we address the critical task of 24-h streamflow forecasting using advanced
deep-learning models, with a primary focus on the transformer architecture which has seen …

The volume and variety of Earth data have increased as a result of growing attention to
climate change and, subsequently, the availability of large-scale sensor networks and …

The temporal and spatial resolution of rainfall data is crucial for environmental modeling
studies in which its variability in space and time is considered as a primary factor. Rainfall …

Flooding causes fatalities, injuries, and damage to properties, agricultural sites, critical
infrastructure, and transportation networks in urban and agricultural communities. The …

Rainfall nowcasting provides short-term, high-resolution information on the location,
intensity, and timing of rainfall, which is crucial for weather forecasting, flood warning, and …

Water is essential for all living things on the planet. Excessive rainfall, on the other hand,
might result in disasters such as flooding. Accurate streamflow forecasting and efficient flood …

The literature on deep learning applications for hydrologic research could be broadly
summarized as repeated usage of the similar deep learning architectures for new regions …

Flooding is one of the most prevalent types of natural disasters affecting communities
worldwide. It is expected to persist with increasing magnitude and frequency due to climate …

Flooding causes fatalities, injuries, and damage to properties, agricultural sites, critical
infrastructure, and transportation networks in urban and agricultural communities. The …