Artificial intelligence (AI) holds significant promise for advancing natural disaster
management through the use of predictive models that analyze extensive datasets, identify …

The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume,
variety and velocity of water-related data are increasing due to large-scale sensor networks …

Leaf area index (LAI) is a terrestrial essential climate variable that is required in a variety of
ecosystem and climate models. The Global LAnd Surface Satellite (GLASS) LAI product has …

The booming development of remote sensing images in many visual tasks has led to an
increasing demand for obtaining images with more precise details. However, it is impractical …

The purpose of the study was to use hierarchical clustering and Thiessen polygon
algorithms to identify the significant rain gauge stations for flood forecasting at Sardar …

Accurate quantitative precipitation estimation (QPE) is essential for various applications,
including land surface modeling, flood forecasting, drought monitoring and prediction. In situ …

This study proposed a novel double machine learning (DML) approach to merge multiple
satellite-based precipitation products (SPPs) and gauge observations, and tested its …

Global climate change has led to an increase in both the frequency and magnitude of
extreme events around the world, the risk of which is especially imminent in tropical regions …

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

Abstract Long Short-Term Memory (LSTM) has recently emerged as a crucial tool for
scientific research in hydrology and water resources. Despite its widespread use, a …