Soil salinization is considered one of the disasters that have significant effects on
agricultural activities in many parts of the world, particularly in the context of climate change …

Crop classification using remote sensing data has emerged as a prominent research area in
recent decades. Studies have demonstrated that fusing synthetic aperture radar (SAR) and …

Class imbalance is a condition where the amount of data in the minority class is smaller than
that of the majority class. The impact of the class imbalance in the dataset is the occurrence …

Flood models based on traditional hydrodynamic modeling encounter significant difficulties
with real-time predictions, require enormous computational resources, and perform poorly in …

Mapping mangrove forests is crucial for their conservation, but it is challenging due to their
complex characteristics. Many studies have explored machine learning techniques that use …

cardiovascular diseases rank among the top causes of death around the world. Anticipating
cardiovascular illness is a major challenge for the healthcare industry. It has been …

The currently used standardised precipitation index (SPI) does not allow for a reliable
assessment of the impact of drought due to the small and unevenly distributed network of …

Accurate prediction of streamflow plays an important role in water resource management
and the continuous assessment of inundation susceptibility in the context of climate change …

Imbalanced datasets are one of the main issues encountered by artificial intelligence
researchers, as machine learning (ML) algorithms can become biased toward the majority …

The research and exploration of time series prediction (TSP) have attracted much attention
recently. Researchers can achieve effective TSP based on the deep learning model and a …