Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …

Despite major improvements in weather and climate modelling and substantial increases in
remotely sensed observations, drought prediction remains a major challenge. After a review …

Recent breakthroughs in artificial intelligence (AI), and particularly in deep learning (DL),
have created tremendous excitement and opportunities in the earth and environmental …

Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming
industry applications and generating new and improved capabilities for scientific discovery …

These days, food safety is getting more attention than in the recent past due to consumer
awareness, regulations, and industrial competition to offer best quality products. Meat and …

Accurate and timely precipitation estimates are critical for monitoring and forecasting natural
disasters such as floods. Despite having high-resolution satellite information, precipitation …

A deep learning convolutional neural network model is used to explore the possibilities of
estimating tropical cyclone (TC) intensity from satellite images in the 37-and 85–92-GHz …

A common post-processing approach to improve precipitation forecasts is to use machine
learning models such as artificial neural networks (more specifically, multi-layer …

Near-real-time precipitation estimates based on geostationary meteorological satellites are
of great importance for monitoring hydrology-related disasters. Currently, the contributions of …

Hydrology is the science of studying the natural flow of water and the effect of human activity
on the water. Hydrological modeling is essential for the management and conservation of …