Deep Learning of Climate Change and Extreme Events, Hydrological Processes and Land Use Dynamics Relationships

The impact of climate change on hydrological processes, droughts, land use patterns, and ecosystem health is a critical area of research for understanding and managing the future of our planet. At the same time, changes in land use, agricultural methods, and population growth may contribute to climate change.

With its ability to process large datasets and identify hidden patterns, deep learning has provided new tools for analyzing complex environmental data and developing predictive models. These tools offer a promising avenue for advancing our potential response to environmental challenges.

This Special Issue brings together researchers from diverse fields, applying deep learning methods and new models to investigate climate change drivers and their impact on droughts, desertification, ice sheet melting, land use changes, and ecosystem service value. The goal is to enhance the capacity for predicting the interrelationships between the above-mentioned environmental components.