In this article, we analyse the role that artificial intelligence (AI) could play, and is playing, to
combat global climate change. We identify two crucial opportunities that AI offers in this …

Earth system models (ESMs) are our main tools for quantifying the physical state of the Earth
and predicting how it might change in the future under ongoing anthropogenic forcing. In …

Developing countries have had numerous obstacles in diagnosing the COVID-19 worldwide
pandemic since its emergence. One of the most important ways to control the spread of this …

Deep learning has recently been utilized with great success in a large number of diverse
application domains, such as visual and face recognition, natural language processing …

Swarm intelligence techniques have been created to respond to theoretical and practical
global optimization problems. This paper puts forward an enhanced version of the firefly …

Clustering has primarily been used as an analytical technique to group unlabeled data for
extracting meaningful information. The fact that no clustering algorithm can solve all …

While global warming has generally increased the occurrence of extreme precipitation, the
physical mechanisms by which climate change alters regional and local precipitation …

In this paper, the performance of three machine-learning methods for predicting short-term
evolution and for reproducing the long-term statistics of a multiscale spatiotemporal Lorenz …

This study investigates how deep-learning can be configured to optimise the prediction of
2D maximum water depth maps in urban pluvial flood events. A neural network model is …

Numerical weather prediction models require ever‐growing computing time and resources
but, still, have sometimes difficulties with predicting weather extremes. We introduce a data …