Numerous deep learning architectures have been developed to accommodate the diversity
of time-series datasets across different domains. In this article, we survey common encoder …

In recent years, we have witnessed a significant shift toward ever-more complex and ever-
larger-scale systems in the majority of the grand societal challenges tackled in applied …

In recent decades, the warming in the Arctic has been much faster than in the rest of the
world, a phenomenon known as Arctic amplification. Numerous studies report that the Arctic …

Abstract Multi-Model Ensembles (MMEs) are used for improving the performance of GCM
simulations. This study evaluates the performance of MMEs of precipitation, maximum …

Ocean warming and acidification, decreases in dissolved oxygen concentrations, and
changes in primary production are causing an unprecedented global redistribution of marine …

It is commonplace to encounter heterogeneous data, of which some aspects of the data
distribution may vary but the underlying causal mechanisms remain constant. When data are …

Artificial intelligence methods and application have recently shown great contribution in
modeling and prediction of the hydrological processes, climate change, and earth systems …

Ingestion of polycyclic aromatic hydrocarbons (PAHs) contaminated water has potential
human-health and ecological consequences. A comprehensive investigation of PAHs' …

Deep sequential (DS) models are extensively employed for forecasting time series data
since the dawn of the deep learning era, and they provide forecasts for the values required …

Studies that evaluate climate change projections over the whole of South America (SA) and
including different seasons and models are scarce. In this context, the objective of this work …