Forthcoming superspectral satellite missions dedicated to land monitoring, as well as
planned imaging spectrometers, will unleash an unprecedented data stream. The …

Climate and weather variables such as rainfall, temperature, and pressure are indicators for
hazards such as tropical cyclones, floods, and fires. The impact of these events can be due …

Causal discovery seeks to recover cause–effect relationships from statistical data using
graphical models. One goal of this paper is to provide an accessible introduction to causal …

The application of machine learning to agriculture is currently experiencing a “surge of
interest” from the academic community as well as practitioners from industry. This increased …

In this paper we introduce a new type of climate network based on temporal probabilistic
graphical models. This new method is able to distinguish between direct and …

The exposure to sea-level rise (SLR) risks emerges as a challenging issue in the broader
debate about the possible consequences of global environmental change for at least four …

Understanding the causes and consequences of, and devising countermeasures to, global
warming is a profoundly complex problem. Network representations are sometimes the only …

Bayesian networks support the probabilistic failure analysis of complex systems, eg, dams
and bridges, needed for a better understanding of the system reliability and for taking …

In order to understand the impact of climatic and environmental changes as well as socio-
economic drivers on human migration, it remains a challenging task to find a method to …

The autonomous decision‐making of a UAV is based on rapid and accurate threat
assessment of the target. Accordingly, modeling of threat assessment under the condition of …