Abstract Dynamic Bayesian networks (DBNs) as an extension of traditional Bayesian
networks have recently been paid great concern to environmental modeling to capture …

Bayesian networks are a popular class of multivariate probabilistic models as they allow for
the translation of prior beliefs about conditional dependencies between variables to be …

Dam inflow prediction is important in terms of optimal water allocation and reduction of
potential risks of floods and droughts. It is necessary to select a suitable model to reduce …

With the recent development of computational intelligence (CI), data-driven models have
gained growing interest to be applied in various scientific disciplines. This paper aims at …

With the proliferation of sensor generated weather data, the data-driven modeling for
prediction of meteorological time series has gained increasing research interest in current …

With the advancement of telecommunications, sensor networks, crowd sourcing, and remote
sensing technology in present days, there has been a tremendous growth in the volume of …

Bayesian network has gained increasing popularity among the data scientists and research
communities, because of its inherent capability of capturing probabilistic information and …

Spatial time series prediction is one of the most fascinating areas of modern data science. It
has enormous application in various domains including environmental management …

This paper proposes a space-time model for prediction of meteorological time series data.
The proposed prediction model is based on a spatially extended Bayesian network …

This paper proposes a novel data-driven model (BESTED), based on spatial Bayesian
network with incorporated exponential smoothing mechanism, for predicting precipitation …