Special Issue on Stochastic Environmental Research and Risk Assessment (SERRA) , co-edited by researchers at IDYST. Papers in this issue include case studies from hydrology and hydromorphology, to geology, geomechanics, atmospheric phenomena and renewable energy, pathogenic viruses and associated diseases. Available in Open Access (see the Link to "SI Online ")
In a broader sense, Data Science is a discipline that combines domain expertise, computer science skills, mathematical and statistical algorithms to transform data into actionable knowledge allowing to support and validate decisions as well as performing predictions. While this new paradigm has found widespread application in a variety of scientific domains, progresses in geo-environmental sciences have been sluggish and have had a limited impact on our understanding of environmental, climatic, and social processes.
Spatiotemporal data science will have a crucial role in addressing many of the great environmental and social challenges of the XXI century and of the other centuries to come. In this context, this special issue, edited by reseacrhers at IDYST, contributes to the open discussion on the methodological advances needed to analyze complex spatiotemporal processes. At the same time, several applied case studies highlight the potential of spatiotemporal data science in giving reliable solutions to real-world problems.