As the role played by statistical and computational sciences in climate and environmental modelling and prediction becomes more important, Machine Learning researchers are becoming more aware of the relevance of their work to help tackle the climate crisis. Indeed, being universal nonlinear function approximation tools, Machine Learning algorithms are efficient in analysing and modelling spatially and temporally variable environmental data. While Deep Learning models have proved to be able to capture spatial, temporal, and spatio-temporal dependencies through their automatic feature representation learning, the problem of the interpolation of continuous spatio-temporal fields measured on a set of irregular points in space is still under-investigated. To fill this gap, we introduce here a framework for spatio-temporal prediction of climate and environmental data using deep learning. Specifically, we show how spatio-temporal processes can be decomposed in terms of a sum of products of temporally referenced basis functions, and of stochastic spatial coefficients which can be spatially modelled and mapped on a regular grid, allowing the reconstruction of the complete spatio-temporal signal. Applications on two case studies based on simulated and real-world data will show the effectiveness of the proposed framework in modelling coherent spatio-temporal fields.

For more than a decade, the European Union recognizes soil as a common good and considers it as a finite resource of inestimable value. The European Union defines it as the “upper layer of earth’s crust, formed by mineral particles, organic matter, water, air and living organisms”. Despite such definitions, usually, planning choices do not take into account the need to reduce soil consumption to build up resilience. This paper presents the controversial case of Agri Valley (Basilicata, Southern Italy); on the one hand, this region is characterized by the presence of extremely valuable land, because of the exceptional degree of soil fertility; on the other hand, Valdagri is also known to have one of the largest oilfields of Europe. An application built around the SLEUTH model was developed in order to produce a simulation and an estimate of the extent to which urban areas may grow in the near future. Results confirm that urban policies implemented so far by local governments—which aimed almost exclusively to favor industrial development—irreversibly threaten the integrity of the natural values of the valley.