The Scientific Bases of Sustainability: Methods, Measures and Correlations

Defining and assessing sustainability of complex systems (ecosystems, production systems, territorial systems, etc.) is a crucial challenge for modern science. Several instruments are necessary to answer a lot of questions related to the interactions between man and Nature. Policy makers, businessmen, researchers, managers, environmentalists and common people need information in order to understand what is sustainability and what is the distance of their behaviours from it. Sustainability indicators have been developed with the purpose to answer all these questions.The paper presents the results of the SPIn-Eco project, a sustainability analysis of the Province of Siena (Italy). It has produced a data set that allows a practical comparison among several approaches and indicators by means of correlation analysis. Important correlations were found between Ecological Footprint and CO2 emissions as well as with the non renewable exogenous part of Emergy flow. No correlation was found between total emergy flow and total ecological footprin

Prediction of severe thunderstorm events with ensemble deep learning and radar data

The problem of nowcasting extreme weather events can be addressed by applying either numerical methods for the solution of dynamic model equations or data-driven artificial intelligence algorithms. Within this latter framework, the most used techniques rely on video prediction deep learning methods which take in input time series of radar reflectivity images to predict the next future sequence of reflectivity images, from which the predicted rainfall quantities are extrapolated. Differently from the previous works, the present paper proposes a deep learning method, exploiting videos of radar reflectivity frames as input and lightning data to realize a warning machine able to sound timely alarms of possible severe thunderstorm events. The problem is recast in a classification one in which the extreme events to be predicted are characterized by a an high level of precipitation and lightning density. From a technical viewpoint, the computational core of this approach is an ensemble learning method based on the recently introduced value-weighted skill scores for both transforming the probabilistic outcomes of the neural network into binary predictions and assessing the forecasting performance. Such value-weighted skill scores are particularly suitable for binary predictions performed over time since they take into account the time evolution of events and predictions paying attention to the value of the prediction for the forecaster. The result of this study is a warning machine validated against weather radar data recorded in the Liguria region, in Italy