This paper elaborates on the Random Network Model (RNM) as a mathematical framework for modelling and analyzing the generation of complex networks. Such framework allows the analysis of the relationship between several network characterizing features (link density, clustering coefficient, degree distribution, connectivity, etc.) and entropy-based complexity measures, providing new insight on th...

This paper explores the utility of an ensemble decision-tree method called random forest, in comparison with the classic classification and regression trees (CART) algorithm, for forecasting ground-level ozone pollution in the Sydney metropolitan region. Statistical forecasting models are developed to provide daily ozone forecasts in November-March for three subregions, i.e., Sydney east, Sydne...