A multilevel graph approach for rainfall forecasting: A preliminary study case on London area

Increasing populations and rapid large-scale urbanization has created a demand to increase the quality of life through economic development, social stability, and better quality environments. These issues are addressed in the field of Smart Cities where, through the Internet of Things, efforts are being made to support added-value services for the administration of the city and for citizens. The continuous exchange of information inevitably produces a huge amount of data, which demands analyses of data using unconventional methods within a Big Data context. How can we properly process these data? How can we properly use these data in order to increase the competitiveness and efficiency of services, and how could they contribute to social development? Services that could be useful in this field include Early Warning Systems. Information management environments, or more generally pervasive data contexts, may be supported by context representation approaches and enhanced through adopting probabilistic approaches such as Context Dimension Tree, Ontology, and Bayesian Network. The aim of this work is to introduce and explain a methodology for merging CDTs and Ontologies, and probabilistic approach based on BNs in order to help expert users handle emergencies and provide suggestions for improving the liveability of cities for their inhabitants.