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Advisor(s)
Abstract(s)
This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size,
space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or
the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires
catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an
annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First,
we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering
algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional
Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived
to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships
among the data and to identify forest fire patterns.