Predicting Wildfires Propositional and Relational Spatio-Temporal Pre-processing Approaches

dc.contributor.author Mariana Rafaela Oliveira en
dc.contributor.author Luís Torgo en
dc.contributor.author Vítor Santos Costa en
dc.date.accessioned 2017-12-12T13:27:50Z
dc.date.available 2017-12-12T13:27:50Z
dc.date.issued 2016 en
dc.description.abstract We present and evaluate two different methods for building spatio-temporal features: a propositional method and a method based on propositionalisation of relational clauses. Our motivating application, a regression problem, requires the prediction of the fraction of each Portuguese parish burnt yearly by wildfires - a problem with a strong socio-economic and environmental impact in the country. We evaluate and compare how these methods perform individually and combined together. We successfully use under-sampling to deal with the high skew in the data set. We find that combining the approaches significantly improves the similar results obtained by each method individually. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/3909
dc.identifier.uri http://dx.doi.org/10.1007/978-3-319-46307-0_12 en
dc.language eng en
dc.relation 6110 en
dc.relation 4982 en
dc.relation 5129 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Predicting Wildfires Propositional and Relational Spatio-Temporal Pre-processing Approaches en
dc.type conferenceObject en
dc.type Publication en
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