No abstract

A constant and controlled level of emission of carbon and other gases into the atmosphere is a pre-condition for preventing global warming and an essential issue for a sustainable world. Fires in the natural environment are phenomena that extensively increase the level of greenhouse emissions and disturb the normal functioning of natural ecosystems. Therefore, estimating the risk of fire outbreaks and fire prevention are the first steps in reducing the damage caused by fire. In this study, we build predictive models to estimate the risk of fire outbreaks in Slovenia, using data from a GIS, Remote Sensing imagery and the weather prediction model ALADIN.The study is carried out on three datasets, from three regions: one for the Kras region, one for the coastal region and one for continental Slovenia. On these datasets, we apply both classical statistical approaches and state-of-the-art data mining algorithms, such as ensembles of decision trees, in order to obtain predictive models of fire outbreaks.Responsible editor: Katharina Morik, Kanishka Bhaduri and Hillol Kargupta. This paper has its origins in a project report ) and a short conference paper (Stojanova et al. 2006) that introduced the problem of forest fire prediction in Slovenia, using GIS, RS and meteorological data. However, this paper significantly extends and upgrades the work presented there. In particular: We consider a wider set of data mining techniques, from single classifiers to ensembles; We present a comparison of the predictive performance in terms of several frequently used evaluation measures for classification; We present an example of the results obtained from the modeling task in the form of decision rules, explain and interpret their meaning; We generate geographical maps and compare them with other fire prediction models (e.g., FWI fire risk danger maps) provided by other services.