Exploring the Relationships of Fire Occurrence Variables by Means of CART and MARS Models. Amatulli, G. & Camia, A. In Proceedings of the 4th International Wildland Fire Conference, Sevilla, Spain, 13-18 May 2007. ![Exploring the Relationships of Fire Occurrence Variables by Means of CART and MARS Models [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex Recently, in the framework of long-term fire risk assessment, researcher have implemented spatial and non-spatial non-parametric prediction models to discover complex relationships among wildfire variables. The main scope was to overcome the assumption of spatial stationarity in the relationship among the response variable and the predictors, assumed by the traditional regression techniques. The present article aims to test and compare the potential of the CART and MARS models in predicting fire occurrence at local scale. The test is performed in the Arno River Basin, a fire prone area located in the central part of Italy. Road network, topographic variables and population data were implemented to build up fire prediction model using 1621 ignition points recorded during the period 1997-2003. The models produce two prediction maps slightly similar. In general the CART model overperform compare to the MARS one. Nonetheless, the MARS model produces a smoothened surface that theoretically better follow the probability of a fire event.
@inproceedings{amatulliExploringRelationshipsFire2007,
title = {Exploring the Relationships of Fire Occurrence Variables by Means of {{CART}} and {{MARS}} Models},
booktitle = {Proceedings of the 4th {{International Wildland Fire Conference}}, {{Sevilla}}, {{Spain}}, 13-18 {{May}} 2007},
author = {Amatulli, Giuseppe and Camia, Andrea},
date = {2007},
url = {http://www.fire.uni-freiburg.de/sevilla-2007/contributions/doc/cd/SESIONES_TEMATICAS/ST4/Amatulli_Camia_ITALY.pdf},
abstract = {Recently, in the framework of long-term fire risk assessment, researcher have implemented spatial and non-spatial non-parametric prediction models to discover complex relationships among wildfire variables. The main scope was to overcome the assumption of spatial stationarity in the relationship among the response variable and the predictors, assumed by the traditional regression techniques. The present article aims to test and compare the potential of the CART and MARS models in predicting fire occurrence at local scale. The test is performed in the Arno River Basin, a fire prone area located in the central part of Italy. Road network, topographic variables and population data were implemented to build up fire prediction model using 1621 ignition points recorded during the period 1997-2003. The models produce two prediction maps slightly similar. In general the CART model overperform compare to the MARS one. Nonetheless, the MARS model produces a smoothened surface that theoretically better follow the probability of a fire event.},
isbn = {978-84-8014-690-6},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-11921600,comparison,complexity,environmental-modelling,forest-fires,italy,modelling,regression,wildfires}
}
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