Fire! Firing Inductive Rules from Economic Geography for Fire Risk Detection
David Vaz, VĂtor Santos Costa and Michel Ferreira
June 2010
Abstract
Wildfires can importantly affect the ecology and economy of large
regions of the world. Effective prevention techniques are fundamental
to mitigate their consequences. The design of such preemptive methods
requires a deep understanding of the factors that increase the risk of
fire, particularly when we can intervene on these factors. This is the
case for the maintenance of ecological balances in the landscape that
minimize the occurrence of wildfires. We use an inductive logic
programming approach over detailed spatial datasets: one describing
the landscape mosaic and characterizing it in terms of its use; and
another describing polygonal areas where wildfires took place over
several years. Our inductive process operates over a logic term
representation of vectorial geographic data and uses spatial
predicates to explore the search space, leveraging the framework of
Spatial-Yap, its multi-dimensional indexing and tabling extensions. We
show that the coupling of a logic-based spatial database with an
inductive logic programming engine provides an elegant and powerful
approach to spatial data mining.
Bibtex
@InProceedings{vaz-ilp10,
author = {D. Vaz and V. Santos Costa and M. Ferreira},
title = {{Fire! Firing Inductive Rules from Economic Geography for Fire Risk Detection}},
booktitle = {Proceedings of the 20th International Conference on Inductive Logic
Programming (ILP 2010)},
editor = {P. Frasconi and F. A. Lisi},
month = {June},
year = {2010},
address = {Firenze, Italy},
}
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