An intelligent system for forest fire risk prediction and fire fighting management in Galicia

• Published in: Expert systems with applications 2003-11, Vol.25 (4), p.545-554
• Main Authors: Alonso-Betanzos, Amparo, Fontenla-Romero, Oscar, Guijarro-Berdiñas, Bertha, Hernández-Pereira, Elena, Inmaculada Paz Andrade, Marı́a, Jiménez, Eulogio, Luis Legido Soto, Jose, Carballas, Tarsy
• Format: Article
• Language: English
• Subjects: CommonKads; Fire risk prediction; Forest fires; Knowledge intensive systems; Neural Networks; Resources management
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/S0957-4174(03)00095-2

Over the last two decades in southern Europe, more than 10 million hectares of forest have been damaged by fire. Due to the costs and complications of fire-fighting a number of technical developments in the field have been appeared in recent years. This paper describes a system developed for the region of Galicia in NW Spain, one of the regions of Europe most affected by fires. This system fulfills three main aims: it acts as a preventive tool by predicting forest fire risks, it backs up the forest fire monitoring and extinction phase, and it assists in planning the recuperation of the burned areas. The forest fire prediction model is based on a neural network whose output is classified into four symbolic risk categories, obtaining an accuracy of 0.789. The other two main tasks are carried out by a knowledge-based system developed following the CommonKADS methodology. Currently we are working on the trail of the system in a controlled real environment. This will provide results on real behaviour that can be used to fine-tune the system to the point where it is considered suitable for installation in a real application environment.