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.
Over the last two decades in southern Europe, more than 10 million hectares of forest have been damaged by fire. Each annual fire-fighting season incurs significant costs, measurable principally in terms of loss of human life, investment in fire-fighting resources, damage to the environment and the cost of recuperating the affected areas. However, the costs and complications of fire-fighting make it impractical to simultaneously maintain active fire-fighting units in various parts of a country. Recent years, therefore, have seen a number of technical developments in the field, aimed at improving communications networks, detection systems and fire prediction systems design. However, due to differing conditioning factors (vegetation type, climate, soil composition, orography, etc), it is not feasible to adopt general solutions or to adapt solutions developed for specific regions or countries.
This paper describes a system developed for the region of Galicia in NW Spain (Fig. 1), one of the regions of Europe most affected by fires. During the 1990s, for example, although it represents a mere 5.8% of the surface area of Spain, Galicia alone accounted for around 50% of all forest fires in that country. Moreover, in the same period the number of forest fires continued to grow despite an increase in the human and financial resources allocated to fire-fighting (Merida, 2002).
The system developed in this work fulfills three main aims, as follows:
1.It predicts forest fire risks and therefore acts as a crucial preventive tool by permitting fire-fighting units to focus on areas with the highest fire risk.
2.It backs up the forest fire monitoring and extinction phase.
3.It assists in planning the recuperation of the burned areas.
The above aims are achieved, from a technical point of view, using artificial neural networks and expert systems.
Our article is organised as follows: Section 2 provides a brief background analysis; 3 and 4 describe, respectively, the fire prediction module and the subsystem for fire management and recuperation of the affected areas; Section 5 describes the overall architecture and additional features of the system; finally, 6 and 7 discuss, respectively, the results obtained and our conclusions.
This paper describes an intelligent system for management and control of fire-fighting actions from beginning to end to be applied in Galicia. A rule-based system supports decision-making in the organization of fire-fighting actions and the recuperation of the affected area. It is based largely on meteorological and geographical data, and the decisions are guided by a need to minimise costs in terms of human life and the loss of natural resources. The CommonKADS methodology was used to develop the system, which required significant work on the structuring of domain knowledge. It will also facilitate future extensions and improvements.
The system also includes a forest fire prediction model based on a neural network and using meteorological data as the basis for assessing fire risk. The prediction obtained acceptable results using real data, bearing in mind that an intrinsic level of error that cannot be reduced occurs as a consequence of the significant number of fires that are deliberately provoked in this area.
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.
