An intelligent system for forest fire risk prediction and fire fighting management in Galicia
Introduction
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 The forest fire risk prediction subsystem, 4 The fire management subsystem 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 Discussion and future work, 7 Conclusions discuss, respectively, the results obtained and our conclusions.
Section snippets
Background
Developed countries currently avail of well structured organisations, programmes and protocols for fighting forest fires, a fact that undoubtedly facilitates the application of new technologies in the domain.
The forest fire domain is an ideal one in which to apply intelligent systems. A large part of the domain knowledge is to be found in procedural models and written material; the remaining knowledge resides in practical accumulated experience that can be captured using knowledge engineering
The forest fire risk prediction subsystem
The aim of this sub-system (Alonso-Betanzos et al., 2002) is to calculate a numeric daily forest fire risk index for each of the 360 10×10 km2 squares into which the map of Galicia is divided by the Zone 29 of the UTM (Universal Transverse Mercator). Before this index is presented to the user, however, it will need to be classified in terms of one of four symbolic risk categories: low, medium, high and extreme.
The basis for this subsystem is historical information on fires that have occurred
The fire management subsystem
The development of a software platform for fire management and recuperation of the burned areas requires a methodical structuring of the knowledge specific to the fire-fighting organisation for which the system is being developed. The system described here, which manages fires from beginning to end, includes a series of features that will assist decision making in terms of the organisation of the resources to be mobilised.
With a view to developing a system that can be easily maintained, the
System architecture
The tasks described form part of a more ambitious project that includes the components depicted in Fig. 7; these are:
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Extern Data Access
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Online acquisition of meteorological data: an independent module designed to obtain data, via the Internet, from the automatic meteorological stations in Galicia (marked in Fig. 1). These data are used both by the prediction system and the fire management system.
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Databases: these store information on previous fire control actions, the environment, records of
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Discussion and future work
The system described in this article integrates three tasks that are crucial to the fight against forest fires: prediction of fires, management of fire extinction resources and recuperation of the affected areas.
Regarding to the Prediction System, comparing the performance measures provided in Table 2, it can be observed that the generalisation capacities of the network are maintained when evaluated over a set of cases not used in its development. This fact can also be observed in the ROC
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
Acknowledgements
This research has been funded by the European Regional Development Fund (ERDF) project 1FD97-1122-C06-01 and by the Spanish Comisión Interministerial de Ciencia y Tecnologı́a (CICYT) under project REN-2001-3216-CO4-01.
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