A heuristic expert system for forest fire guidance in Greece

Area of Science:

• Environmental Science
• Computer Science
• Risk Management

Background:

• Greece faces a severe forest fire problem, necessitating effective forecasting to protect national wealth.
• Traditional statistical models (regression, time series) failed to accurately predict forest fire occurrences based on historical data.
• Qualitative factors like political stability and drought cycles significantly influence annual forest fires and burnt areas.

Purpose of the Study:

• To design and implement an Expert System for forest fire forecasting in Greece.
• To classify Greek prefectures into distinct forest fire risk zones.
• To develop a tool for effective forest fire prevention planning.

Main Methods:

• Developed an Expert System incorporating heuristics to handle uncertainty and qualitative factors.
• Utilized fuzzy logic to generate fuzzy expected intervals for each prefecture.
• Classified prefectures into risk zones by comparing these fuzzy intervals.

Main Results:

• The Expert System successfully classified Greek prefectures into forest fire risk zones.
• Testing for 1994 and 1995 demonstrated high prediction accuracy for the number of forest fires per prefecture.
• Achieved average accuracies of 85.25% (1995) and 80.89% (1994).

Conclusions:

• The developed Expert System is a valuable tool for forest fire forecasting and prevention planning in Greece.
• The system's ability to accurately predict fire risk based on qualitative factors offers significant advantages over traditional methods.
• Accurate forecasting enables proactive measures to mitigate forest fire destruction and preserve national resources.