[A prediction model for forest fire-burnt area based on meteorological factors]

Area of Science:

• Forestry science
• Environmental science
• Statistical modeling

Context:

• Forest fires pose a significant threat to ecosystems and resources in Heilongjiang Province.
• Understanding temporal and spatial patterns of forest fires is crucial for effective management.
• Meteorological factors are known to influence fire ignition and spread.

Purpose:

• To analyze the occurrence patterns of forest fires in Heilongjiang Province.
• To develop a predictive model for forest fire-burnt area based on meteorological data.
• To identify key months with higher probabilities of large fire occurrences in different forest types.

Summary:

• Statistical analysis revealed distinct seasonal patterns for forest fires in Larix gmelinii and broadleaved Korean pine forest regions.
• A prediction model incorporating average wind speed, relative humidity, and mean temperature was established.
• The model identified March, May, and June as high-risk months for L. gmelinii forests, and May, March, and April for broadleaved Korean pine forests, with an average precision of 63.3%.

Impact:

• The developed model provides a valuable tool for predicting forest fire burnt areas.
• Findings can inform proactive fire prevention and management strategies in Heilongjiang Province.
• Improved prediction capabilities can aid in resource allocation and mitigation efforts for forest fires.