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Updated: Jun 27, 2025

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Forest fire susceptibility assessment under small sample scenario: A semi-supervised learning approach using

Tianwu Ma1, Gang Wang2, Rui Guo3

  • 1Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China; School of Geography, Nanjing Normal University, Nanjing, 210023, China.

Journal of Environmental Management
|April 27, 2024
PubMed
Summary

Semi-supervised learning, using transductive support vector machines (TSVM), offers superior forest fire susceptibility assessment accuracy compared to traditional supervised methods when data is limited. This approach is crucial for effective environmental management and public safety.

Keywords:
Forest fireLimited sampleSemi-supervised learningSpatial predictionSupervised learningUnlabeled data

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Area of Science:

  • Environmental Science
  • Computer Science
  • Machine Learning

Background:

  • Forest fires pose significant threats to ecosystems, economies, and safety.
  • Accurate forest fire susceptibility assessment is vital for environmental management.
  • Traditional supervised learning methods require extensive labeled data, which is often unavailable.

Purpose of the Study:

  • To evaluate the effectiveness of semi-supervised learning, specifically transductive support vector machine (TSVM), for forest fire susceptibility assessment.
  • To compare TSVM performance against supervised learning methods in low-data scenarios.
  • To generate and assess forest fire susceptibility maps using limited sample data.

Main Methods:

  • Employed transductive support vector machine (TSVM), a semi-supervised learning algorithm.
  • Conducted comparative analysis against supervised learning methods like random forests.
  • Assessed prediction accuracy across various small sample sizes (4-32 samples) in Dayu County, China.

Main Results:

  • TSVM demonstrated higher prediction accuracy than supervised methods in limited sample scenarios.
  • At 4, 16, and 28 samples, TSVM achieved accuracies of ~0.8037, ~0.9257, and ~0.9583.
  • Supervised methods like random forests showed lower accuracies (e.g., ~0.7424, ~0.8916, ~0.9431 at the same sample sizes).

Conclusions:

  • Semi-supervised learning, particularly TSVM, is a promising approach for forest fire susceptibility mapping with limited data.
  • TSVM provides more reliable susceptibility maps compared to supervised methods under data scarcity.
  • The study highlights the potential of leveraging unlabeled data to improve environmental risk assessments.