Tạp chí
 
Năm 2025
 
Số 66, Kỳ 2, [04 - 2025] - Tiếng Anh

Optimizing machine learning models for enhanced forest fire susceptibility mapping in Gia Lai province

DOI:10.46326/JMES.2025.66(2).02

  • Cơ quan:

    1 Thuyloi University, Hanoi, Vietnam
    2 Hanoi University of Mining and Geology, Hanoi Vietnam

  • *Tác giả liên hệ:
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  • Nhận bài: 24-10-2024
  • Sửa xong: 14-01-2025
  • Chấp nhận: 29-01-2025
  • Ngày đăng: 01-04-2025
Trang: 2 - 14
Lượt xem: 85
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Tóm tắt:

Forest fires pose significant risks to ecosystems, biodiversity, human health, and the economy, with escalating global impacts. In Vietnam, particularly during the dry season, the rising threat of forest fires necessitates accurate predictive models for effective prevention and management. This study advances forest fire susceptibility mapping in Gia Lai province by leveraging optimized machine learning models. We evaluated five models - Deep Neural Networks (DNN), Random Forest (RF), Gradient Boosting (GB), Logistic Regression (LR), and Support Vector Machines (SVM) - using a dataset of 2,827 fire incidents (2007÷2021), an equal number of non-fire points, and 12 influencing factors: slope, aspect, elevation, curvature, land use, NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), NDMI (Normalized Difference Moisture Index), temperature, wind speed, relative humidity, and rainfall. Among the models, RF outperformed others and was further optimized using Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Bayesian Optimization (BO). The Acc-GA-Opt-RF model (Accuracy-Optimized Random Forest using GA) achieved the best performance, with 84.4% accuracy, an AUC (Area Under the ROC Curve) of 0.9083, PPV (Positive Predictive Value) of 88.2%, NPV (Negative Predictive Value) of 81.2%, sensitivity of 79.3%, specificity of 89.4%, F-score of 0.8354, and Kappa of 0.687, demonstrating significant improvements over the unoptimized RF model. Factor importance analysis, employing Average Impurity Decrease (AID) and Permutation Feature Importance (PFI), identified NDVI and NDWI as key predictors, highlighting the critical role of vegetation indices in forest fire susceptibility. The optimized RF model was utilized to generate a forest fire susceptibility map categorizing the region into six risk levels, providing actionable insights for targeted fire prevention and management in Gia Lai province.

Trích dẫn
Hung Van Le, Duc Anh Hoang và Giang Truong Tran, 2025. Optimizing machine learning models for enhanced forest fire susceptibility mapping in Gia Lai province, Tạp chí Khoa học kỹ thuật Mỏ - Địa chất, số 66, kỳ 2, tr. 2-14.
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