Wildfire Susceptibility Prediction Based on a CA-Based CCNN with Active Learning Optimization
Abstract
:1. Introduction
- In order to capture the spatial distribution characteristics of pixels and the correlation between local regions in the fire-influencing factor raster map, we embed the CA module into the feature extraction network of CCNN in order to aggregate different location information into channels and thereby model the global information of the fire-influencing factor raster map;
- We adopt an active learning approach based on two types of complementary learning strategies in order to improve the network’s discriminative performance by prioritizing the selection of fire-influencing factor patches with high confidence. Compared with traditional learning methods, the model can achieve a balance of high prediction accuracy and low annotation cost;
- We adopt the CA-based CCNN to output the fire probability of fire-influencing factor raster patches. Instead of fixed input patches used in CNN, the proposed method allows different patch sizes without changing the structural parameters of the model, which contributes to improving the application flexibility of the wildfire susceptibility prediction model in different scenes.
2. Study Area and Data Sources
2.1. Study Area
2.2. Data Sources
3. Methods
3.1. Generation of Fire-Influencing Factor Raster Maps
3.2. The Model Input
3.3. The Structure of the CA-Based CCNN Model
3.4. Active Learning Training Strategy
4. Experiments
4.1. Implement Details
4.2. Metrics
5. Results
5.1. Ablation Study
5.2. Comparison with State-of-the-Art Machine Learning Methods
5.3. Wildfire Susceptibility Maps
5.4. Verification of Training Efficiency of Two Types of Complementary Learning Strategy
6. Discussions
6.1. Fire-Influencing Factors and Fire Susceptibility Prediction
6.2. Advantages of CCNN
6.3. The Basis for CA Module Selection
6.4. Labeling Cost
6.5. Wildfire Susceptibility Map
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Input Size | 15 × 15 | 25 × 25 | 35 × 35 | |
---|---|---|---|---|
Training | TP | 2005 | 2005 | 2000 |
TN | 1992 | 1993 | 1988 | |
FP | 2 | 1 | 6 | |
FN | 1 | 1 | 6 | |
Sensitivity (%) | 99.95 | 99.95 | 99.70 | |
Specificity (%) | 99.90 | 99.95 | 99.70 | |
PPV (%) | 99.90 | 99.95 | 99.70 | |
NPV (%) | 99.95 | 99.95 | 99.70 | |
Accuracy (%) | 99.93 | 99.95 | 99.70 | |
Validating | TP | 466 | 468 | 472 |
TN | 447 | 449 | 440 | |
FP | 59 | 57 | 66 | |
FN | 28 | 26 | 22 | |
Sensitivity (%) | 94.33 | 94.74 | 95.55 | |
Specificity (%) | 88.34 | 88.74 | 86.96 | |
PPV (%) | 88.76 | 89.14 | 87.73 | |
NPV (%) | 94.11 | 94.53 | 95.24 | |
Accuracy (%) | 91.30 | 91.70 | 91.20 |
Models | Ours | CNN-Based Method [18] | CCNN | CCNN + SE | CCNN + CBAM | |
---|---|---|---|---|---|---|
Training | TP | 2005 | 1992 | 2001 | 2000 | 1997 |
TN | 1993 | 1918 | 1978 | 1984 | 1983 | |
FP | 1 | 71 | 16 | 10 | 11 | |
FN | 1 | 19 | 5 | 6 | 9 | |
Sensitivity (%) | 99.95 | 99.06 | 99.75 | 99.70 | 99.55 | |
Specificity (%) | 99.95 | 96.43 | 99.20 | 99.50 | 99.45 | |
PPV (%) | 99.95 | 96.56 | 99.21 | 99.50 | 99.45 | |
NPV (%) | 99.95 | 99.02 | 99.75 | 99.70 | 99.55 | |
Accuracy (%) | 99.95 | 97.75 | 99.48 | 99.60 | 99.50 | |
Validating | TP | 468 | 437 | 464 | 463 | 460 |
TN | 449 | 429 | 436 | 448 | 444 | |
FP | 57 | 82 | 70 | 58 | 62 | |
FN | 26 | 52 | 30 | 31 | 34 | |
Sensitivity (%) | 94.74 | 89.37 | 93.93 | 93.72 | 93.12 | |
Specificity (%) | 88.74 | 83.95 | 86.17 | 88.54 | 87.75 | |
PPV (%) | 89.14 | 84.2 | 86.89 | 88.87 | 88.12 | |
NPV (%) | 94.53 | 89.19 | 93.56 | 93.53 | 92.89 | |
Accuracy (%) | 91.70 | 86.60 | 90.00 | 91.10 | 90.40 |
Models | Ours | CNN | RF | Decision Tree | MLP | SVM | |
---|---|---|---|---|---|---|---|
Training | TP | 2005 | 1992 | 2003 | 1695 | 1530 | 1611 |
TN | 1993 | 1918 | 1985 | 1742 | 1555 | 1558 | |
FP | 1 | 71 | 4 | 247 | 434 | 431 | |
FN | 1 | 19 | 8 | 316 | 481 | 400 | |
Sensitivity (%) | 99.95 | 99.06 | 99.60 | 84.29 | 76.08 | 80.11 | |
Specificity (%) | 99.95 | 96.43 | 99.80 | 87.58 | 78.18 | 78.33 | |
PPV (%) | 99.95 | 96.56 | 99.80 | 87.28 | 77.90 | 78.89 | |
NPV (%) | 99.95 | 99.02 | 99.60 | 84.65 | 76.38 | 79.57 | |
Accuracy (%) | 99.95 | 97.75 | 99.70 | 85.93 | 77.13 | 79.23 | |
Validating | TP | 468 | 437 | 353 | 331 | 346 | 358 |
TN | 449 | 429 | 379 | 382 | 396 | 382 | |
FP | 57 | 82 | 132 | 129 | 115 | 129 | |
FN | 26 | 52 | 136 | 158 | 143 | 131 | |
Sensitivity (%) | 94.74 | 89.37 | 72.19 | 67.69 | 70.76 | 73.21 | |
Specificity (%) | 88.74 | 83.95 | 74.17 | 74.76 | 77.50 | 74.76 | |
PPV (%) | 89.14 | 84.2 | 72.78 | 71.96 | 75.05 | 73.51 | |
NPV (%) | 94.53 | 89.19 | 73.59 | 70.74 | 73.47 | 74.46 | |
Accuracy (%) | 91.70 | 86.60 | 73.20 | 71.30 | 74.20 | 74.00 |
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Share and Cite
Yu, Q.; Zhao, Y.; Yin, Z.; Xu, Z. Wildfire Susceptibility Prediction Based on a CA-Based CCNN with Active Learning Optimization. Fire 2024, 7, 201. https://doi.org/10.3390/fire7060201
Yu Q, Zhao Y, Yin Z, Xu Z. Wildfire Susceptibility Prediction Based on a CA-Based CCNN with Active Learning Optimization. Fire. 2024; 7(6):201. https://doi.org/10.3390/fire7060201
Chicago/Turabian StyleYu, Qiuping, Yaqin Zhao, Zixuan Yin, and Zhihao Xu. 2024. "Wildfire Susceptibility Prediction Based on a CA-Based CCNN with Active Learning Optimization" Fire 7, no. 6: 201. https://doi.org/10.3390/fire7060201