SHAP-Driven Explainable Artificial Intelligence Framework for Wildfire Susceptibility Mapping Using MODIS Active Fire Pixels: An In-Depth Interpretation of Contributing Factors in Izmir, Türkiye
Abstract
:1. Introduction
- (1)
- Establishing a spatial ML framework for mapping wildfire susceptibility using well-known decision tree-based ensemble models, namely RF, XGBoost, and LightGBM, and identifying the best-performing model through comprehensive metrics evaluation.
- (2)
- Examining the relationship between the best model and conditioning factors through SHAP outputs by employing SHAP summary plots to derive the overall contribution of each factor to the prediction, providing a comprehensive view of factor importance and SHAP dependence plots to assess the isolated impact of individual factors on the model’s predictions, offering valuable insights into the influence of each contributing factor.
- (3)
- And exploring spatial variations in model outcomes to predict wildfire susceptibility across the study area.
2. Materials
2.1. Study Area
2.2. Wildfire Inventory
2.3. Conditioning Factors
3. Methodology
3.1. Feature Selection
3.2. ML Classifiers
3.2.1. Random Forest (RF)
3.2.2. eXtreme Gradient Boosting Machines (XGBoost)
3.2.3. Light Gradient Boosting Machine (LightGBM)
3.2.4. Hyperparameters Tuning
3.3. Performance Assessment
- True Positives (): The number of actual fires correctly identified as fires;
- True Negatives (): The number of non-fires correctly identified as non-fires;
- False Positives (): The number of non-fires incorrectly identified as fires;
- False Negatives (): The number of fires incorrectly identified as non-fires.
3.4. Explainability of Classifiers with SHAP Method
4. Results and Discussions
4.1. Multicollinearity Results
4.2. Tuned Hyperparameters
- RF: {n_estimators: 611}, {min_samples_split: 3}, {max_depth: 11}, {min_samples_leaf: 3};
- XGBoost: {n_estimators: 509}, {eta (learning_rate): 0.01145}, {max_depth: 12}, {subsample: 0.9854};
- LightGBM: {n_estimators: 399}, {eta (learning_rate): 0.01227}, {max_depth: 12}, {subsample: 0.90390}.
4.3. Classifier Performance
4.4. SHAP-Based Feature Importance (Summary Plots)
4.5. SHAP Dependence Plots
4.6. Generated Susceptibility Map
4.7. Contributions to the Community, Lessons Learned, and Limitations
- Firstly, the identification of critical factors through SHAP analysis and dependence plots elucidates their complex interactions in shaping fire ignition and spread dynamics.
- Secondly, these insights enable stakeholders to prioritize mitigation efforts. For instance, areas distant from villages require heightened attention due to higher fuel accumulation and longer response times for firefighting efforts. Conversely, the roads serve as a natural firebreak and facilitate quicker responses and interventions.
- Thirdly, adaptation strategies must consider regional characteristics. Factors like wind speed and solar radiance significantly amplify fire risk, necessitating tailored interventions such as firebreaks on steep slopes and vegetation management strategies near water bodies to mitigate moisture fluctuations and vegetation flammability.
- Furthermore, the variability in rainfall patterns highlights the necessity for continuous environmental monitoring to discern nuanced shifts in fire susceptibility. This adaptive approach will ensure timely adjustments in preventive measures to address evolving climatic conditions and their influence on fire behavior.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Factor Type | Sources and Resolution | Conditioning Factors | Min | Max | Mean |
---|---|---|---|---|---|
Topographical | SRTM DEM (30 m) | Elevation (m) | 0 | 2148 | 211 |
Slope (degrees) | 0 | 75.429 | 11.02 | ||
Aspect (degrees) | 0 | 360 | 114.3 | ||
Topographic Wetness Index (TWI) | 2.031 | 25.717 | 4.12 | ||
Climatic | WorldClim v.2.1 Database (~1 km) | Average Temperature (°C) | 7.6 | 17.8 | 15.1 |
Annual Rainfall (mm) | 46 | 74 | 58.8 | ||
Solar Radiation (kJ/m2 × day) | 0 | 17,959 | 17,346.9 | ||
Average Wind Speed (m/s) | 2.2 | 4.8 | 3.32 | ||
Anthropogenic | Open Street Maps | Distance to Streams (m) | 0 | 51,313 | 25,001.7 |
Distance to Roads (m) | 0 | 26,432 | 14,011.1 | ||
Distance to Villages (m) | 0 | 10,819 | 2012.6 | ||
CORINE 2018 | Land Use Land Cover (LULC) | - | - | - | |
Vegetation- related | Copernicus Land Monitoring Service (10 m) | Forest Type | - | - | - |
Tree Cover Density (%) | 0 | 100 | 34.2 | ||
Landsat 8 (30 m) | Normalized Difference Vegetation Index (NDVI) | −0.319 | 0.856 | 0.151 |
Metric | Formula |
---|---|
Sensitivity | |
Specificity | |
Accuracy | |
Precision | |
F1-score | |
AUC |
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Iban, M.C.; Aksu, O. SHAP-Driven Explainable Artificial Intelligence Framework for Wildfire Susceptibility Mapping Using MODIS Active Fire Pixels: An In-Depth Interpretation of Contributing Factors in Izmir, Türkiye. Remote Sens. 2024, 16, 2842. https://doi.org/10.3390/rs16152842
Iban MC, Aksu O. SHAP-Driven Explainable Artificial Intelligence Framework for Wildfire Susceptibility Mapping Using MODIS Active Fire Pixels: An In-Depth Interpretation of Contributing Factors in Izmir, Türkiye. Remote Sensing. 2024; 16(15):2842. https://doi.org/10.3390/rs16152842
Chicago/Turabian StyleIban, Muzaffer Can, and Oktay Aksu. 2024. "SHAP-Driven Explainable Artificial Intelligence Framework for Wildfire Susceptibility Mapping Using MODIS Active Fire Pixels: An In-Depth Interpretation of Contributing Factors in Izmir, Türkiye" Remote Sensing 16, no. 15: 2842. https://doi.org/10.3390/rs16152842
APA StyleIban, M. C., & Aksu, O. (2024). SHAP-Driven Explainable Artificial Intelligence Framework for Wildfire Susceptibility Mapping Using MODIS Active Fire Pixels: An In-Depth Interpretation of Contributing Factors in Izmir, Türkiye. Remote Sensing, 16(15), 2842. https://doi.org/10.3390/rs16152842