Forest Fire Analysis Prediction and Digital Twin Verification: A Trinity Framework and Application
Abstract
1. Introduction
- Decoupling analysis phase: To address the challenge of distinguishing the characteristics of various influencing factors, this study engages in a multi-factor decoupling analysis. By integrating PCA and SHAP, this study develops a framework termed “factor screening-importance ranking-interpretation analysis.” This framework is designed to elucidate the impact of different factors on forest fire occurrence more clearly.
- Model prediction phase: The research introduces a pioneering approach to spatiotemporal decoupling in model prediction. This involves resolving the issue of spatial coupling of multiple factors through PCA and addressing the time-delay effects using the ALSTM time-series prediction model. By decoupling the spatial and temporal data sources, this method enhances the accuracy of forest fire predictions.
- Scenario validation phase: Employing digital twin technology, this paper constructs a three-dimensional model that dynamically analyzes the physical mechanisms and quantitative relationships of key meteorological and combustible indices affecting forest fire behavior. This model addresses the limitations of traditional one-dimensional and two-dimensional static simulations and the challenges faced by conventional machine learning models in field testing.
2. Related Research
2.1. Research on Influencing Factors
2.2. Current Status of Forest Fire Prediction
2.3. Three-Dimensional Fire Modeling
3. Overview
4. Materials and Methods
4.1. Data Preparation
4.2. Multi-Factor Decoupling Analysis
4.2.1. Principal Component Analysis
4.2.2. Interpretability Analyses
4.3. Forest Fire Prediction Model
4.3.1. Model Architecture Design
- (1)
- Core time-series feature extraction: This study employs an LSTM layer to capture the long-term dependencies within the sequence data. The layer’s gating mechanisms—comprising a forget gate, an input gate, and an output gate—effectively model the temporal dynamics associated with fire-related factors.
- (2)
- Attention fusion: A Multiply layer is introduced to achieve a weighted fusion between attention weights and LSTM hidden states, thereby emphasizing key factors driving forest fires.
- (3)
- Efficiency and generalization: To reduce the dimensionality and, consequently, the number of parameters and computational overhead, this study utilizes a GlobalAveragePooling1D layer. Additionally, a Dropout layer with a 0.3 inactivation rate is incorporated to effectively mitigate the risk of overfitting, thus enhancing the model’s ability to generalize across diverse and variable forest environments.
- (4)
- Probabilistic output: The model concludes with a layer utilizing a Sigmoid activation function, which outputs the probability of a forest fire occurrence within a range of [0, 1].
4.3.2. Key Parameter Optimization Experiments
- Time Step Optimization
- 2.
- Learning Rate Optimization
- 3.
- Optimizer Selection
4.4. Mountain Fire Scenario Evolution Model
4.4.1. Physical Driving Equations
4.4.2. Data Preparation for Twin Model Construction
5. Results and Analyses
5.1. Results of Decoupling Analyses
5.1.1. Principal Component Analysis Results
5.1.2. Analysis of Variable Contributions
5.2. Model Prediction Results
5.2.1. Improvement of Training Effect by Feature Decoupling
5.2.2. Comprehensive Model Accuracy Assessment
5.3. Verification of Simulation Results
6. Conclusions
- (1)
- The model training depends on data from specific climatic zones in Algeria, exhibiting strong regional specificity. Its generalizability to broader forested areas remains to be verified.
- (2)
- The physical-spatial processes under the coupled interactions of the influencing factors are highly complex. The study constructs a mountain fire scenario evolution model based on a simplified hypothetical dynamics module, which restricts the precision of quantitative fire behavior simulations and limits the capacity to fully capture the impacts of extreme weather events.
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Considerations | Meteorological Factor | Combustibility Factor | Topographic Factor | Anthropogenic Factors | Vegetation Factor | Derivatives Index (FWI System) |
---|---|---|---|---|---|---|
Impact factor | Temperature | Fine Fuel Moisture Code (FFMC) | Slope | Ignition Sources | Normalized Differential Vegetation Index (NDVI) | Initial Spread Index (ISI) |
Relative Humidity (RH) | Duff Moisture Code (DMC) | Slope direction | Fire Management | vegetation type | Buildup Index (BUI) | |
Wind Speed (WS) | Drought Code (DC) | Elevation | Population Density | vegetation cover | Fire Weather Index (FWI) | |
Rainfall (Rain) | Type of combustible | Topography Complexity | Distance from the river | vegetative diversity | ||
Atmospheric Pressure | Fuel Load | terrain roughness | Distance from road | vegetation |
Layer Name | Typology | Parameters/Settings | Output Shape Extrapolation | Function |
---|---|---|---|---|
input_layer | input layer | - | (None, 1, 7) | Receives preprocessed time-series data after PCA dimensionality reduction |
LSTM | LSTM layer | Units = 64, return_sequences = True, Activation = tanh | (None, 1, 64) | Captures sequential dependencies and extracts temporal features via gating mechanisms |
Dense_1 | full connectivity layer | Units = 1, Activation = tanh | (None, 1, 1) | Computes raw attention weight scores |
Activation | activation layer | Activation = softmax | (None, 1, 1) | Normalizes attention weights into a probability distribution |
Multiply | element-by-element multiplication | - | (None, 1, 64) | Applies attention weights to LSTM outputs, emphasizing key features |
GlobalAveragePooling1D | Global average pooling | - | (None,64) | Reduces temporal dimension while preserving feature information |
Dense_2 | full connectivity layer | Units = 64, Activation = relu | (None,64) | Performs nonlinear transformation and advanced feature mapping |
Dropout | stochastic inactivation | Rate = 0.3 | (None,64) | Randomly deactivates 30% of neurons to prevent overfitting |
Dense_3 | input layer | Units = 1, Activation = sigmoid | (None, 1) | Produces binary classification probability for fire occurrence |
Optimiser | Accuracy | Precision | Recall |
---|---|---|---|
Adam | 96.72% | 0.9983 | 0.9691 |
SGD | 96.10% | 0.9167 | 0.9856 |
RMSprop | 96.56% | 0.9248 | 0.9741 |
Adagrad | 96.45% | 0.9156 | 0.9826 |
Nadam | 96.06% | 0.9211 | 0.9778 |
State of Affairs | Trigger Condition | Interaction Events and Scene Effects |
---|---|---|
Unburned state | Environmental parameters do not satisfy combustion conditions and topographic “block” impedes combustion equation | Vegetation retains its normal textures and colors; no flame particles are generated, resulting in no smoke effect in the scene; the three-dimensiona model is displayed as a static natural environment. |
Open flame state | Environmental parameters satisfy combustion conditions, and no topographic “block” impedes combustion equation | Activation of the flame particle system occurs, the flame model expands as temperature rises, and smoke particles are generated concurrently; the vegetation model dynamically transitions to a burning texture, enhanced by particle effects such as sparks and splashes. |
extinguished state | Environmental parameters no longer satisfy combustion conditions or topographic “block” impedes combustion equation | Flame particles gradually decay and vanish, and smoke concentration diminishes; the vegetation model updates to a “burnt” texture, retaining indications of possible rekindling if combustible material remains unburnt. |
Ingredient | Explanation of Variance (%) | Cumulative Variance (%) | Main Environmental Impacts |
---|---|---|---|
PCA1 | 52.61 | 52.61 | FWI, FFMC, DMC, ISI, BUI |
PCA2 | 15.93 | 68.54 | Ws, DC |
PCA3 | 9.47 | 78.01 | Rain, RH |
PCA4 | 7.35 | 85.36 | Ws, Temperature |
PCA5 | 6.04 | 91.40 | Region |
Model | Precision | Accuracy | Recall | F1 | PR-AUC |
---|---|---|---|---|---|
ALSTM | 0.9782 (±0.0237) | 0.9571 (±0.0213) | 0.9461 (±0.0373) | 0.9612 (±0.0196) | 0.9945 (±0.0048) |
LSTM | 0.9634 (±0.0434) | 0.9449 (±0.0205) | 0.9398 (±0.0402) | 0.9499 (±0.0164) | 0.9926 (±0.0068) |
LR | 0.9865 (±0.0208) | 0.9224 (±0.0238) | 0.8710 (±0.0396) | 0.9246 (±0.0243) | 0.9893 (±0.0087) |
Metric | Comparison | Mean Difference | 95% CI Lower | 95% CI Upper | p-Value |
---|---|---|---|---|---|
Accuracy | ALSTM vs. LSTM | 0.012244898 | 0.001859192 | 0.026348988 | 0.001126189 * |
Accuracy | ALSTM vs. LR | 0.034693878 | 0.017766171 | 0.051621584 | 0.001225564 * |
Precision | ALSTM vs. LSTM | 0.014776261 | 0.009789991 | 0.039342513 | 0.002718957 * |
Precision | ALSTM vs. LR | −0.008263151 | 0.006996895 | 0.010470593 | 0.004445289 * |
Recall | ALSTM vs. LSTM | 0.006283075 | −0.011413825 | 0.023979976 | 0.442580109 |
Recall | ALSTM vs. LR | 0.07508576 | 0.045020052 | 0.105151469 | 0.000313807 * |
F1 Score | ALSTM vs. LSTM | 0.011346472 | 0.00163506 | 0.024328005 | 0.00941261 * |
F1 Score | ALSTM vs. LR | 0.036625092 | 0.01861564 | 0.054634543 | 0.001289685 * |
PR-AUC | ALSTM vs. LSTM | 0.001909956 | 0.000994991 | 0.004814904 | 0.001099468 * |
PR-AUC | ALSTM vs. LR | 0.005237726 | 0.000113348 | 0.010362104 | 0.046070699 * |
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Li, W.; Zai, W.; Fan, W.; Tang, Y. Forest Fire Analysis Prediction and Digital Twin Verification: A Trinity Framework and Application. Forests 2025, 16, 1546. https://doi.org/10.3390/f16101546
Li W, Zai W, Fan W, Tang Y. Forest Fire Analysis Prediction and Digital Twin Verification: A Trinity Framework and Application. Forests. 2025; 16(10):1546. https://doi.org/10.3390/f16101546
Chicago/Turabian StyleLi, Wenyan, Wenjiao Zai, Wenping Fan, and Yao Tang. 2025. "Forest Fire Analysis Prediction and Digital Twin Verification: A Trinity Framework and Application" Forests 16, no. 10: 1546. https://doi.org/10.3390/f16101546
APA StyleLi, W., Zai, W., Fan, W., & Tang, Y. (2025). Forest Fire Analysis Prediction and Digital Twin Verification: A Trinity Framework and Application. Forests, 16(10), 1546. https://doi.org/10.3390/f16101546