Application of Explainable Artificial Intelligence in Predicting Wildfire Spread: An ASPP-Enabled CNN Approach
Bibliographic record
Abstract
Forest ecosystems have been persistently affected by wildfires, leading to significant damage worldwide. The severity and frequency of wildfires have escalated in recent years, necessitating more effective prediction models. This study presents an application of convolutional neural networks (CNNs) for wildfire spread prediction, focusing on the use of atrous spatial pyramid pooling (ASPP) mechanisms in these networks. However, the black-box nature of these algorithms has not been fully explored. To bridge this gap, we proposed an explainable CNN model with an ASPP mechanism (CNN-ASPP) in this study. More specifically, we utilize the Next Day Wildfire Spread dataset, which includes environmental variables, to evaluate the performance of our model. The proposed model is compared with state-of-the-art machine learning (ML) methods, including random forest (RF), support vector machine (SVM), artificial neural network (ANN), and another CNN model. Our results showed that CNN-ASPP achieved an F1-score of 97%, outperforming the ML methods with an F1-score of 90% for a neighborhood size of$7\times 7$. We also opened the black box and tried to explanation different convolutional layers based on the gradient-weighted class activation mapping (Grad-CAM) algorithm. Our findings indicate that larger dilation rates (DRs) can extract more meaningful features from the input data. This study contributes to the development of more transparent and accurate models for wildfire spread prediction, which could have significant implications for forest management and wildfire prevention strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".