Dataset Creation and Imbalance Mitigation in Big Data: Enhancing Machine Learning Models for Forest Fire Prediction
Bibliographic record
Abstract
Historically, forest fire prediction methods have leaned on heuristics, local insights, and basic statistical models, often neglecting the complex interplay of variables such as temperature, humidity, wind speed, and vegetation type. The lack of real-time prediction capabilities, paired with unpredictable weather patterns attributed to climate change, underscores the shortcomings of traditional methods, especially in geographically varied regions like Canada. In contrast, machine learning provides the adaptability needed for real-time responses, effectively harnessing updated data and addressing region-specific forest fire risks. The shift towards machine learning is both a timely and revolutionary approach. \n \n \nThis research addresses the urgent need for effective forest fire prediction and management strategies, specifically in the Canadian context, by harnessing machine learning methodologies. Using Copernicus’s reanalysis data, this study establishes a comprehensive predictive framework employing four cutting-edge machine learning algorithms. Random Forest, XGBoost, LightGBM, and CatBoost. The study features a robust data pre-processing pipeline, class imbalance correction, and rigorous model evaluation measures. Key contributions include the creation of a feature-rich dataset, comprehensive methods for addressing the class imbalance in large scale datasets, and the development of a machine learning framework tailored for forest fire classification. The findings have significant implications for data-driven forest management strategies, with the aim of facilitating proactive fire prevention measures on a large scale. \n \n \nOne primary challenge encountered was the inherent class imbalance in fire classification datasets, with a striking 158:1 ratio between "non-fire" and "fire" events. To address this, the study utilized various re-sampling strategies, encompassing under-sampling, over-sampling, and hybrid techniques. Specific methods employed included NearMiss, SMOTE, and SMOTE-ENN. The NearMiss method with a 0.09 sampling ratio was found to be particularly effective in addressing this imbalance. When combined with NearMiss version 3 at a 0.09 ratio, the XGBoost model outperformed its peers, showcasing an accuracy of 98.08%, a sensitivity of 86.06%, and a specificity of 93.03%. The findings indicate that while high recall from NearMiss Version 3 optimized sensitivity, there was sometimes a trade-off with precision.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".