Wavelet Based Machine Learning Algorithms for Wildfire Prediction
Bibliographic record
Abstract
It is estimated that there are 70,000 wildfires every year in the United States alone. These wildfires pose a significant danger to humans, both from property damage and health implications; air quality can drastically decrease from the resulting smoke, leading to over 33000 premature deaths each year [1]. As a result, it is imperative that improvements to wildfire prediction models be made. Previous research has demonstrated the effectiveness of Machine Learning (ML) methods such as XGBoost and Convolutional Neural Network (CNN). In this research, we utilize wavelet analysis to preprocess our data by applying a discrete wavelet transform (DWT) in order to increase the accuracy of our models in predicting the next day's wildfire danger in certain counties in California. We input this DWT transformed data into an XGBoost model and compare the model performance to a traditional model (one trained on non-DWT data). We also apply DWT on aerial images data set from Canada and input the results into a convolutional neural network (CNN) to investigate the effects of DWT transformed image data and to determine if such wavelet based CNN can accurately distinguish between days with and days without a wildfire. We find that the use of DWT significantly improves model performance by denoising the data as well as adding more information in the form of hidden features for the model to train on.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".