Mutli-Modality Federate Learning with Multi-Source Data for Forest Fire Prediction
Bibliographic record
Abstract
Forest fires result in significant destruction of natural resources and human lives.They are commonly caused by humans or naturally occurring phenomena like lightning strikes.They may start due to lightning when necessary climatic conditions prevail for a the fire to ignite.The available forest fire data contains both fire and non-fire data and is highly imbalanced.To overcome this, the thesis provides a spatio-temporal agnostic subsampling (STAS) framework to subsample the highly imbalanced forest fire data.The proposed framework also works with limited variations between fire and non-fire data.Apart from lightning, weather and drought conditions are also significant factors in forest fires.Forest fire prediction which does not take these into consideration, may not be accurate.Therefore, the thesis is also focused on building and applying a multi-modality modeling approach for predicting forest fires.The multi-modality model is built using available datasets on forest fires, lightning, weather, and hydrometrics to predict the probability of forest fires.Using the proposed STAS framework and multi-modality model, the research aimed to generate a generalized model by applying multi-modality federated learning to predict forest fires independent of geographic location without the need for calibration.This research was able to successfully work with federated learning framework and multi-modality model to obtain high F 1Score (> 0.9) and R 2 Score (> 0.8).Technology for their inputs during the research.I also acknowledge Environment Canada (EC) for the data used in the research.This research would not have been successful or possible without the data obtained from them.The encouragement I received from my family and friends is unforgettable.I thank each one of them.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".