Machine Learning for Forest Fire Prediction: A Case Study in North Algeria
Bibliographic record
Abstract
Wildland fires are the most common peril for forests due to climate change.Furthermore, it is an uncontrollable disaster and poses a great deal of threat to human health and ecosystems.In Algeria, almost 40,000 hectares are burned each year, approximately 1% of all existing woodlands of the country.In this work, the forest fire event prediction is highlighted using machine learning.The study utilized data sets from several sources, including fire data obtained from the fire information system for resource management by NASA (FIRMS) and climate data accessed from the NASA energy project API, derived from the MODIS satellite (NASA forecasting of energy resources around the world).Fire data from NASA provides real-time information, spanning from 2000 to 2020.The methodology process of creating the prediction system involved collecting the data, preprocessing the data, finding the best models, training and testing the models, and evaluating them for validation.The machine learning model was trained and validated using 70% and 30% of the set features with a performance accuracy of up to 86%.Upon completion, we deployed our selected machine learning model to create a Web platform enables different end users to check possible future forest fires by select a geographical area on a world map.The objective of our machine learning model is to analyze the weather data of the selecting area on the map in real time and predict whether a fire will occur or not.This prediction system will enhance early detection, allowing prompt response measures to be implemented, reducing the risk of uncontrolled wildfires and safeguarding ecosystems and communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".