A comprehensive survey of the machine learning pipeline for wildfire risk prediction and assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wildfires, intensified by climate change and human activity, present a growing global threat to ecosystems, economies, and public safety. This survey offers a comprehensive overview of machine learning approaches for wildfire risk prediction and assessment, encompassing the entire pipeline from data acquisition to model deployment. It highlights the integration of diverse data sources, including remote sensing, in-situ measurements, geospatial layers, and historical fire records and outlines pre-processing and feature engineering techniques to represent climatic, topographic, vegetation, anthropogenic, and temporal fire patterns. The paper categorizes a wide array of machine learning techniques applied in wildfire risk assessment, including traditional, deep learning, spatial, temporal, reinforcement learning, and hybrid approaches. It also examines post-processing strategies such as fire susceptibility mapping and uncertainty quantification. Key challenges, including data sparsity, interpretability, and integration of heterogeneous data, are discussed alongside prospects for ethical, adaptive, and real-time systems. By organizing the literature into a unified end-to-end pipeline, this work offers guidance for developing scalable, interpretable, and operational machine learning solutions for wildfire risk assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it