Extreme Fire Severity Classification using Clustering and Decision Tree
Bibliographic record
Abstract
With climate change, large, unpredictable and difficult to suppress forest fires are increasingly frequent. To increase the ability to anticipate and respond to these extreme events it is necessary to characterize the meteorological conditions associated with the risk levels of these events. The main objective of this work is to identify those conditions characterizing extreme forest fires in Portugal in the period 2001-2020 with at least 100ha burned area (90% percentile). The conditions characterizing the extreme fires are elicited by applying unsupervised fuzzy clustering and predictive methods to forest fire data and corresponding fire risk indices, namely the Canadian Forest Fire Risk Index (FWI), and subindices, as well as the Continuous Haines Index (CHI), provided by the Portuguese Institute of Sea and Atmosphere (IPMA). The dates and localization of fires are obtained from the shapefiles provided by the Portuguese Institute for Nature Conservation and Forests (ICNF), and complemented with data from the MODIS Global Burned Area Product MCD64A1 downloaded from the University of Maryland repository. The unsupervised fuzzy clustering algorithm (fuzzy c-means) is used for data classification and segmentation, and of the predictive model (decision trees), for weather characterization and extraction of rules. The fuzzy c-means was used to segment the data into 5 or 7 clusters, and to each cluster it is assigned the fire risk scale class of the cluster’s prototype, respectively the EEFIS scale (European-Forest-Fire Information System) for 5 clusters and IPMA fire risk scale for 7 clusters. Using the data from the 2001-2018, decision trees were induced and tested with the data from 2019 and 2020. To ensure the quality of its results, metrics and validation techniques such as cross-validation and bootstrapping are applied. From the experimental study, it is concluded that both the fuzzy c-means algorithm and the decision trees were effective in addressing the problem at hand. From the meteorological conditions, described by the fire risk indices, it was found that these were not always in agreement with the reference forest fire risk prediction scales, revealing the importance of adapting the indices values according to the region in question and taking into account several factors (forest fire risk indices) in the analysis of the conditions associated with the level of risk of an extreme forest fire. The proposed approach proved to be a proof of concept to test the applicability of this type of algorithm in this domain and to compare the results with the two fire risk scales used by IPMA and EEFIS.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".