Extreme Fire Severity Classification using Clustering and Decision Tree
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».