Notice bibliographique
Résumé
The thesis discusses forest fire occurrence in the Karst forest management region in the period from 1995 to 2009. Data analysis has shown that fire occurrence has two season peaks which are highly associated with human activities in the natural environment (land cultivation, railway). Therefore fire mostly starts in unwooded areas, from where the wind spreads it into the woods. Most frequently it occurs in deciduous forests, and the largest burnt areas occur in coniferous forests. The kernel density showed that lightning fires most frequently occur in the northern part of the region, where hills rise above the surrounding landscape. Forest fire occurrence is impacted by people, tree composition and terrain, which coincides with the findings of authors who explore forest fires in different natural environments. Fire is a natural process with its own rules that does not change regardless of where it occurs. To predict the incidence of forest fire on the basis of weather variables we used the Canadian Forest Fire Weather Index System (CFFWIS), which is applied in Europe within the European Fire Forest Index System (EFFIS). Its performance has not been thoroughly assessed, especially in environments less prone to fire, but this has been done herein. Five fire danger classes (very high, high, moderate low and very low) were derived from percentile analysis of the CFFWIS Fire Weather Index. We established that these classes are related to former forest fire occurrence. Predicting the day when the forest fire might occur was done more precisely with the classification tree than with the logistic regression. Fire activity is more related to current weather conditions than to drought. Our findings stress the applicability of CFFWIS in forest fire protection, and simultaneously suggest limitations related to a small number of fires and a small study area. On the basis of data on forest stands, we created the forest fire occurrence probability model with the ordinary least squares method (OLS) and geographically weighted regression (GWR). Higher quality model was obtained with geographically weighted regression which showed which characteristics of forest stands influence forest fire occurrence and in what way. The GWR model has also shown that in addition to characteristics of forest stands we also need other data (railway). A combination of spatial statistical methods on the characteristics of forest stands has allowed detailed insight into forest fire occurrence and its characteristics. One of the duties of predicting forest fire danger is also predicting forest fire behaviour, which, in addition to weather data and topography, also requires a fuel model. We examined forest databases and established that they do not contain all data needed for calculating fuel model parameters. However, they do contain data on forest stands (forest stand maps), which can be applied to the spatial display of the fuel model (fuelbed). This serves as the basis for further studies in \nthe area of predicting forest fire behaviour. We established that usefulness of forest stand maps overcome their original purpose of collecting and preparing for forest management purposes. Their real significance will become clear only in the coming years as crucial information on forests and forest area, which provides a starting point for monitoring various habitat types and species, and environmental protection.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».