Site-Specific Wildfire Risk Index in Croatian Wildfire Monitoring and Surveillance System
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Résumé
first_page settings Order Article Reprints Font Type: Arial Georgia Verdana Font Size: Aa Aa Aa Line Spacing: Column Width: Background: Open AccessAbstract Site-Specific Wildfire Risk Index in Croatian Wildfire Monitoring and Surveillance System † by Darko Stipaničev *, Marin Bugarić, Ljiljana Šerić, Damir Krstinić and Dunja Božić-Štulić Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture (FESB) University of Split, 21000 Split, Croatia * Author to whom correspondence should be addressed. † Presented at the Third International Conference on Fire Behavior and Risk, Sardinia, Italy, 3–6 May 2022. Environ. Sci. Proc. 2022, 17(1), 34; https://doi.org/10.3390/environsciproc2022017034 Published: 9 August 2022 (This article belongs to the Proceedings of The Third International Conference on Fire Behavior and Risk) Download Download PDF Download XML Download Epub Versions Notes Identifying the danger of fire is important for both wildfire prevention and protection. It can be useful for improving automatic fire detection systems, but also for many other fire-fighting activities that happen before the actual wildfire. The Croatian wildfire risk index is related to estimation of wildfire ignition danger and propagation danger. It is calculated on a micro-location level; therefore, it is a site-specific wildfire risk index. During its development, we have studied the possible influence of various parameters on risk index value using the correlation analysis with past wildfires in Split-Dalmatia County. Finally, two categories of parameters have been chosen:Static parameters: vegetation (fuel fire sensitivity), terrain configuration (elevation, slope, aspect) and anthropogenic parameters (settlements, roads, transmission lines);Dynamic parameters: wind speed and direction (correlated with slope and aspect) and Canadian Forest Fire Weather Index (FWI). Dynamic parameters are provided by the Croatian Meteorological Service once a day with 24 h forecast by ALADIN model in a 3 h time scale. The relative influence of specific parameter to overall risk index value were optimized by genetic algorithms.In its present version, it is integrated with the Croatian online wildfire intelligent monitoring and surveillance system (OIV Fire Detect AI) installed in Croatian Dalmatian counties and has been used by Croatian firefighters in everyday practice since 2016. Currently, we are working on its further improvement through the H2020 FirEUrisk project, particularly in parts dedicated to propagation danger and wildfire vulnerability, but also in more accurate determination of parameters' influence and new user-friendly visualization.In our research, we will describe in more detail how the Croatian wildfire risk index is calculated and used, including its statistical evaluation, but also how it will be improved through FirEUrisk project. Author ContributionsConceptualization, D.S. and M.B. and L.Š.; methodology, D.S. and L.Š.; software, M.B.; validation, D.K., D.B.-Š.; investigation, D.K.; resources, L.Š.; data curation, D.B.-Š.; writing—original draft preparation, D.S. and M.B.; writing—review and editing, D.S. and M.B. All authors have read and agreed to the published version of the manuscript.FundingThis project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101003890.Institutional Review Board StatementNot applicable.Informed Consent StatementNot applicable.Data Availability StatementNot applicable.Conflicts of InterestThe authors declare no conflict of interest.Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Share and Cite MDPI and ACS Style Stipaničev, D.; Bugarić, M.; Šerić, L.; Krstinić, D.; Božić-Štulić, D. Site-Specific Wildfire Risk Index in Croatian Wildfire Monitoring and Surveillance System. Environ. Sci. Proc. 2022, 17, 34. https://doi.org/10.3390/environsciproc2022017034 AMA Style Stipaničev D, Bugarić M, Šerić L, Krstinić D, Božić-Štulić D. Site-Specific Wildfire Risk Index in Croatian Wildfire Monitoring and Surveillance System. Environmental Sciences Proceedings. 2022; 17(1):34. https://doi.org/10.3390/environsciproc2022017034 Chicago/Turabian Style Stipaničev, Darko, Marin Bugarić, Ljiljana Šerić, Damir Krstinić, and Dunja Božić-Štulić. 2022. "Site-Specific Wildfire Risk Index in Croatian Wildfire Monitoring and Surveillance System" Environmental Sciences Proceedings 17, no. 1: 34. https://doi.org/10.3390/environsciproc2022017034 Find Other Styles Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here. Article Metrics No No Article Access Statistics Multiple requests from the same IP address are counted as one view.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».