A Bayesian network model for prediction and analysis of possible forest fire causes
Yazarlar (3)
Doç. Dr. Volkan Sevinç Muğla Sıtkı Koçman Üniversitesi, Türkiye
Prof. Dr. Ömer KÜÇÜK Kastamonu Üniversitesi, Türkiye
Dr. Öğr. Üyesi Merih Göltaş İstanbul Üniversitesi-Cerrahpaşa, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Forest Ecology and Management | ||
| Dergi ISSN | 0378-1127 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI | ||
| Makale Dili | İngilizce | Basım Tarihi | 02-2020 |
| Cilt / Sayı / Sayfa | 457 / 1 / 117723–0 | DOI | 10.1016/j.foreco.2019.117723 |
| Makale Linki | https://linkinghub.elsevier.com/retrieve/pii/S0378112719311776 | ||
| UAK Araştırma Alanları |
Orman Entomolojisi ve Koruma
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| Özet |
| Possible causes of a forest fire ignition could be human-caused (arson, smoking, hunting, picnic fire, shepherd fire, stubble burning) or natural-caused (lightning strikes, power lines). Temperature, relative humidity, tree species, distance from road, wind speed, distance from agricultural land, amount of burnt area, month and distance from settlement are the risk factors that may affect the occurrence of forest fires. This study introduces the use of Bayesian network model to predict the possible forest fire causes, as well as to perform an analysis of the multilateral interactive relations among them. The study was conducted in Mugla Regional Directorate of Forestry area located in the southwest of Turkey. The fire data, which were recorded between 2008 and 2018 in the area, were provided by General Directorate of Forestry. In this study, after applying some different structural learning algorithms, a Bayesian network … |
| Anahtar Kelimeler |
| Bayesian networks | Forest fires | Sensitivity analysis | Structural learning |
Science Direct
A Bayesian network model for prediction and analysis of possible forest fire causes
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 129 |
| Scopus | 160 |
| Google Scholar | 231 |