| Yazarlar |
Prof. Dr. Ömer KÜÇÜK
Kastamonu Üniversitesi, Türkiye |
Volkan Sevinç
Türkiye |
| Özet |
| Modeling forest fire behavior is very important for the effective control of forest fires and the setting up of necessary precautions before fires start. However, studies of forest fire behavior are complex studies that depend on many variables and usually involve large data sets. For this reason, the predictive power and speed of classical forecasting models are lower than of artificial intelligence models in cases involving big data and many variables. Moreover, classical forecasting models must satisfy certain statistical assumptions, unlike artificial intelligence methods. Thus, in this study, predictions were made of surface fire behavior, especially the rate of fire spread and the fire intensity, at the location at which fires started using two artificial intelligence methods, an artificial neural network and a decision tree. The accuracy of the developed models was fitted and tested. Finally, the classical regression model for predicting surface fire behavior was compared with the two artificial intelligence methods. The accuracy measures of the artificial intelligence models were found to be better than those of the classical model. |
| Anahtar Kelimeler |
| Artificial intelligence | Artificial neural networks | Black pine | Decision trees | Fire behavior | Forest fires |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | FOREST ECOLOGY AND MANAGEMENT |
| Dergi ISSN | 0378-1127 |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q1 |
| Makale Dili | Türkçe |
| Basım Tarihi | 02-2023 |
| Cilt No | 529 |
| Sayı | 1 |
| Doi Numarası | 10.1016/j.foreco.2022.120707 |
| Makale Linki | http://dx.doi.org/10.1016/j.foreco.2022.120707 |
| Atıf Sayıları | |
| WoS | 8 |
| SCOPUS | 10 |
| Google Scholar | 11 |
