Browsing by Author "Sevinc V."

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A Bayesian network model for prediction and analysis of possible forest fire causes
(2020-02-01) Sevinc V.; Kucuk O.; Goltas M.
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, which is built on the nodes relative humidity, temperature, wind speed, month, distance from settlement, amount of burnt area, distance from agricultural land, distance from road and tree species, was estimated. The model showed that month is the first and temperature is the second most effective factor on the forest fire ignitions. The Bayesian network model approach adopted in this study could also be used with data obtained from different areas having different sizes.
Fire behavior prediction with artificial intelligence in thinned black pine (Pinus nigra Arnold) stand
(2023-02-01) Kucuk O.; Sevinc V.
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.