Browsing by Author "Goltas M."

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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.
Diurnal surface fuel moisture prediction model for Calabrian pine stands in Turkey
(2019-06-01) Bilgili E.; Coskuner K.A.; Usta Y.; Saglam B.; Kucuk O.; Berber T.; Goltas M.
This study presents a dynamic model for the prediction of diurnal changes in the moisture content of dead surface fuels in normally stocked Calabrian pine stands under varying weather conditions. The model was developed based on several empirical relationships between moisture contents of dead surface fuels and weather variables, and calibrated using field data collected from three Calabrian stands from three different regions of Turkey (Mugla, southwest; Antalya, south; Trabzon, north-east). The model was tested and validated with independent measurements of fuel moisture from two sets of field observations made during dry and rainy periods. Model predictions showed a mean absolute error (MAE) of 1.19% for litter and 0.90% for duff at Mugla, and 3.62% for litter and 14.38% for duff at Antalya. When two rainy periods were excluded from the analysis at Antalya site, the MAE decreased from 14.38% to 4.29% and R2 increased from 0.25 to 0.83 for duff fuels. Graphical inspection and statistical validation of the model indicated that the diurnal litter and duff moisture dynamics could be predicted reasonably. The model can easily be adapted for other similar fuel types in the Mediterranean region.
Predicting canopy fuel characteristics in Pinus brutia Ten., Pinus nigra Arnold and Pinus pinaster Ait. Forests from stand variables in North-Western Turkey
(2021-02-01) Kucuk O.; Goltas M.; Demirel T.; Mitsopoulos I.; Bilgili E.
Canopy fuel characteristics play an important role in crown fire behaviour in conifer forests. In this study, the canopy fuel characteristics of Calabrian pine, Anatolian Black pine and Maritime pine stands in Turkey are estimated using forest stand parameters. Sets of equations are fitted to the measured data revealing correlations between canopy fuel characteristics and stand parameters by performing a stepwise multiple regression analysis. At the stand level, the resulting models explain a high percentage of the observed variability. The developed equations can be used by forest and fire managers to estimate canopy fuel characteristics, predict crown-fire behaviour and design fuel treatment projects in Turkey.