A Bayesian network model for prediction and analysis of possible forest fire causes

Abstract

Abstract 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.