Forest Research: Open Access
Open Access

Abstract

Comparison of Statistical Modeling and AHP Methods in Fire Risk Assessment in Oak Forests of Iran

Leyla Darvishi, Mehrdad Ghodskhah Daryaei* and Abouzar Heidari Safari Kouchi

One of the initial steps in management of forests and reduction of fire damages is to identify areas susceptible to this phenomenon. In this study, zonation of fire hazard was performed in Babahur forest area of Dorud city of Iran using statistical modeling method and analytical hierarchy process. Then, performance of the two methods was evaluated. In this study, sixty three areas, including burned and unburned lands, were detected totally. Then, quantitative values of some effective parameters in forest fires like temperature, wind speed, rainfall, altitude, aspect, slope, distance from residential area and vegetation cover, distance from river and road, were estimated using data and maps obtained from the Geographical Information System (GIS) and remote sensing (RS) as well as field studies. Finally, the model was presented through regression analysis and then its performance was tested and validated. Based on correlation rate, main factors that influenced fires in multiple regression analysis were vegetation cover percent (r= 0.79) and rainfall (r= -0.34), respectively. In the first step of fire hazard zonation through analytical hierarchy process (AHP), 45 samples including burned areas were identified. Between studied factors, temperature with a coefficient of determination of 0.402 was the most important factor affecting the occurrence of fire in the studied area. Then, Intra-layer and extra-layer valuation of fire factors was performed. Maps of fire factors were developed in GIS and fire hazard zonation map was prepared through combining the layers of fire factors according to their weights. Also, the fire hazard zonation map was prepared through multiple regression analysis and analytical hierarchy process. Comparison of their results through estimation of accuracy and performance of the maps indicated the higher capability of the statistical modeling approach in fire risk assessment in studied area.

Published Date: 2020-06-23; Received Date: 2020-06-09