Modeling the Susceptibility of Forest Fires Using a Genetic Algorithm: A Case Study in Mountain Areas of Southwestern China

Abstract

Modeling fire susceptibility in fire-prone areas of forest ecosystems was essential for providing guidance to implement prevention and control measures of forest fires. Traditional models were developed on the basis of random selection of absence data (i.e., nonfire data from unburned areas), which could bring uncertainties to modeling results. Here, a new model with the genetic algorithm for Rule-set Production (GARP) algorithm and 10 environmental layers was proposed to process presence-only data in the susceptibility modeling of forest fires in Chongqing city. To do this, 70% of 684 fire occurrence data (479) during the period of 2000–2018 were applied to train the proposed model. And, 30% of these fire occurrence data (205) and the same amount of no-fire data (205) were emerged as validation dataset. The results showed that, for some environmental layers (i.e., distance to the nearest road, land cover, precipitation, distance to the nearest settlement, aspect, relative humidity, elevation, wind speed, and temperature), their $P$ values were less than 0.05, indicating that these 9 environmental layers have significant influence on the spatial distribution of fire susceptibility in Chongqing city. On the contrary, with a higher $P$ value (i.e., 0.126), the slope layer has an insignificant effect on fire susceptibility in the study area. Furthermore, the results of receiver operating characteristic analysis (ROC) showed that the proposed model has a good performance with an AUC value of 0.869, an accuracy value of 0.732, a sensitivity value of 0.59, a specificity value of 0.873, a positive predictive value of 0.823, and a negative predictive value of 0.681. This study revealed the validity of the proposed model in modeling the susceptibility of forest fires.