On the contrary, with a higher P value (i.e., 0.126), the slope layer has an insigni cant e ect on re susceptibility in the study area. e 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 signi cant in uence on the spatial distribution of re susceptibility in Chongqing city. And, 30% of these re occurrence data (205) and the same amount of no-re data (205) were emerged as validation dataset. To do this, 70% of 684 re occurrence data (479) during the period of 2000-2018 were applied to train the proposed model. 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 res in Chongqing city. Traditional models were developed on the basis of random selection of absence data (i.e., non re data from unburned areas), which could bring uncertainties to modeling results. Modeling re susceptibility in re-prone areas of forest ecosystems was essential for providing guidance to implement prevention and control measures of forest res.
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