Providing a method based on cellular automata for modeling the forest fire development in Golestan national park forests

In the present study, firstly, the effective factors of Golestan national park forests fire have been identified. Then, by using these factors, forest fire development modeling is performed based on cellular automata. Multiple linear regression and genetic algorithm are used to determine the effective factors on Golestan national park forests fire. In order to investigate the effect of spatial resolution of the maps are used on the results of modeling, effective factors have been generated in different spatial resolution and these data are used as the input of the proposed algorithm. Also, the neighboring filters 3×3, 5×5, and 7×7 are used to investigate the effect of the neighboring filter in the forest fire development process. Cellular automata is used for modeling Golestan national park forests fire development, and the artificial bee colony is proposed to calibrate it. The results of this study show that using the proposed algorithm with 3×3 neighboring filter is more accurate than the other neighboring filters. In the best case, the Kappa index, the overall accuracy, and the relative operating characteristic are 0.924, 0.960, and 0.494, respectively that these results are for spatial resolution of 30 meters on November 17, 2010.