Research on escape route planning analysis in forest fire scenes based on the improved A* algorithm

Abstract

The forest fire environment is complex and volatile, posing a serious threat to the lives of firefighters at any time. Aiming at how people choose escape routes when facing these dangers, this paper proposed an escape route planning method based on the improved A* algorithm. Taking the forest fire that occurred in Xintian County, Yongzhou City, Hunan Province on October 17, 2022, as the research object, this study collected data from multi-temporal remote sensing imagery including GF-4, and Sentinel-2 to obtain 11 factors affecting the escape route planning. We used the FAHP-CRITIC combination weighting method to analyze the weights of the escape impact factors. Forest fire scenes were categorized into five classes based on escape risk coefficient from high to low. The heuristic function and weight coefficients of the traditional A* algorithm were reconstructed to obtain the improved A* algorithm. The computing time of both A* algorithm is similar in the same fire scene. But the percentage of the escape route length, located inside the high-risk zones, in the total escape route planned by the improved algorithm decreased by 53.63% than that planned by the traditional A* algorithm. The escape risk coefficient was reduced by 24.22%, and the escape safety was significantly improved. On this basis, the study combined the improved A* algorithm with the dynamic escape window method to search for the nearest safe area to the escapees and compute the corresponding escape routes. The real-time safety of the algorithm was verified using GF-1 remote sensing images obtained 7 and 35 min after the previous fire burning moment. Results revealed that the escape paths planned by the improved A* algorithm remained in a safe state and were able to be updated in real time according to the trend of the fire. This demonstrates the ability of the improved A* algorithm to adapt to real-time changes in forest fire scenes. It can provide multiple reliable escape options for escapees, thus scientifically and effectively reducing human casualties.