Visualization of local wind field based forest-fire’s forecast modeling for transportation planning

The forest fire occurs every year and brings huge losses to human life and property. How to predict the trend of forest fire accurately for commanders to make decisions in a very short period of time, has become a hot issue of research in recent years. In many cases, temporal and spatial variation of wind direction and wind speed become the main factors in affecting the spread of forest fire. Research on local wind field in micro scale is helpful to improve the accuracy of the prediction of forest fire spreading, but in reality, the data provided by meteorological department are large scale wind field data. Micro scale of the local wind field data mostly rely on the speculation based on the experience of people. So the system which can transform the large scale wind field data to the micro scale local wind field data to improve the prediction accuracy of forest fire spreading is especially necessary. In this paper, according to the characteristics of the near surface wind field we calculate the wind speed by the wind profile, get differential of wind direction of each grid position through the diagnosis of the wind field model, so as to realize the finer micro scale wind field in the complex topography. Wind field and forest fire model can be displayed in real time visualization platform. In the final of this paper, we selected multiple fire cases contained with the southern typical fire cases which have the complete data as the research object to analyze, and have implemented the simulation of forest fire inversion in the self-developed software FFSimulator v1. 0. The simulation of local wind field, fixed wind field and the no wind field forest fire are conducted under the conditions of the same terrain and the same combustible material. With the comparison of simulation results and the case study of forest fire burning area and spread edges, the experimental results show that the simulation using local wind field data of forest fire is more accurate in forecasting the trend of fire spreading, and can provide more favorable reference for making fire prevention and fire fighting decisions and transportation planning in forest.