Three-Dimensional Dynamic Simulation System for Forest Surface Fire Spreading Prediction

Author:

Li Jianwei1ORCID,Li Xiaowen2,Chen Chongchen3,Zheng Huiru4,Liu Naiyuan1

Affiliation:

1. College of Physics and Information Engineering, Fuzhou University, No. 2, Xueyuan Road, Fuzhou City, Fujian 350116, P. R. China

2. Longyan University, No. 1, Dongxiao North Road, Longyan City, Fujian 364012, P. R. China

3. Spatial Information Research Center of Fujian, Fuzhou University, No. 2, Xueyuan Road, Fuzhou City, Fujian 350116, P. R. China

4. School of Computing, Ulster University, Shore Road, Newtownabbey, Co. Antrim, BT37 0QB, UK

Abstract

Forest fire is one of the most frequent, fast spreading and destructive natural disasters. Many countries have developed their own fire prediction model and computational systems to predict the fire spreading, however, the user interaction, display effect and prediction accuracy have not yet met the requirements for firefighting in real forest fire events. The forest fire spreading is a complex process affected by multi-factors. Understanding the relationships between these multi-factors and the forest fire spreading trend is vital to predicting the fire spreading promptly and accurately to make the strategy in extinguishing the forest fire. In this paper, we propose and develop a three-dimensional (3D) forest fire spreading simulation system, FFSimulator, to visualize the impact of multi-factors to the fire spread. FFSimultor integrates the multi-factor analysis approach with the FARSITE prediction model to improve the prediction. The FFSimulator developed applies 3D scene organization, template-based vector data mapping and overlaps visualization techniques to provide a 3D dynamic visualization of large-scale forest fire. The 3D multi-factors superposition analysis simulates the impacts of individual factor and multi-factors on the trend of surface fire spreading, which can be used to identify the key sites for the prevention and the control of forest fires. The system has been tested and evaluated using real data of Shanghan forest fire.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Fujian Province

Science Foundation of Fuzhou university

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

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