Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

Tongxin Hu,Shufa Sun,Jiuqing Liu,Shiyu Zhang,Xingdong Li,Mingxian Zhang,Long Sun

doi:10.3390/fire5010013

Abstract

The simulation of forest fire spread is a key problem for the management of fire, and Cellular Automata (CA) has been used to simulate the complex mechanism of the fire spread for a long time. The simulation of CA is driven by the rate of fire spread (ROS), which is hard to estimate, because some input parameters of the current ROS model cannot be provided with a high precision, so the CA approach has not been well applied yet in the forest fire management system to date. The forest fire spread simulation model LSTM-CA using CA with LSTM is proposed in this paper. Based on the interaction between wind and fire, S-LSTM is proposed, which takes full advantage of the time dependency of the ROS. The ROS estimated by the S-LSTM is satisfactory, even though the input parameters are not perfect. Fifteen kinds of ROS models with the same structure are trained for different cases of slope direction and wind direction, and the model with the closest case is selected to drive the transmission between the adjacent cells. In order to simulate the actual spread of forest fire, the LSTM-based models are trained based on the data captured, and three correction rules are added to the CA model. Finally, the prediction accuracy of forest fire spread is verified though the KAPPA coefficient, Hausdorff distance, and horizontal comparison experiments based on remote sensing images of wildfires. The LSTM-CA model has good practicality in simulating the spread of forest fires.

Highlights

Publisher’s Note: MDPI stays neutralForest fire is a global natural disaster [1]
The design of the LSTM and S-LSTM was presented in Section 2, and wind speed and the rate of fire spread (ROS) are input into the LSTM for the ROS prediction
In order to verify the effectiveness of the model, this paper sets up two comparative experiments: the first experiment compares the simulation results with the actual results, and the second experiment compares the results of LSTM-Cellular Automata (CA) and extreme learning machine (ELM)-CA

Summary

Introduction

Publisher’s Note: MDPI stays neutralForest fire is a global natural disaster [1]. In recent years, the frequent occurrence of forest fires has been caused by global warming, the annual increase in the amount of combustible materials, and the difficulty controlling fire source [2,3]. There are many complex factors influencing the spread of forest fires [4,5]. Accurate simulation [6,7,8,9,10] of forest fire spread can effectively reduce casualties and property losses [11]. The Rothermel [12] and Wang Zhengfei [13] models are the most common methods for simulating the spread of forest fire. These two methods belong to the ROS model, whose parameters are optimized experimentally.

Results

Discussion

Conclusion