Sensitivity Analysis on Ecological Factors Affecting Forest Fire Spreading: Simulation Study

Abstract

Forest fires are expected to increase in severity and frequency under global climate change and thus better understanding of fire dynamics is critical for mitigation and adaptation. Researchers with different background, such as ecologists, physicists, and mathematical biologists, have developed various simulation models to reproduce forest fire spread dynamics. However, these models have limitations in the fire spreading because of the complicated factors such as fuel types, wind, and moisture. In this study, we suggested a simple model considering the wind effect and two different fuel types. The two fuels correspond to susceptible tree and resistant tree with different probabilities of transferring fire. The trees were randomly distributed in simulation space with a density ranging from 0.0 (low) to 1.0 (high). The susceptible tree had higher value of the probability than the resistant tree. Based on the number of burnt trees, we then carried out the sensitivity analysis to quantify how the forest fire patterns are affected by wind and tree density. The statistical analysis showed that the total tree density had greatest effect on the forest fire spreading and wind had the next greatest effect. The density of the susceptible tree was relatively lower factor affecting the forest fire. We believe that our model can be a useful tool to explore forest fire spreading patterns.

Improvement of False Report Detection Performance Based on Invalid Data Detection Using Neural Network in WSNF

“…P shows the probability that a tree with a value of Pr is affected by the wind and has a higher probability of fire. Pw is a random variable representing the wind strength [31].The fire transition probability function is as follows: = + (1 − ) . Figure 6 shows a brief overview of the field's fire state transition process when CH = 2500, wind direction = south, wind speed =7, and the maximum unit time (UT) is 15.…”

Emergency Decision System for Surface Gathering and Transporting of CBM Based on Technologies of 3G Wireless Video Monitoring and 3S

“…Among them, A is a constant, K a is wind coefficient, K b is terrain coefficient, K c is DBH coefficient, K d is the minimum relative humidity coefficient and R is the spreading speed of forest fire [7]. In view of the different spreading direction, calculate spreading speed of forest fire at each grid distance; 4) based on the spreading speed R, calculate the position (coordinates) where the forest fire spread to along east direction, northeast, north, northwest, west, southwest direction, south, southeast direction after t hours; 5) based on the points in eight direction in the above step, take these points form a polygon, and get the final result after smoothing polygon boundary.…”