Predicting and Modeling Wildfire Propagation Areas with BAT and Maximum-State PageRank

Wei-Chang Yeh,Chia-Chen Kuo

doi:10.3390/app10238349

Abstract

The nature and characteristics of free-burning wildland fires have significant economic, safety, and environmental impacts. Additionally, the increase in global warming has led to an increase in the number and severity of wildfires. Hence, there is an increasing need for accurately calculating the probability of wildfire propagation in certain areas. In this study, we firstly demonstrate that the landscapes of wildfire propagation can be represented as a scale-free network, where the wildfire is modeled as the scale-free network whose degree follows the power law. By establishing the state-related concepts and modifying the Binary-Addition-Tree (BAT) together with the PageRank, we propose a new methodology to serve as a reliable tool in predicting the probability of wildfire propagation in certain areas. Furthermore, we demonstrate that the proposed maximum-state PageRank used in the methodology can be implemented separately as a fast, simple, and effective tool in identifying the areas that require immediate protection. The proposed methodology and maximum-state PageRank are validated in the example generated from the Barabási-Albert model in the study.

Highlights

IntroductionThe network research is very popular and has been applied to research in many fields [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38].Recently, wildfires have seen unbelievable growth in size, frequency, and complexity
Using the proposed novel state concepts and traditional PageRank, whose details are provided in Section 2.3, this study proposes an innovative methodology based on BAT [46] and PageRank [54,55] to predict the probable areas affected by the wildfire propagation that is modeled using the scale-free network generated from the Barabási-Albert model [45] and assess the risk of wildfire spread
The scale-free network illustrated in Figure 1 is used to validate the proposed algorithm in estimating the occurrence probability of the wildfire propagation areas, i.e., Pr(i, Narea ) for i = 0, 1

Summary

Introduction

The network research is very popular and has been applied to research in many fields [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38].Recently, wildfires have seen unbelievable growth in size, frequency, and complexity. As defined in [28], wildfire is a non-structure and non-prescribed that occurs in wildlands. Wildfires often ignite close to wildland-human boundaries, resulting in severe damage to assets [28,29,30], air pollution [31,32], loss of plants and forest [33,34,35], and high fatalities [36,37]. Wildfire is a severe practical problem [28,38], and does not always occur in the same area, such as a town or city, but expands to the neighboring areas, and is affected by weather conditions, e.g., wind, rain, etc., and location surface, such as forest or lake [36]. Unlike the traditional path problem [39], which states that all events occur in a path, and the spanning tree problem [40], which has fixed destinations, the wildland fire problem requires a new algorithm to estimate its occurrence and the possible results of its propagation [32,33,34]

Objectives

Methods

Results

Conclusion