Abstract

Bushfire susceptibility mapping helps the government authorities predict and provide the required disaster management plans to reduce the adverse impacts from bushfires. In this paper, we investigated Gene Expression Programming (GEP) and ensemble methods to create bushfire susceptibility maps for Victoria, Australia, as a case study. Bushfire susceptibility maps indicate that the eastern part of Victoria where forests are predominant has the highest probability of bushfire. Western part of Victoria which is covered by cropland, shrubland and grassland has the lowest bushfire probability. Two ensemble methods, namely an ensemble of GEP and Frequency Ratio (GEPFR) and an ensemble of Logistic Regression and Frequency Ratio (LRFR), were proposed and compared with stand-alone GEP and stand-alone Frequency Ratio (FR) methods. The proposed methods were evaluated by Area Under Curve (AUC). AUCs of GEPFR, LRFR, GEP and FR are 0.860, 0.852, 0.850, and 0.840, respectively. It can be concluded that GEPFR outperforms the other three methods, and the ensemble methods outperform the stand-alone methods. GEPFR, LRFR and GEP produced the bushfire probability with an accuracy in the range of 90.79%−92.27%, and therefore they are equally useful for policy makers and managers to have better natural hazard management plans.

摘要

丛林火灾敏感性绘图有助于政府当局预测并提供所需的灾害管理计划，以减少丛林火灾的不利影响。在本文中，作为案例研究，我们研究了基因表达编程 (GEP) 和集成方法，以创建澳大利亚维多利亚州的丛林火灾易感性图。丛林火灾敏感性地图表明，维多利亚州东部以森林为主的地区发生丛林火灾的可能性最高。维多利亚州西部被农田、灌木丛和草地覆盖的森林火灾概率最低。提出了两种集成方法，即 GEP 和频率比 (GEPFR) 的集成以及逻辑回归和频率比 (LRFR) 的集成，并与独立 GEP 和独立频率比 (FR) 方法进行了比较。所提出的方法通过曲线下面积 (AUC) 进行评估。GEPFR、LRFR、GEP 和 FR 的 AUC 分别为 0.860、0.852、0.850 和 0.840。可以得出结论，GEPFR 优于其他三种方法，集成方法优于独立方法。GEPFR、LRFR 和 GEP 以 90.79%-92.27% 的准确度产生了丛林火灾概率，因此它们对于政策制定者和管理者制定更好的自然灾害管理计划同样有用。