Application of an Ensemble Statistical Approach in Spatial Predictions of Bushfire Probability and Risk Mapping

Mahyat Shafapour Tehrany,Vahideh Saeidi,Bahareh Kalantar,Mohammad Reza Habibi,Haluk Özener,Farzin Shabani,Naonori Ueda,Fariborz Shabani,Abdellah Touhafi

doi:10.1155/2021/6638241

Abstract

The survival of humanity is dependent on the survival of forests and the ecosystems they support, yet annually wildfires destroy millions of hectares of global forestry. Wildfires take place under specific conditions and in certain regions, which can be studied through appropriate techniques. A variety of statistical modeling methods have been assessed by researchers; however, ensemble modeling of wildfire susceptibility has not been undertaken. We hypothesize that ensemble modeling of wildfire susceptibility is better than a single modeling technique. This study models the occurrence of wildfire in the Brisbane Catchment of Australia, which is an annual event, using the index of entropy (IoE), evidential belief function (EBF), and logistic regression (LR) ensemble techniques. As a secondary goal of this research, the spatial distribution of the wildfire risk from different aspects such as urbanization and ecosystem was evaluated. The highest accuracy (88.51%) was achieved using the ensemble EBF and LR model. The outcomes of this study may be helpful to particular groups such as planners to avoid susceptible and risky regions in their planning; model builders to replace the traditional individual methods with ensemble algorithms; and geospatial users to enhance their knowledge of geographic information system (GIS) applications.

Highlights

Wildfires, alternatively termed forest fires, bushfires, woodland fires, and vegetation fires, boosted by wind and high summer temperatures, are able to destroy entire forests faster than they can be brought under control [1], causing irreversible, incalculable environmental, economic, and social damage [2]
To address the important factors in bushfire occurrence in the Brisbane Catchment, Australia, we evaluated and ranked the initial fourteen causative factors (i.e., altitude, slope, aspect, curvature, topographic wetness index (TWI), topographic position index (TPI), rainfall, geology, soil, land use land cover (LULC), distance from rivers, distance from roads, wind, and normalized difference vegetation index (NDVI))
This research has focused on increasing the accuracy of wildfire susceptibility mapping through ensemble modeling

Summary

Introduction

Alternatively termed forest fires, bushfires, woodland fires, and vegetation fires, boosted by wind and high summer temperatures, are able to destroy entire forests faster than they can be brought under control [1], causing irreversible, incalculable environmental, economic, and social damage [2]. Wildfires cause direct forest degradation [3]. Like the Australian wildfires 2020 [4] which a wide variety of forest flora [5] and forest species [6] were destroyed within a very short period of time. Destroying watersheds [12] and reducing water quality [13, 14] are destructive impacts of this disaster. Impacts on human settlements and health [15, 16] can be considered as nonreturnable negative influence of wildfire disaster

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