Application of an Ensemble Statistical Approach in Spatial Predictions of Bushfire Probability and Risk Mapping. (23rd April 2021)

• Record Type: Journal Article
• Authors: Tehrany, Mahyat Shafapour; Özener, Haluk; Kalantar, Bahareh; Ueda, Naonori; Habibi, Mohammad Reza; Shabani, Fariborz; Saeidi, Vahideh; Shabani, Farzin
• Other Names: Touhafi Abdellah Academic Editor.
• Is Part Of: Journal of sensors. Volume 2021(2021)
• Publication Date: 2021-04-23
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2021/6638241
• Languages: English
• ISSNs: 1687-725X

Abstract

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.