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

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.

Fusion of the Multisource Datasets for Flood Extent Mapping Based on Ensemble Convolutional Neural Network (CNN) Model

“…Recently, climate and human impact on the ecosystem trigger natural hazards such as drought, floods, and wildfire with higher frequency [1][2][3]. Among these types of disasters, floods are considered as the most devastating and costly disaster worldwide [4,5].…”

“…In other words, the ML is a mathematical expression that represents data in the context of a problem. The ML methods are applied in two main categories: (1) supervised method by predicting some output variable associated with each input sample and (2) unsupervised method that does not need any sample data and provides a prediction by considering input feature dataset. The ML methods are widely deployed in many applications based on different sensors and datasets such as quasidistributed smart textile [37], simultaneous assessment of magnetic field intensity [38], paddy rice seed classification [39,40], anime film visualization [41], eggplant seed classification [42], regional digital construction [43], flood mapping [44], and flood prevention [45].…”

“…The ML methods are widely deployed in many applications based on different sensors and datasets such as quasidistributed smart textile [37], simultaneous assessment of magnetic field intensity [38], paddy rice seed classification [39,40], anime film visualization [41], eggplant seed classification [42], regional digital construction [43], flood mapping [44], and flood prevention [45]. Although the ML methods have provided promising results in many abovementioned applications, they suffer from lower coverage and generalization [1]. Accurate performance of ML highly depends on the input feature map which is a timeconsuming process to extract suitable and informative features, especially in hazardous situations.…”

Application and Potential of Drone Technology in Oil Palm Plantation: Potential and Limitations

“…On the other hand, hyperspectral sensors have several hundred bands that are commonly used to obtain and process information from the electromagnetic spectrum in each pixel of the image taken. However, for light detection and ranging (LiDAR) sensor, it was usually utilized to obtain the slope elevation and structural data [31].…”

Sınırlı Eğitim Verileri Durumunda Orman Yangını Duyarlılık Haritalamasında Makine Öğrenimi Performansının Değerlendirilmesi

“…Such techniques are mostly based on experts' knowledge that in some cases might contain errors. Quantitative techniques predict wildfire susceptibility regions using mathematical assessment of the data [16]. In addition, the subjectivity of qualitative approaches is not included in quantitative techniques.…”