Wildfire Susceptibility Mapping for South-eastern Australia by Evolutionary Algorithms and Statistical Methods

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Copyright: Copyright 2021, University of New South Wales
Australia is one of the most flammable counties due to fuel accumulation and frequent droughts. The number and size of wildfire incidents have increased during the last decades. Global warming, industrialisation and extensive human activities played an important role in the increase of wildfire incidents. Wildfires are a considerable threat to human lives and properties, especially in populated areas. In addition, wildfires will negatively impact the components of our ecosystem such as vegetation, soil, water and forests. Wildfire susceptibility maps show the areas with different probabilities of fire occurrence. These maps help managers and policymakers to act efficiently and reduce the negative impacts of wildfires. Many models were created by Geospatial Information System (GIS) and Remote Sensing (RS) to predict wildfires. This thesis aims to investigate wildfire susceptibility in Victoria located in south-eastern Australia with an area of 227,444 km2. The elevation in this area ranged between -76 m to 1,986 m. More than a million hectares burned in Victoria in the last bushfire season in 2019-2020. In addition, more than 110 homes or businesses were destroyed during this period. A wildfire susceptibility model could be a useful tool to control and manage the future wildfires by predicting vulnerable areas. This study aims to generate wildfire susceptibility maps for the south-eastern part of Australia. The main research objectives are as follows. 1. To generate a wildfire inventory map from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. 2. To develop the conditioning factors and map layers. 3. To generate wildfire susceptibility maps using statistical methods e.g., Frequency Ratio (FR) and Logistic Regression (LR) and evolutionary algorithms separately. 4. To apply ensemble techniques (statistical methods combined with evolutionary algorithms) to generate wildfire susceptibility maps. 5. To evaluate the performance of the proposed methods by using the Receiver Operating Characteristics (ROC) curve.
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Gene Expression Programming
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