Exploratory analysis of wild-fires in Australia & a machine learning approach for wildfire modeling in Google Earth Engine

The free remotely sensed data allows analyzing this disaster and therefore this thesis investigates the Australian wildfires based data from Earth observation with the respect to uncovering the general insights. In the last few years, machine learning (ML) has proven to be successful in many domains due to the capability of learning also from hidden relationships.

In this study, the overall objective is to create an automatized process of creating a fire training dataset at a continental level with an efficient computational expense for the ML algorithms. These results of fire occurrence locations and no-fire occurrence locations are mapped alongside with 15 fire causal factors.

The training dataset is applied to different ML algorithms, such as Random Forest (RF), Naïve Bayes, and Classification and Re-gression Tree (CART).

All ML approaches were trained using 70% of the wildfire dataset and tested using the remaining 30% of the dataset. The ML algorithm with the best performance, the RF model, helps to identify the driving factors using variable importance analysis based on ML methods. Typically, this model can learn certain properties from a training dataset to make predictions. Thus, the re-sults of this thesis study disclose the fire occurrence probability in Australia as well as identified the driving factors and their dynamic influence on fire occurrence. Improved preventive measures can be implemented in the fire-prone areas to reduce the risk of fire in Australia by considering the factors identified in this study.

The flowchart of processes employed in the study for generating the predictive model in GEE.

Data mining and pre-processing is an important step to generate the training da-taset as an input for the ML models. The training dataset consists of independent variables also referred to as the predictors (land cover, temperature, etc.) and dependent variables also known as the responding variables (fire, no-fire).

The dependent variable in this study is fire and non-fire occurrence locations. Thus, mapping susceptibility of fire occurrence can be considered from the ML perspective as a binary classification problem with two classes: fire and no-fire. However, the dataset of recently occurred fire locations with high resolution is not available form the Australian official sources. Therefore, collecting fire and no-fire occurrence locations is developed in this study as an automated workflow presented in Figure.

The flowchart of fire occurrence locations applied in methodology.

The distribution of fire and no-point from the automated process

An example of wildfire in pre-fire and post-fire RGB imagery and monthly active fire from the S-2 mis-sion for visual verification of fire points.

Creating the selection of independent variables, which are also known as predic-tors or conditioning factors, is a critical step in predictive modelling. For this study, 15 conditioning factors are selected based on both the field observation found in different studies and available satellite data on the GEE platform. These applied wildfire conditioning factors can be divided into five categories, such as topog-raphy, vegetation type, infrastructure, meteorology and socio-economic factors. Table 3 summarizes each of the datasets used in this study.

The list and description of variable datasets included in the study.

Topographical factors: elevation, aspect and slope.

Climate factors: precipitation, maximum temperature and wind speed.

Environmental factors: land cover (the legend is in Appendix), soil depth, soil moisture, drought severity index and NDVI.

Socio-economic factors: GHM, population, electric lines and distance from roads.

Predictive modeling is the overall concept of building a ML model that is capable of making predictions. Typically, such a model includes a machine learning algo-rithm that learns certain properties from a training dataset in order to make those predictions. In this study, the RF model and the training dataset present the wild-fires in Australia during the 2019-2020 season.

The fire susceptibility map using the RF model.

The fire susceptibility map with classes using the RF model.

The variable importance was calculated based on the training dataset

The variable importance analysis based on the RF model.