Abstract
Climate change has increased the probability of the occurrence of catastrophes like wildfires, floods, and storms across the globe in recent years. Weather conditions continue to grow more extreme, and wildfires are occurring quite frequently and are spreading with greater intensity. Wildfires ravage forest areas, as recently seen in the Amazon, the United States, and more recently in Australia. The availability of remotely sensed data has vastly improved, and enables us to precisely locate wildfires for monitoring purposes. Wildfire inventory data was created by integrating the polygons collected through field surveys using global positioning systems (GPS) and the data collected from the moderate resolution imaging spectrometer (MODIS) thermal anomalies product between 2012 and 2017 for the study area. The inventory data, along with sixteen conditioning factors selected for the study area, was used to appraise the potential of various machine learning (ML) methods for wildfire susceptibility mapping in Amol County. The ML methods chosen for this study are artificial neural network (ANN), dmine regression (DR), DM neural, least angle regression (LARS), multi-layer perceptron (MLP), random forest (RF), radial basis function (RBF), self-organizing maps (SOM), support vector machine (SVM), and decision tree (DT), along with the statistical approach of logistic regression (LR), which is very apt for wildfire susceptibility studies. The wildfire inventory data was categorized as three-fold, with 66% being used for training the models and 33% being used for accuracy assessment within three-fold cross-validation (CV). Receiver operating characteristics (ROC) was used to assess the accuracy of the ML approaches. RF had the highest accuracy of 88%, followed by SVM with an accuracy of almost 79%, and LR had the lowest accuracy of 65%. This shows that RF is better suited for wildfire susceptibility assessments in our case study area.
Highlights
Forests are considered crucial natural resources that play an integral function in preserving the ecological equilibrium of the environment and shielding one-third of the earth
The susceptibility map indicates the probability of wildfire occurrences based on the relevant conditioning factors for map indicates the probability of wildfire occurrences based on the relevant conditioning factors for a a given region
Each of machine learning (ML) approaches used for attaining the susceptibility maps was classified based on the natural break the eleven ML approaches used for attaining the susceptibility maps was classified based on the classification method and into one of five classes, which natural break classification method and into one of five classes
Summary
Forests are considered crucial natural resources that play an integral function in preserving the ecological equilibrium of the environment and shielding one-third of the earth. According to FAO, forests across the world inhabit an area of about 4000 million ha, which is approximately 30% of the earth’s total surface area [1]. Forests play a vital role in the production of oxygen and purifying the environment [2]. Ecological health is measured by the state and well-being of the forest which are true signs of the condition of the region. Forests have economic and social importance and play important roles in the existence of all living things on planet Earth.
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