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A machine learning algorithm approach to map the wildfire probability based on static parameters
* 1 , 2 , 3 , 4 , 5
1  Postdoctoral Research Fellow
2  Research Fellow
3  Data Scientist
4  Scientist
5  Associate Professor
Academic Editor: Victor Resco de Dios


Wildfires are occurring throughout the world, causing more damage to the plant and animal species, humans, and the environment. Fire danger indices are useful for forecasting fire danger and these indices are an integration of both static and dynamic indices. The static indicators, such as vegetation, topographic characteristics, etc., are constant over the study area and are variables that promote the ignition of fires and, therefore, useful for understanding fire patterns and distribution in the study area. In this study, The Static fire danger index (SFDI) is generated using MODIS Land cover type (MCD12Q1), Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and Open Street Map datasets by applying Random Forest (RF) algorithm. Random forest (RF) is a machine learning algorithm, which can automatically select important variables and flexibly evaluate the complex interaction between variables. The MODIS TERRA and AQUA active fire points (MCD14) during 2011-2017 have been used to train the RF algorithm and fire probability maps are generated for the years 2018 and 2019. The fire probability maps are categorized into 5 fire danger classes, i.e., very low, low, medium, high, and very high based on the RF prediction probability values. The active fire points (MCD14) have been used for validating the SFDI and accuracy is found to be 85.74% and 87.91% for the years 2018 and 2019 respectively. Thus, the machine learning algorithm is successfully applied for generating the wildfire susceptibility maps.

Keywords: wildfires; RF; MODIS; SFDI; machine learning