Implementation of a Random Forest Classifier to Examine Wildfire Predictive Modelling in Greece Using Diachronically Collected Fire Occurrence and Fire Mapping Data

Forest fires cause severe damages in ecosystems, human lives and infrastructure globally. This situation tends to get worse in the next decades due to climate change and the expected increase in the length and severity of the fire season. Thus, the ability to develop a method that reliably models the risk of fire occurrence is an important step towards preventing, confronting and limiting the disaster. Different approaches building upon Machine Learning (ML) methods for predicting wildfires and deriving a better understanding of fires’ regimes have been devised. This study demonstrates the development of a Random Forest (RF) classifier to predict “fire”/“non fire” classes in Greece. For this a prototype and representative for the Mediterranean ecosystem database of validated fires and fire related features has been created. The database is populated with data (e.g. Earth Observation derived biophysical parameters and daily collected climatic and weather data) for a period of nine years (2010–2018). Spatially it refers to grid cells of 500 m wide where Active Fires (AF) and Burned Areas/Burn Scars (BSM) were reported during that period. By using feature ranking techniques as Chi-squared and Spearman correlations the study showcases the most significant wildfire triggering variables. It also highlights the extent by which the database and selected features scheme can be used to successfully train a RF classifier for deriving “fire”/“non-fire” predictions over the country of Greece in the prospect of generating a dynamic fire risk system for daily assessments.