Fire Risk Prediction Using Remote Sensed Products: A Case of Cambodia

Forest fire is threatening human life in monsoon countries, such as Cambodia, which suffers from forest fire frequently. Developing an efficient method to predict fire risk for large areas is becoming significantly important. However, the methods used in fire risk prediction are mostly based on field-based meteorological data, and the coefficients are harddefined, heavily depending on user experience. We propose to use a user-friendly machine learning method, Random Forest(TM), to train a regression model by synthesizing publicly available remote sensed products to predict fire risk ratings at pixel-level in eight-day advance. The structure of our model synthesizes features in three-time intervals T1, T2, and T3 to predict fire occurrence probability in T4. The experiment demonstrates the efficiency of such model in predicting fire occurrence with a correlation coefficient of 0.987 and mean square error being 0.00285. It results in a practical way to predict fire risk and prevent fires.