Integrated Anomaly Detection and Early Warning System for Forest Fires in the Odisha Region

The present study aims to develop a random forest algorithm-based classifier to predict the occurrence of fire events using observed meteorological parameters a day in advance. We considered the skin temperature, the air temperature close to the surface, the humidity close to the surface level, and soil moisture as important meteorological factors influencing forest fire occurrence. Twenty additional parameters were derived based on these four parameters that account for the energy exchanged in sensible and latent forms and the change in parameters in recent trends. We used the mutual information approach to identify critical meteorological parameters that carry significant information about fire occurrence the next day. The top nine parameters were then fed as input to the random forest algorithm to predict fire/no fire the next day. The weighted data sampling and SMOTE techniques were employed to address the class imbalance in the fire data class. Both techniques correctly classified fire incidents well, given the meteorological input from the previous days. This study also showed that as the class imbalance increases to 1:9, the performance based on the precision, recall, F1 score, and accuracy are maximum, showing the model's ability to perform with class imbalance. Both techniques helped the random forest algorithm forecast fire instances as the data sample size increased.