The recent episodes of forest fire in Brazil and Australia of 2019 are tragic reminders of the hazards of the forest fire. Globally incidents of forest fire events are in the rise due to human encroachment into wilderness and climate change. Sikkim with a forest cover of more than 47%, suffers seasonal instances of frequent forest fire during the dry winter months. To address this issue, a GIS-aided and MaxEnt machine learning-based forest fire prediction map has been prepared using forest fire inventory database and maps of environmental features. The study indicates that amongst the environmental features, climatic conditions and proximity to roads are the major determinants of the forest fire. Model validation criteria like ROC curve, correlation coefficient and Cohen’s Kappa show a good predictive capability (AUC = 0.95, COR = 0.78, κ = 0.78). The outcomes of this study in the form of a forest fire prediction map can aid the stakeholders of the forest in taking informed mitigation measures.

The recent episodes of forest fire in Brazil and Australia of 2019 are tragic reminders of the hazards of the forest fire. Globally incidents of forest fire events are in the rise due to human encroachment into wilderness and climate change. Sikkim with a forest cover of more than 47%, suffers seasonal instances of frequent forest fire during the dry winter months. To address this issue, a GIS-aided and MaxEnt machine learning-based forest fire prediction map has been prepared using forest fire inventory database and maps of environmental features. The study indicates that amongst the environmental features, population density and proximity to roads are the major determinants of the forest fire. This indicates the role of human activities on the incidences of a forest fire. Model validation criteria like ROC curve, correlation coefficient and Cohen’s Kappa show a good predictive capability (AUC = 0.95, COR = 0.77, κ = 0.77). The outcomes of this study in the form of a forest fire prediction map can aid the stakeholders of the forest in taking informed mitigation measures.