Dataset of 'MODIS FIRMS and Maximum entropy method-based forest fire prediction mapping of Sikkim Himalaya.'

Globally incidents of forest fire
events are in the rise due to human encroachment into wilderness and climate
change. Likewise, Sikkim 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 = 0.78, 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.