Robust stacking-based ensemble learning model for forest fire detection

Abstract

Forests reduce soil erosion and prevent drought, wind, and other natural disasters. Forest fres, which threaten millions of hectares of forest area yearly, destroy these precious resources. This study aims to design a deep learning model with high accuracy to intervene in forest fres at an early stage. A stacked-based ensemble learning model is proposed for fre detection from forest landscape images in this context. This model ofers high test accuracies of 97.37%, 95.79%, and 95.79% with hold-out validation, fvefold cross-validation, and tenfold cross-validation experiments, respectively. The artifcial intelligence model developed in this study could be used in real-time systems run on unmanned aerial vehicles to prevent potential disasters in forest areas.