Optimizing machine learning models for enhanced forest fire susceptibility mapping in Gia Lai province
This study advances forest fire susceptibility mapping in Gia Lai province by leveraging optimized machine learning models. We evaluated five models -Deep Neural Networks (DNN), Random Forest (RF), Gradient Boosting (GB), Logistic Regression (LR), and Support Vector Machines (SVM) - using a dataset of 2,827 fire incidents (2007÷2021), an equal number of non-fire points, and 12 influencing factors: slope, aspect, elevation, curvature, land use, NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), NDMI (Normalized Difference Moisture Index), temperature, wind speed, relative humidity, and rainfall.
