Predicting potential fire severity in Türkiye's diverse forested areas: a SHAP-integrated random forest classification approach.

This study introduces a methodology that integrates SHAP (SHapley Additive exPlanations) analysis with Random Forest (RF) classification to enhance the prediction accuracy of fire severity across diverse forested regions in Türkiye. Leveraging a comprehensive forest fire database spanning from 2018 to 2022 and utilizing the Google Earth Engine (GEE) platform, 436 fire events ranging from 9 to 53,764.1 hectares were automatically detected and mapped using the difference Normalized Burn Ratio (dNBR). Subsequently, a robust fire severity model was developed by incorporating 19 variables, including biophysical, topographic, climatic, and vegetation-related factors. The RF classification achieved noteworthy performance metrics, with an overall accuracy of 0.75, a Kappa value of 0.61, and a macro-average AUC value of 0.88. Furthermore, the integration of SHAP analysis provided insightful contributions to the RF classification model, elucidating the impacts of individual input features. Notably, variables such as NDMI (Normalized Difference Moisture Index), LAI (Leaf Area Index), and LWVI (Land Water Vegetation Index) emerged as significant influencers, followed by WSPD (Wind Speed) and LST (Land Surface Temperature). Additionally, an analysis of fire severity distribution across fuel types (FMT) revealed intricate patterns, underscoring the complex relationships between vegetation composition and fire behavior. The findings of this study have implications for forest management and wildfire risk assessment, offering valuable insights for decision-making processes. Furthermore, the integration of SHAP analysis with RF classification enhances the interpretability and transparency of machine learning-based fire severity prediction models, contributing to the advancement of fire management strategies. [ABSTRACT FROM AUTHOR]