Prediction and key drivers analysis of forest surface Dead Fine Fuel Moisture Content: A stacking ensemble learning and IoT-based system

Abstract

Dead Fine Fuel Moisture Content (DFFMC) is a critical factor influencing wildfire risk and fire spread behavior in forest fire management. DFFMC field-measurement relies on manual sampling, suffering from slow response, high labor costs, and limited spatial coverage. Moreover, existing predictive models of DFFMC are mostly based on single machine learning algorithms, which struggle to balance spatial generalization and local fitting capabilities, thereby limiting overall model performance. This study proposes a DFFMC prediction approach that integrates a stacking ensemble learning model with a hybrid dataset from different regions and Internet of Things (IoT) technology, offering the advantages of high accuracy, high spatial generalization, and rapid responsiveness. A stacking ensemble learning model was trained using publicly available international datasets covering diverse ecological and climatic zones. To evaluate the model’s spatial generalization capability, field data collected from Bajia Country Park in Beijing, China, were used exclusively as an independent validation set. The model demonstrated strong predictive performance on the domestic dataset, achieving a correlation coefficient of 0.91 and a mean absolute error below 2. Key drivers analysis revealed that humidity and precipitation are the key drivers of DFFMC. Partial dependence plots indicate nonlinear DFFMC responses when humidity exceeds 60% and precipitation surpasses 3 mm. Bivariate dependence analysis further highlights complex interactions among meteorological factors, underscoring the value of multi-factor modeling for accurate DFFMC prediction and wildfire risk management.