Prediction and key drivers analysis of forest surface Dead Fine Fuel Moisture Content: A stacking ensemble learning and IoT-based system
Bibliographic record
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. • Develops an ensemble learning framework for adaptive DFFMC modeling across diverse regions. • Designs a multi-sensor IoT node for real-time meteorological data acquisition in forested areas. • Experimental analysis reveals humidity and rainfall as key factors influencing DFFMC dynamics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".