Abstract
Forest fire modeling often requires estimates of fuel moisture status. Among the various fuel variables used for fire modeling studies, the 10-h fuel moisture content (10-h FMC) is a promising predictor since it can be automatically measured in real time at study sites, yielding more information for fire models. Here, the performance of 10-h FMC models based on three different approaches, including regression (MREG), machine learning algorithms (MML) with random forest and support vector machine, and a process-based model (MFSMM), were compared. In addition, whole-year models of each type were compared with their respective seasonal models to explore whether the development of separate seasonal models yielded better estimates. Meteorological conditions and 10-h FMC were measured each minute for 18 months in and near a forest site and used for constructing and examining the 10-h FMC models. In the assessments, MML showed the best performance (R2 = 0.77–0.82 and root mean squared error [RMSE] = 2.05–2.84%). The introduction of the correction coefficient into MREG improved its estimates (R2 improved from 0.56–0.58 to 0.68–0.70 and RMSE improved from 3.13–3.85% to 2.64–3.27%) by reducing the errors associated with high 10-h FMC values. MFSMM showed the worst performance (R2 = 0.41–0.43 and RMSE = 3.70–4.39%), which could possibly be attributed to the lack of radiation input from the study sites as well as the particular fuel moisture stick sensor that was used. Whole-year models and seasonal models showed almost equal performance because 10-h FMC varied in response to atmospheric moisture conditions rather than specific seasonal patterns. The adoption of a hybrid modeling approach that blends machine-learning and process-based approaches may yield better predictability and interpretability. This study provides additional evidence of the lagged response of 10-h FMC after rainfall, and suggests a new way of accounting for this response in a regression model. Our approach using comparisons among models can be utilized for other fire modeling studies, including those involving fire danger ratings.
Highlights
Dead fuel moisture content (FMC) is an important factor in forest fire research and in operational systems for fire danger rating or fire behavior because it affects the occurrence, spread, and intensity of the forest fire, and the survival of vegetation near the fire [1]
We have compared the capability of various types of 10-h FMC model, including regression, machine learning, and process-based models
Our R2 results show that the machine learning-based models performed the best, followed by the regression models, with the process-based model doing the worst
Summary
Dead fuel moisture content (FMC) is an important factor in forest fire research and in operational systems for fire danger rating or fire behavior because it affects the occurrence, spread, and intensity of the forest fire, and the survival of vegetation near the fire [1]. The numbers indicate the approximate time in hours necessary for a given size fuel to lose. Forests 2020, 11, 982; doi:10.3390/f11090982 www.mdpi.com/journal/forests (EMC), and are related to fuel size [3]. Fuel moisture content is usually measured by a manual oven-drying process e.g., [5,6,7]. 10-h FMC can be measured automatically under near-real-time conditions by using a commercial standardized fuel stick sensor, e.g., [8,9,10]
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