Effects of Weather Factors on Fuel Moisture Contents of Forestland in Chuncheon, South Korea

Chae, Hee Mun; Jeon, Bo Ram; Lee, Si Young; Ohga, Shoji

doi:10.5109/1798137

Cited by 3 publications

(1 citation statement)

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“…The frequency and strength of forest fires are affected by the flammability of fuel that has accumulated in the understory, a factor closely associated with moisture content, which is in turn affected by external environmental factors such as climate and weather. (3)(4)(5) Using the Automatic Weather Stations of the Meteorological Administration and Korea Forest Service, we analyzed the generated wind fields that affect the occurrence of forest fires, and the distribution was found to be highly correlated with mountain characteristics. (6) Forest disasters are driven by weather conditions such as temperature, RH, wind speed, and geographic conditions such as topography, forest type, and structure.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Microclimate Characteristics of Small Mountain Forest in Korea Using Weather Sensor Array

Chae¹

2021

Sensors and Materials

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Section: Introductionmentioning

confidence: 99%

Analysis of Microclimate Characteristics of Small Mountain Forest in Korea Using Weather Sensor Array

Chae¹

2021

Sensors and Materials

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Prediction model of moisture content of dead fine fuel in forest plantations on Maoer Mountain, Northeast China

Masinda

Liu

et al. 2021

J. For. Res.

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Preventing and suppressing forest fires is one of the main tasks of forestry agencies to reduce resource loss and requires a thorough understanding of the importance of factors affecting their occurrence. This study was carried out in forest plantations on Maoer Mountain in order to develop models for predicting the moisture content of dead fine fuel using meteorological and soil variables. Models by Nelson (Can J For Res 14:597–600, 1984) and Van Wagner and Pickett (Can For Service 33, 1985) describing the equilibrium moisture content as a function of relative humidity and temperature were evaluated. A random forest and generalized additive models were built to select the most important meteorological variables affecting fuel moisture content. Nelson's (Can J For Res 14:597–600, 1984) model was accurate for Pinus koraiensis, Pinus sylvestris, Larix gmelinii and mixed Larix gmelinii—Ulmus propinqua fuels. The random forest model showed that temperature and relative humidity were the most important factors affecting fuel moisture content. The generalized additive regression model showed that temperature, relative humidity and rain were the main drivers affecting fuel moisture content. In addition to the combined effects of temperature, rainfall and relative humidity, solar radiation or wind speed were also significant on some sites. In P. koraiensis and P. sylvestris plantations, where soil parameters were measured, rain, soil moisture and temperature were the main factors of fuel moisture content. The accuracies of the random forest model and generalized additive model were similar, however, the random forest model was more accurate but underestimated the effect of rain on fuel moisture.

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Modification and Comparison of Methods for Predicting the Moisture Content of Dead Fuel on the Surface of Quercus mongolica and Pinus sylvestris var. mongolica under Rainfall Conditions

Hu,

Ma,

Gao

et al. 2023

Fire

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The surface fine dead fuel moisture content (FFMC) is an important factor in predicting forest fire risk and is influenced by various meteorological factors. Many prediction methods rely on temperature and humidity as factors, resulting in poor model prediction accuracy under rainfall conditions. At the same time, there is an increasing number of methods based on machine learning, but there is still a lack of comparison with traditional models. Therefore, this paper selected the broad-leaved forest tree species Quercus mongolica and the coniferous forest species Pinus sylvestris var. mongolica in Northeast China. Taking surface dead fine fuel as the research object, we used indoor simulated rainfall experiments to explore the impact of rainfall on the surface dead fuel moisture content. The prediction model for surface dead fuel moisture content was modified by the direct estimation method. Finally, using field data, the direct estimation method and convolution neural network (CNN) model were used in the comparison. The rainfall simulation results showed that the indoor fuel moisture content had a logarithmic increasing trend. Rainfall and previous fuel moisture content had a significant impact on the fuel moisture content prediction model, and both the relational model and nonlinear model performed well in predicting fuel moisture content under indoor rainfall conditions. Under field conditions, humidity, temperature and rainfall played a significant role in fuel moisture content. Compared with the unmodified direct estimation method, the modified direct estimation method significantly improved the prediction accuracy and the goodness of fit (R2) increased from 0.85–0.94 to 0.94–0.96. Mean absolute error (MAE) decreased from 9.18–18.33% to 6.86–10.74%, and mean relative error (MRE) decreased from 3.97–17.18% to 3.53–14.48%. The modified direct estimation method has higher prediction accuracy compared with the convolutional neural network model; the R2 value was above 0.90, MAE was below 8.11%, and MRE was below 8.87%. The modified direct estimation method had the best prediction effect among them. This study has a certain reference value for the prediction model of surface fuel moisture content in post-rainfall fire risk assessment and is also of great significance for forest fire management in Northeast China.

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Effects of Weather Factors on Fuel Moisture Contents of Forestland in Chuncheon, South Korea

Cited by 3 publications

References 0 publications

Analysis of Microclimate Characteristics of Small Mountain Forest in Korea Using Weather Sensor Array

Analysis of Microclimate Characteristics of Small Mountain Forest in Korea Using Weather Sensor Array

Prediction model of moisture content of dead fine fuel in forest plantations on Maoer Mountain, Northeast China

Modification and Comparison of Methods for Predicting the Moisture Content of Dead Fuel on the Surface of Quercus mongolica and Pinus sylvestris var. mongolica under Rainfall Conditions

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