Fractal dimensions of wildfire spreading

Wang, S.-L.; Lee, H.-I.; Li, S.-P.

doi:10.5194/npg-21-815-2014

Cited by 5 publications

(2 citation statements)

References 23 publications

(29 reference statements)

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“…Although there are several different ways of measuring F, we employed the frequently used box-counting method (Wang et al 2014) in which a square mesh of various sizes r is laid over the image containing the object. The number of mesh-boxes (N r ) containing a part of the image is counted, and fractal dimension F is given by the Figure 1.…”

Section: Discussionmentioning

confidence: 99%

Effects of wind and tree density on forest fire patterns in a mixed-tree species forest

Song

Lee

2016

Forest Science and Technology

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It is well known that global climate change causes an increase in forest fire frequency and severity. Thus, understanding fire dynamics is necessary to comprehend the mitigation of the negative effects of forest fires. Our objective was to inform how fire spreads in a simulated two-species forest with varying wind strengths. The forest in this study was comprised of two different tree species with varying probabilities of transferring fire that was randomly distributed in space at densities (C tot ) ranging from 0.0 (low) to 1.0 (high). We studied the distribution pattern of burnt trees by using local rules of the two-dimensional model. This model incorporated wind blowing from south to north with strength (P w ) ranging from 0.0 (low) to 1.0 (high). Simulation results showed that when C tot > 0.45 the fire covered the entire forest, but when C tot 0.45 the fire did not spread. The wind effect on the variation of the amount of the burnt tree was maximized at the critical density and dramatically decreased with increasing C tot . Additionally, we found that the term of C tot and P w plays an important role in determining the distribution.

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Section: Discussionmentioning

confidence: 99%

Effects of wind and tree density on forest fire patterns in a mixed-tree species forest

Song

Lee

2016

Forest Science and Technology

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“…To solve the problems in geology, many scholars investigate the fractal characteristics of well logging data, and make a great progress (Li, 2003). A lot of theoretical research about the fractal characteristic of well logging curves has been done, which mainly focus on self-similarity, fractal interpolation, fractal correction multi-fractal Figures Wang et al, 2014). Besides, the geological information in well logging data was quantitative interpreted using fractal dimension of well logging curves.…”

Section: Introductionmentioning

confidence: 99%

Comparison of box counting and correlation dimension methods in well logging data analysis associate with the texture of volcanic rocks

Mou

Wang

2016

Preprint

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Introduction Conclusions References Tables Figures Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Abstract Introduction Conclusions References Tables Figures Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion AbstractWe have developed a fractal analysis method to estimate the dimension of well logging curves in Liaohe oil field, China. The box counting and correlation dimension are methods that can be applied to predict the texture of volcanic rocks with calculation the fractal dimension of well logging curves. The well logging curves are composed 5 of gamma ray (GR), compensated neutron logs (CNL), acoustic (AC), density (DEN), Resistivity lateral log deep (R LLD ), every curve contains a total of 6000 logging data. The dimension of well logging curves are calculated using box counting and correlation algorithms respectively. It is shown that two types of dimension of CNL, DEN and AC have the same average value. The box counting dimension of volcanic lava is lower 10 than the pyroclastic rock obviously. The majority of correlation dimension of volcanic lava is lower than the pyroclastic rock, but a small amount of correlation dimension of volcanic lava is equal to the pyroclastic rock. It is demonstrated that the box counting dimension is more suitable for predicting the texture of volcanic rocks. Applications to logging data, A well show the relationship between the fractal dimension and the 15 texture of volcanic rock in certain depth. 25 analysis and calculating method of fractal dimension (Dimri, 1998; Tarquis et al., 2 NPGD Abstract Introduction Conclusions References Tables NPGD Abstract Introduction Conclusions References Tables 10 NPGD Abstract 20The result is shown in Fig. 2. Correlation dimensionCorrelation dimension represent the association degree of data series. On the basis of the embedding theory and the idea of phase space reconstruction, Grassberger 5 NPGDAbstract Introduction Conclusions References Tables NPGD Abstract Introduction Conclusions References Tables 20 7 NPGD Abstract Introduction Conclusions References Tables NPGD Abstract Introduction Conclusions References Tables NPGD Abstract Introduction Conclusions References Tables Abstract Introduction Conclusions References Tables Abstract Introduction Conclusions References Tables Abstract Introduction Conclusions References Tables Abstract Introduction Conclusions References Tables Abstract Introduction Conclusions References Tables

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Automatic mutation feature identification from well logging curves based on sliding t test algorithm

Shang

2018

Cluster Comput

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Fractal dimensions of wildfire spreading

Cited by 5 publications

References 23 publications

Effects of wind and tree density on forest fire patterns in a mixed-tree species forest

Effects of wind and tree density on forest fire patterns in a mixed-tree species forest

Comparison of box counting and correlation dimension methods in well logging data analysis associate with the texture of volcanic rocks

Automatic mutation feature identification from well logging curves based on sliding t test algorithm

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