Characteristics of Spatiotemporal Changes in the Occurrence of Forest Fires

The purpose of this study is to understand the characteristics of the spatial distribution of forest fire occurrences with the local indicators of temporal burstiness in Korea. Forest fire damage data were produced in the form of areas by combining the forest fire damage ledger information with VIIRS-based forest fire occurrence data. Then, detrended fluctuation analysis and the local indicator of temporal burstiness were applied. In the results, the forest fire occurrence follows a self-organized criticality mechanism, and the temporal irregularities of fire occurrences exist. When the forest fire occurrence time series in Gyeonggi-do Province, which had the highest value of the local indicator of temporal burstiness, was checked, it was found that the frequency of forest fires was increasing at intervals of about 10 years. In addition, when the frequencies of forest fires and the spatial distribution of the local indicators of forest fire occurrences were compared, it was found that there were spatial differences in the occurrence of forest fires. This study is meaningful in that it analyzed the time series characteristics of the distribution of forest fires in Korea to understand that forest fire occurrences have long-term temporal correlations and identified areas where the temporal irregularities of forest fire occurrences are remarkable with the local indicators of temporal burstiness.

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Characteristics of Korean Forest Fires and Forest Fire Policies in the Joseon Dynasty Period (1392–1910) Derived From Historical Records

Forests ◽

10.3390/f10010029 ◽

2019 ◽

Vol 10 (1) ◽

pp. 29

Author(s):

Donghyun Kim

Keyword(s):

Forest Fire ◽

Forest Fires ◽

East Coast ◽

Historical Record ◽

Capital City ◽

Fire Damage ◽

Historical Records ◽

Royal Authority ◽

Joseon Dynasty ◽

Fire Stations

This study examined the records of forest fire outbreaks and characteristics over the 518 years of the Joseon Dynasty period (1392–1910) through the analysis of major historical records of Korea. The historical books used in this study were 14 major national historical books, and include the Annals of the Joseon Dynasty (朝鮮王朝實錄), the Diaries of the Royal Secretariat (承政院日記), and the literature was examined, centering on official records of the royal palace in the Joseon Dynasty period. The contents of forest fires recorded in the historical record literature include the overviews of outbreak, forest fire types, and forest fire damage. According to the results of analysis of historical records, the largest forest fire damage was in the forest fire that occurred on the east coast in 1672, in which 65 persons died and in the forest fire that occurred in the same area in 1804, in which 61 persons died and 2600 private houses were destroyed by fire. The causes of fire outbreak were shown to be unknown causes in 42 cases, accidental fires in 10 cases, arson in 3 cases, thunder strike in 3 cases, hunting activities in 2 cases, child playing with fire in 1 case, cultivating activities in 1 case, and house fire in 1 case. Forest fire outbreaks were analyzed by region and by season and according to the results, 56% (39 cases) of the forest fires broke out on the east coast and 73% (46 cases) broke out in the spring. Forest fire policies include those for general forests, those for reserved forests, those for prohibited forests, those for capital city forests, those for royal family’s graves, royal ancestral shrine, and placenta chamber, those for hunting grounds such as martial art teaching fields, and relief policies for people in areas damaged by forest fires, forest fire policies for national defense facilities such as beacon fire stations, and burning and burning control policies for pest control. In conclusion, due to the seriousness of forest fires in the Joseon Dynasty period, the royal authority and local administrative agencies made various forest fire prevention policies, policies for stabilization of the people’s livelihood damaged due to forest fires, and methods to manage major facilities in forests.

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The Temporal-Spatial Distribution and Information-Diffusion-Based Risk Assessment of Forest Fires in China

Sustainability ◽

10.3390/su132413859 ◽

2021 ◽

Vol 13 (24) ◽

pp. 13859

Author(s):

Shu Wu

Keyword(s):

Risk Assessment ◽

Principal Component Analysis ◽

Spatial Distribution ◽

Forest Fire ◽

Forest Fires ◽

Inner Mongolia ◽

Information Diffusion ◽

Principal Component ◽

Component Analysis ◽

Expected Number

As forest fires are becoming a recurrent and severe issue in China, their temporal-spatial information and risk assessment are crucial for forest fire prevention and reduction. Based on provincial-level forest fire data during 1998–2017, this study adopts principal component analysis, clustering analysis, and the information diffusion theory to estimate the temporal-spatial distribution and risk of forest fires in China. Viewed from temporality, China’s forest fires reveal a trend of increasing first and then decreasing. Viewed from spatiality, provinces characterized by high population density and high coverage density are seriously affected, while eastern coastal provinces with strong fire management capabilities or western provinces with a low forest coverage rate are slightly affected. Through the principal component analysis, Hunan (1.33), Guizhou (0.74), Guangxi (0.51), Heilongjiang (0.48), and Zhejiang (0.46) are found to rank in the top five for the severity of forest fires. Further, Hunan (1089), Guizhou (659), and Guanxi (416) are the top three in the expected number of general forest fires, Fujian (4.70), Inner Mongolia (4.60), and Heilongjiang (3.73) are the top three in the expected number of large forest fires, and Heilongjiang (59,290), Inner Mongolia (20,665), and Hunan (5816) are the top three in the expected area of the burnt forest.

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Chinese forest fire occurrence prediction based on machine learning methods

10.21203/rs.3.rs-62305/v2 ◽

2021 ◽

Author(s):

yudong Li ◽

Zhongke Feng ◽

Ziyu Zhao ◽

Wenyuan Ma ◽

Shilin Chen ◽

...

Keyword(s):

Random Forest ◽

Forest Fire ◽

Forest Fires ◽

Prevention And Control ◽

Prediction Models ◽

Area Under The Curve ◽

Fire Prevention ◽

Fire Occurrence ◽

Forest Fire Prevention ◽

And Control

Abstract Forest fires can cause serious harm. Scientifically predicting forest fires is an important basis for preventing them. Currently, there is little research on the prediction of long time-series forest fires in China. Choosing a suitable forest fire prediction model and predicting the probability of Chinese forest fire occurrence are of great importance to China’s forest fire prevention and control work. Based on fire hotspot, meteorological, terrain, vegetation, infrastructure, and socioeconomic data collected from 2003 to 2016, we used a random forest model as a feature-selection method to identify 13 major drivers of forest fires in China. The forest fire prediction models developed in this study are based on four machine-learning algorithms: an artificial neural network, a radial basis function network, a support-vector machine, and a random forest. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area under the curve. We used the optimal model to obtain the probability of forest fire occurrence in various provinces in China and created a spatial distribution map of the areas with high incidences of forest fires. The results showed that the prediction accuracy of the four forest fire prediction models was between 75.8% and 89.2%, and the area under the curve value was between 0.840 and 0.960. The random forest model had the highest accuracy (89.2%) and area under the curve value (0.96); thus, it was used as the optimal model to predict the probability of forest fire occurrence in China. The prediction results indicate that the areas with high incidences of forest fires are mainly concentrated in north-eastern China (Heilongjiang Province and northern Inner Mongolia Autonomous Region) and south-eastern China (including Fujian Province and Jiangxi Province). In areas at high risk of forest fire, management departments can improve forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study helps in understanding the main drivers of forest fires in China, provides a reference for the selection of high-precision forest fire prediction models, and provides a scientific basis for China’s forest fire prevention and control work.

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Change of Persistence in European Electricity Spot Prices

10.20944/preprints202112.0277.v1 ◽

2021 ◽

Author(s):

Leonardo Rydin Gorjão ◽

Dirk Witthaut ◽

Pedro G. Lind ◽

Wided Medjroubi

Keyword(s):

Time Series ◽

Temporal Correlation ◽

Fluctuation Analysis ◽

Spot Markets ◽

Spot Prices ◽

Temporal Correlations ◽

Power Exchange ◽

Continuous Trading ◽

Short Time ◽

Detrended Fluctuation

The European Power Exchange has introduced day-ahead auctions and continuous trading spot markets to facilitate the insertion of renewable electricity. These markets are designed to balance excess or lack of power in short time periods, which leads to a large stochastic variability of the electricity prices. Furthermore, the different markets show different stochastic memory in their electricity price time series, which seem to be the cause for the large volatility. In particular, we show the antithetical temporal correlation in the intraday 15 minutes spot markets in comparison to the day-ahead hourly market. We contrast the results from Detrended Fluctuation Analysis (DFA) to a new method based on the Kramers–Moyal equation in scale. For very short term (< 12 hours), all price time series show positive temporal correlations (Hurst exponent H > 0.5) except for the intraday 15 minute market, which shows strong negative correlations (H < 0.5). For longer term periods covering up to two days, all price time series are anti-correlated (H < 0.5).

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A Probabilistic Method Predicting Forest Fire Occurrence Combining Firebrands and the Weather-Fuel Complex in the Northern Part of the Daxinganling Region, China

Forests ◽

10.3390/f9070428 ◽

2018 ◽

Vol 9 (7) ◽

pp. 428 ◽

Cited By ~ 3

Author(s):

Ping Sun ◽

Yunlin Zhang

Keyword(s):

Forest Fire ◽

Forest Fires ◽

Probabilistic Method ◽

Fire Risk ◽

The United States ◽

Fire Danger ◽

Fire Occurrence ◽

Weather Variables ◽

Rating Method ◽

Actual Fire

The fire danger rating method currently used in the northern part of the Daxinganling Region with the most severe forest fires in China only uses weather variables without considering firebrands. The discrepancy between fire occurrence and fire risk by FFDWR (Forest Fire-Danger Weather Rating, a method issued by the National Meteorological Bureau, that is used to predict forest fire probability through links between forest fire occurrence and weather variables) in the northern part is more obvious than that in the southern part. Great discrepancy has emerged between fire danger predicted by the method and actual fire occurrence in recent years since a strict firebrand prohibition policy has significantly reduced firebrands in the region. A probabilistic method predicting fire probability by introducing an Ignition Component (IC) in the National Fire Danger Rating System (NFDRS) adopted in the United States to depict effects of both firebrand and weather-fuel complex on fire occurrence is developed to solve the problem. The suitability and accuracy of the new method in the region were assessed. Results show that the method is suitable in the region. IC or the modified IC can be adopted to depict the effect of the weather-fuel complex on fire occurrence and to rate fire danger for periods with fewer firebrands. Fire risk classes and corresponding preparedness level can be determined from IC in the region. Methods of the same principle could be established to diminish similar discrepancy between actual fire occurrence and fire danger in other countries.

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Energy-Efficient Forest Fire Prediction Model Based on Two-Stage Adaptive Duty-Cycled Hybrid X-MAC Protocol

Sensors ◽

10.3390/s18092960 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2960 ◽

Cited By ~ 1

Author(s):

Jin-Gu Kang ◽

Dong-Woo Lim ◽

Jin-Woo Jung

Keyword(s):

Prediction Model ◽

Forest Fire ◽

Duty Cycle ◽

Forest Fires ◽

Energy Efficient ◽

Mac Protocol ◽

Fire Occurrence ◽

Fire Monitoring ◽

Environmental Status ◽

Environment Performance

This paper proposes an adaptive duty-cycled hybrid X-MAC (ADX-MAC) protocol for energy-efficient forest fire prediction. The Asynchronous sensor network protocol, X-MAC protocol, acquires additional environmental status details from each forest fire monitoring sensor for a given period, and then changes the duty-cycle sleep interval to efficiently calculate forest fire occurrence risk according to the environment. Performance was verified experimentally, and the proposed ADX-MAC protocol improved throughput by 19% and was 24% more energy efficient compared to the X-MAC protocol. The duty-cycle was shortened as forest fire probability increased, ensuring forest fires were detected at faster cycle rate.

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Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method

Forests ◽

10.3390/f12010005 ◽

2020 ◽

Vol 12 (1) ◽

pp. 5

Author(s):

Slobodan Milanović ◽

Nenad Marković ◽

Dragan Pamučar ◽

Ljubomir Gigović ◽

Pavle Kostić ◽

...

Keyword(s):

Logistic Regression ◽

Forest Fire ◽

Forest Fires ◽

Fire Suppression ◽

Standard Procedure ◽

Predictive Ability ◽

Fire Risk ◽

Fire Season ◽

Fire Occurrence ◽

Probability Mapping

Forest fire risk has increased globally during the previous decades. The Mediterranean region is traditionally the most at risk in Europe, but continental countries like Serbia have experienced significant economic and ecological losses due to forest fires. To prevent damage to forests and infrastructure, alongside other societal losses, it is necessary to create an effective protection system against fire, which minimizes the harmful effects. Forest fire probability mapping, as one of the basic tools in risk management, allows the allocation of resources for fire suppression, within a fire season, from zones with a lower risk to those under higher threat. Logistic regression (LR) has been used as a standard procedure in forest fire probability mapping, but in the last decade, machine learning methods such as fandom forest (RF) have become more frequent. The main goals in this study were to (i) determine the main explanatory variables for forest fire occurrence for both models, LR and RF, and (ii) map the probability of forest fire occurrence in Eastern Serbia based on LR and RF. The most important variable was drought code, followed by different anthropogenic features depending on the type of the model. The RF models demonstrated better overall predictive ability than LR models. The map produced may increase firefighting efficiency due to the early detection of forest fire and enable resources to be allocated in the eastern part of Serbia, which covers more than one-third of the country’s area.

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Multifractal Detrended Fluctuation Analysis of Temperature Reanalysis Data over Greece

Atmosphere ◽

10.3390/atmos10060336 ◽

2019 ◽

Vol 10 (6) ◽

pp. 336 ◽

Cited By ~ 6

Author(s):

Kostas Philippopoulos ◽

Nikolaos Kalamaras ◽

Chris G. Tzanis ◽

Despina Deligiorgi ◽

Ioannis Koutsogiannis

Keyword(s):

Time Series ◽

Spatial Distribution ◽

Detrended Fluctuation Analysis ◽

Multifractal Spectrum ◽

Spectral Width ◽

Reanalysis Data ◽

Fluctuation Analysis ◽

Spectral Parameters ◽

Multifractal Detrended Fluctuation Analysis ◽

Detrended Fluctuation

The Multifractal Detrended Fluctuation Analysis (MF-DFA) is used to examine the scaling behavior and the multifractal characteristics of the mean daily temperature time series of the ERA-Interim reanalysis data for a domain centered over Greece. The results showed that the time series from all grid points exhibit the same behavior: they have a positive long-term correlation and their multifractal structure is insensitive to local fluctuations with a large magnitude. Special emphasis was given to the spatial distribution of the main characteristics of the multifractal spectrum: the value of the Hölder exponent, the spectral width, the asymmetry, and the truncation type of the spectra. The most interesting finding is that the spatial distribution of almost all spectral parameters is decisively determined by the land–sea distribution. The results could be useful in climate research for examining the reproducibility of the nonlinear dynamics of reanalysis datasets and model outputs.

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Forest Fires Extinguishing Using Suitable Fire-Fighting Equipment

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1001.318 ◽

2014 ◽

Vol 1001 ◽

pp. 318-323 ◽

Cited By ~ 3

Author(s):

Mikuláš Monosi

Keyword(s):

Forest Fire ◽

Natural Environment ◽

Forest Fires ◽

Optimization Model ◽

Slovak Republic ◽

Fire Fighting ◽

Fire Occurrence ◽

Equipment Selection ◽

Operational Conditions

The paper deals with the problem of forest fire occurrence in the Slovak Republic. It in more detail describes the number of fires in natural environment, analyses the problem of suitable fire equipment selection to fight the forest fires in meaning of the legislation standards. It also describes the optimization model to select the suitable fire equipment, based on the parameters of the natural environment and operational conditions of available fire equipment.

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Altered Temporal Structure of Neural Phase Synchrony in Patients With Autism Spectrum Disorder

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.618573 ◽

2021 ◽

Vol 12 ◽

Author(s):

Huibin Jia ◽

Fei Gao ◽

Dongchuan Yu

Keyword(s):

Autism Spectrum Disorder ◽

Time Series ◽

Temporal Structure ◽

Gamma Oscillations ◽

Brain Regions ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Fluctuation Analysis ◽

Phase Synchrony ◽

Temporal Correlations

Functional connectivity, quantified by phase synchrony, between brain regions is known to be aberrant in patients with autism spectrum disorder (ASD). Here, we evaluated the long-range temporal correlations of time-varying phase synchrony (TV-PS) of electrocortical oscillations in patients with ASD as well as typically developing people using detrended fluctuation analysis (DFA) after validating the scale-invariance of the TV-PS time series. By comparing the DFA exponents between the two groups, we found that those of the TV-PS time series of high-gamma oscillations were significantly attenuated in patients with ASD. Furthermore, the regions involved in aberrant TV-PS time series were mainly within the social ability and cognition-related cortical networks. These results support the notion that abnormal social functions observed in patients with ASD may be caused by the highly volatile phase synchrony states of electrocortical oscillations.

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