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

Forests ◽  
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.

What drives forest fire in Fujian, China? Evidence from logistic regression and Random Forests

2016 ◽  
Vol 25 (5) ◽  
pp. 505 ◽  
Author(s):  
Futao Guo ◽  
Guangyu Wang ◽  
Zhangwen Su ◽  
Huiling Liang ◽  
Wenhui Wang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Regional Scale ◽  
Fire Risk ◽  
Driving Factors ◽  
Fire Season ◽  
Fire Occurrence ◽  
Climate Factors ◽  
Local Factors ◽  
Risk Zones

We applied logistic regression and Random Forest to evaluate drivers of fire occurrence on a provincial scale. Potential driving factors were divided into two groups according to scale of influence: ‘climate factors’, which operate on a regional scale, and ‘local factors’, which includes infrastructure, vegetation, topographic and socioeconomic data. The groups of factors were analysed separately and then significant factors from both groups were analysed together. Both models identified significant driving factors, which were ranked in terms of relative importance. Results show that climate factors are the main drivers of fire occurrence in the forests of Fujian, China. Particularly, sunshine hours, relative humidity (fire seasonal and daily), precipitation (fire season) and temperature (fire seasonal and daily) were seen to play a crucial role in fire ignition. Of the local factors, elevation, distance to railway and per capita GDP were found to be most significant. Random Forest demonstrated a higher predictive ability than logistic regression across all groups of factors (climate, local, and climate and local combined). Maps of the likelihood of fire occurrence in Fujian illustrate that the high fire-risk zones are distributed across administrative divisions; consequently, fire management strategies should be devised based on fire-risk zones, rather than on separate administrative divisions.

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

Forests ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 428 ◽  
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.

A logistic model for predicting daily people-caused forest fire occurrence in Ontario

1987 ◽  
Vol 17 (5) ◽  
pp. 394-401 ◽  
Author(s):  
D. L. Martell ◽  
S. Otukol ◽  
B. J. Stocks
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Probability Distribution ◽  
Field Test ◽  
Forest Fire ◽  
Logistic Model ◽  
Northern Region ◽  
Fire Season ◽  
Fire Occurrence ◽  
Analysis Techniques

The authors describe the development of a procedure that can be used to predict daily people-caused forest fire occurrence in the Northern Region of the province of Ontario. The procedure is based on the use of logistic regression analysis techniques to predict the probability of a fire day and the assumption that a Poisson probability distribution can be used to model daily people-caused forest fire occurrence. The results of a field test that was conducted during the summer portion of the 1984 fire season indicate the procedure works well during relatively wet periods.

scholarly journals Dynamic forest fire risk evaluation in Poland

2020 ◽  
Vol 62 (2) ◽  
pp. 139-144
Author(s):  
Ryszard Szczygieł ◽  
Mirosław Kwiatkowski ◽  
Bartłomiej Kołakowski ◽  
Józef Piwnicki
Keyword(s):  
Moisture Content ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Risk ◽  
New Method ◽  
Fire Season ◽  
List Type ◽  
Method Of Evaluation ◽  
The Given ◽  
Forest Fire Risk

AbstractThe weather conditions determine the dynamic forest fire risk. In Poland, the dynamic forest fire risk is calculated using a method elaborated at the Forest Research Institute. The forest fire risk degree (4-level scale) is calculated every day at 9:00 am and at 1:00 pm during the fire season (1.03 till 30.09) for 60 prognostic zones selected on the basis of stand and climatic conditions. 97% of all annual forest fires occur during the fire season. Surface fires are a significant part of the fires (90%) and occur in forest stands where pine is the dominant species. The purpose of the research was to prepare a new method of forecasting forest fire risk, which would enable a more precise method of evaluation of the risk of an outbreak of fire in relation to the existing and forecast meteorological conditions in forests. The results obtained during testing of this method indicate a high accuracy in forecasting fire risk and a satisfactory precision of formulae for calculating moisture content of pine litter.The assumptions of the new method included: –possibility of determining the actual risk of fire for the given area, being the average for all measurement points located on the terrain equally those in which the moisture content measurement of litter has not been performed,–possibility of forecasting the risk of forest fire for the afternoon in the morning hours of the given day,–possibility of forecasting fire risk for the following day,–forecasting moisture content of litter for the afternoon and of the given day and for the following day,–drawing up a method enabling limitation of operational costs of fire prevention system.

scholarly journals Quantitative Analysis of Forest Fires in Southeastern Australia Using SAR Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2386
Author(s):  
Aqil Tariq ◽  
Hong Shu ◽  
Qingting Li ◽  
Orhan Altan ◽  
Mobushir Riaz Khan ◽  
...  
Keyword(s):  
Forest Fires ◽  
Prescribed Burning ◽  
Fire Risk ◽  
Fire Season ◽  
Prescribed Burn ◽  
Backscatter Coefficient ◽  
Prescribed Burns ◽  
Southeastern Australia ◽  
Sar Data ◽  
Sar Imagery

Prescribed burning is a common strategy for minimizing forest fire risk. Fire is introduced under specific environmental conditions, with explicit duration, intensity, and rate of spread. Such conditions deviate from those encountered during the fire season. Prescribed burns mostly affect surface fuels and understory vegetation, an outcome markedly different when compared to wildfires. Data on prescribed burning are crucial for evaluating whether land management targets have been reached. This research developed a methodology to quantify the effects of prescribed burns using multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) imagery in the forests of southeastern Australia. C-band SAR datasets were specifically used to statistically explore changes in radar backscatter coefficients with the intensity of prescribed burns. Two modeling approaches based on pre- and post-fire ratios were applied for evaluating prescribed burn impacts. The effects of prescribed burns were documented with an overall accuracy of 82.3% using cross-polarized backscatter (VH) SAR data under dry conditions. The VV polarization indicated some potential to detect burned areas under wet conditions. The findings in this study indicate that the C-band SAR backscatter coefficient has the potential to evaluate the effectiveness of prescribed burns due to its sensitivity to changes in vegetation structure.

scholarly journals Temporal variations and change in forest fire danger in Europe for 1960–2012

2014 ◽  
Vol 14 (6) ◽  
pp. 1477-1490 ◽  
Author(s):  
A. Venäläinen ◽  
N. Korhonen ◽  
O. Hyvärinen ◽  
N. Koutsias ◽  
F. Xystrakis ◽  
...  
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
National Level ◽  
Temporal Variations ◽  
Fire Danger ◽  
Fire Season ◽  
Fire Weather ◽  
Fire Weather Index ◽  
Current Season ◽  
Forest Fire Danger

Abstract. Understanding how fire weather danger indices changed in the past and how such changes affected forest fire activity is important in a changing climate. We used the Canadian Fire Weather Index (FWI), calculated from two reanalysis data sets, ERA-40 and ERA Interim, to examine the temporal variation of forest fire danger in Europe in 1960–2012. Additionally, we used national forest fire statistics from Greece, Spain and Finland to examine the relationship between fire danger and fires. There is no obvious trend in fire danger for the time period covered by ERA-40 (1960–1999), whereas for the period 1980–2012 covered by ERA Interim, the mean FWI shows an increasing trend for southern and eastern Europe which is significant at the 99% confidence level. The cross correlations calculated at the national level in Greece, Spain and Finland between total area burned and mean FWI of the current season is of the order of 0.6, demonstrating the extent to which the current fire-season weather can explain forest fires. To summarize, fire risk is multifaceted, and while climate is a major determinant, other factors can contribute to it, either positively or negatively.

scholarly journals MULTICRITERIA ANALYSIS FOR IDENTIFYING FOREST FIRE RISK ZONES IN THE BIOLOGICAL RESERVE OF THE SAMA CORDILLERA, BOLIVIA

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 113-118
Author(s):  
S. Mariscal ◽  
M. Ríos ◽  
F. Soria
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Risk Model ◽  
Multicriteria Analysis ◽  
Fire Risk ◽  
Temperature And Precipitation ◽  
Spatial Risk ◽  
Forest Fire Risk ◽  
Very High ◽  
Biological Reserve

Abstract. Forest fires have negative effects on biodiversity, the atmosphere and human health. The paper presents a spatial risk model as a tool to assess them. Risk areas refer to sectors prone to the spread of fire, in addition to the influence of human activity through remote sensing and multi-criteria analysis. The analysis includes information on land cover, land use, topography (aspect, slope and elevation), climate (temperature and precipitation) and socio-economic factors (proximity to settlements and roads). Weights were assigned to each in order to generate the forest fire risk map. The investigation was carried for a Biological Reserve in Bolivia because of the continuous occurrence of forest fires. Five risk categories for forest fires were derived: very high, high, moderate, low and very low. In summary, results suggest that approximately 67% of the protected area presents a moderate to very high risk; in the latter, populated areas are not dense which reduces the actual risk to the type of events analyzed.

scholarly journals River Flows are a Reliable Index of Forest Fire Risk in the Temperate Tasmanian Wilderness World Heritage Area, Australia

2021 ◽  
Author(s):  
David MJS Bowman ◽  
Grant J Williamson
Keyword(s):  
Forest Fire ◽  
River Flow ◽  
Meteorological Data ◽  
Fire Risk ◽  
World Heritage ◽  
Fire Season ◽  
River Flows ◽  
World Heritage Area ◽  
Heritage Area ◽  
Forest Fire Risk

Fire risk can be defined as the probability that a fire will spread. Reliable monitoring of fire risk is essential for effective landscape management. Compilation of fire risk records enable identification of seasonal and inter-annual patterns and provide a baseline to evaluate the trajectories in response to climate change. Typically, fire risk is estimated from meteorological data. In regions with sparse meteorological station coverage environmental proxies provide important additional data stream for estimating past and current fire risk. Here we use a 60-year record of daily flows from two rivers (Franklin and Davey) in the remote Tasmanian Wilderness World Heritage Area (TWWHA) to characterize seasonal patterns in fire risk in temperate Eucalyptus and rainforests. We show that river flows are strongly related to landscape soil moisture estimates derived from down-scaled re-analysis of meteorological data available since 1990. To identify river flow thresholds where forests are likely to burn, we relate river flows to known forest fires that have occurred in the previously defined ecohydrological domains that surround the Franklin and Davey catchments. Our analysis shows that the fire season in the TWWHA is centered on February (70% of all years below the median threshold), with shoulders on December-January and March. Since 1954 forest fire can occur in at least one month for all but four summers in the ecohydrological domain that includes the Franklin catchment, and since 1964 fire fires could occur in at least one month in every summer in the ecohydrological domain that includes the Davey catchment. Our analysis shows that mangers can use river flows as a simple index that provide a landscape-scale forest fire risk in the TWWHA.

scholarly journals Climate change and forest management affect forest fire risk in Fennoscandia

2021 ◽  
Author(s):  
Keyword(s):  
Climate Change ◽  
Forest Management ◽  
Black Carbon ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Risk ◽  
The Arctic ◽  
Large Fires ◽  
Forest Fire Risk ◽  
Fire Ignition

Forest and wildland fires are a natural part of ecosystems worldwide, but large fires in particular can cause societal, economic and ecological disruption. Fires are an important source of greenhouse gases and black carbon that can further amplify and accelerate climate change. In recent years, large forest fires in Sweden demonstrate that the issue should also be considered in other parts of Fennoscandia. This final report of the project “Forest fires in Fennoscandia under changing climate and forest cover (IBA ForestFires)” funded by the Ministry for Foreign Affairs of Finland, synthesises current knowledge of the occurrence, monitoring, modelling and suppression of forest fires in Fennoscandia. The report also focuses on elaborating the role of forest fires as a source of black carbon (BC) emissions over the Arctic and discussing the importance of international collaboration in tackling forest fires. The report explains the factors regulating fire ignition, spread and intensity in Fennoscandian conditions. It highlights that the climate in Fennoscandia is characterised by large inter-annual variability, which is reflected in forest fire risk. Here, the majority of forest fires are caused by human activities such as careless handling of fire and ignitions related to forest harvesting. In addition to weather and climate, fuel characteristics in forests influence fire ignition, intensity and spread. In the report, long-term fire statistics are presented for Finland, Sweden and the Republic of Karelia. The statistics indicate that the amount of annually burnt forest has decreased in Fennoscandia. However, with the exception of recent large fires in Sweden, during the past 25 years the annually burnt area and number of fires have been fairly stable, which is mainly due to effective fire mitigation. Land surface models were used to investigate how climate change and forest management can influence forest fires in the future. The simulations were conducted using different regional climate models and greenhouse gas emission scenarios. Simulations, extending to 2100, indicate that forest fire risk is likely to increase over the coming decades. The report also highlights that globally, forest fires are a significant source of BC in the Arctic, having adverse health effects and further amplifying climate warming. However, simulations made using an atmospheric dispersion model indicate that the impact of forest fires in Fennoscandia on the environment and air quality is relatively minor and highly seasonal. Efficient forest fire mitigation requires the development of forest fire detection tools including satellites and drones, high spatial resolution modelling of fire risk and fire spreading that account for detailed terrain and weather information. Moreover, increasing the general preparedness and operational efficiency of firefighting is highly important. Forest fires are a large challenge requiring multidisciplinary research and close cooperation between the various administrative operators, e.g. rescue services, weather services, forest organisations and forest owners is required at both the national and international level.

scholarly journals Developing a New Hourly Forest Fire Risk Index Based on Catboost in South Korea

2020 ◽  
Vol 10 (22) ◽  
pp. 8213
Author(s):  
Yoojin Kang ◽  
Eunna Jang ◽  
Jungho Im ◽  
Chungeun Kwon ◽  
Sungyong Kim
Keyword(s):  
South Korea ◽  
Forest Fire ◽  
Forest Fires ◽  
Risk Index ◽  
Fire Risk ◽  
Ensemble Model ◽  
Weather Factors ◽  
Meteorological Model ◽  
Forest Fire Risk ◽  
Fire Risk Index

Forest fires can cause enormous damage, such as deforestation and environmental pollution, even with a single occurrence. It takes a lot of effort and long time to restore areas damaged by wildfires. Therefore, it is crucial to know the forest fire risk of a region to appropriately prepare and respond to such disastrous events. The purpose of this study is to develop an hourly forest fire risk index (HFRI) with 1 km spatial resolution using accessibility, fuel, time, and weather factors based on Catboost machine learning over South Korea. HFRI was calculated through an ensemble model that combined an integrated model using all factors and a meteorological model using weather factors only. To confirm the generalized performance of the proposed model, all forest fires that occurred from 2014 to 2019 were validated using the receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) values through one-year-out cross-validation. The AUC value of HFRI ensemble model was 0.8434, higher than the meteorological model. HFRI was compared with the modified version of Fine Fuel Moisture Code (FFMC) used in the Canadian Forest Fire Danger Rating Systems and Daily Weather Index (DWI), South Korea’s current forest fire risk index. When compared to DWI and the revised FFMC, HFRI enabled a more spatially detailed and seasonally stable forest fire risk simulation. In addition, the feature contribution to the forest fire risk prediction was analyzed through the Shapley Additive exPlanations (SHAP) value of Catboost. The contributing variables were in the order of relative humidity, elevation, road density, and population density. It was confirmed that the accessibility factors played very important roles in forest fire risk modeling where most forest fires were caused by anthropogenic factors. The interaction between the variables was also examined.

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