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 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.

scholarly journals Fire Risk Assessment Models Using Statistical Machine Learning and Optimized Risk Indexing

2020 ◽  
Vol 10 (12) ◽  
pp. 4199
Author(s):  
Myoung-Young Choi ◽  
Sunghae Jun
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Logistic Regression ◽  
Prediction Model ◽  
Fire Risk ◽  
Fire Occurrence ◽  
Statistical Machine Learning ◽  
Fire Insurance ◽  
Comparison Results ◽  
Fire Risk Assessment

It is very difficult for us to accurately predict occurrence of a fire. But, this is very important to protect human life and property. So, we study fire hazard prediction and evaluation methods to cope with fire risks. In this paper, we propose three models based on statistical machine learning and optimized risk indexing for fire risk assessment. We build logistic regression, deep neural networks (DNN) and fire risk indexing models, and verify performances between proposed and traditional models using real investigated data related to fire occurrence in Korea. In general, fire prediction models currently in use do not provide satisfactory levels of accuracy. The reason for this result is that the factors affecting fire occurrence are very diverse and frequency of fire occurrence is very sparse. To improve accuracy of fire occurrence, we first build logistic regression and DNN models. In addition, we construct a fire risk indexing model for a more improved model of fire prediction. To illustrate comparison results between our research models and current fire prediction model, we use real fire data investigated in Korea between 2011 to 2017. From the experimental results of this paper, we can confirm that accuracy of prediction by the proposed method is superior to the existing fire occurrence prediction model. Therefore, we expect the proposed model to contribute to evaluating the possibility of fire risk in buildings and factories in the field of fire insurance and to calculate the fire insurance premium.

scholarly journals Geospatial information on geographical and human factors improved anthropogenic fire occurrence modeling in the Chinese boreal forest

2016 ◽  
Vol 46 (4) ◽  
pp. 582-594 ◽  
Author(s):  
Futao Guo ◽  
Selvaraj Selvalakshmi ◽  
Fangfang Lin ◽  
Guangyu Wang ◽  
Wenhui Wang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Human Factors ◽  
Boreal Forest ◽  
Forest Fire ◽  
Management Practices ◽  
Explanatory Power ◽  
Driving Factors ◽  
Fire Occurrence ◽  
Geospatial Information ◽  
Anthropogenic Fire

We applied a classic logistic regression (LR) model together with a geographically weighted logistic regression (GWLR) model to determine the relationship between anthropogenic fire occurrence and potential driving factors in the Chinese boreal forest and to test whether the explanatory power of the LR model could be increased by considering geospatial information of geographical and human factors using a GWLR model. Three tests, “all variables”, “significant variables”, and “cross-validation”, were applied to compare model performance between the LR and GWLR models. Our results confirmed the importance of distance to railway, elevation, length of fire line, and vegetation cover on fire occurrence in the Chinese boreal forest. In addition, the GWLR model performs better than the LR model in terms of model prediction accuracy, model residual reduction, and spatial parameter estimation by considering geospatial information of explanatory variables. This indicates that the global LR model is incapable of identifying underlying causal factors for wildfire modeling sufficiently. The GWLR model helped identify spatial variation between driving factors and fire occurrence, which can contribute better understanding of forest fire occurrence over large geographic areas and the forest fire management practices may be improved based on it.

scholarly journals Spatial Assessment of Wildfires Susceptibility in Santa Cruz (Bolivia) Using Random Forest

Geosciences ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 224
Author(s):  
Marcela Bustillo Sánchez ◽  
Marj Tonini ◽  
Anna Mapelli ◽  
Paolo Fiorucci
Keyword(s):  
Random Forest ◽  
Land Cover ◽  
Spatial Data ◽  
Regional Scale ◽  
Fire Risk ◽  
Predictive Performance ◽  
Burned Area ◽  
Santa Cruz ◽  
Mapping Tool ◽  
Testing Dataset

Wildfires are expected to increase in the near future, mainly because of climate changes and land use management. One of the most vulnerable areas in the world is the forest in central-South America, including Bolivia. Despite that this country is highly prone to wildfires, literature is rather limited here. To fill this gap, we implemented a dataset including the burned area that occurred in the department of Santa Cruz in the period of 2010–2019, and the digital spatial data describing the predisposing factors (i.e., topography, land cover, ecoregions). The main goal was to develop a model, based on Random Forest, in which probabilistic outputs allowed to elaborate wildfires susceptibility maps. The overall accuracy was finally estimated by using 5-fold cross-validation. In addition, the last three years of observations acted as the testing dataset, allowing to evaluate the predictive performance of the model. The quantitative assessment of the variables revealed that “flooded savanna” and “shrub or herbaceous cover, flooded, fresh/saline/brakish water” are respectively the ecoregions and land cover classes with the highest probability of predicting wildfires. This study contributes to the development and validation of an innovative mapping tool for fire risk assessment, implementable at a regional scale in different areas of the globe.

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 Large scale eucalypt plantations associated to increased fire risk

2017 ◽  
Author(s):  
Adolfo Cordero Rivera
Keyword(s):  
Iberian Peninsula ◽  
Large Scale ◽  
Regional Scale ◽  
Fire Risk ◽  
Fire Frequency ◽  
Forest Plantations ◽  
Human Society ◽  
Fire Occurrence ◽  
Mediterranean Countries ◽  
Eucalypt Plantations

To satisfy the high timber demands of human society, forest plantations, especially with fast growing species like pines and eucalypts, are increasing worldwide. In some European countries, the number of wildfires has been augmenting since the second half of the XX century, in parallel with these tree plantations. The record for wildfires in Europe is paradoxically found in the NW of the Iberian Peninsula, a region where broadleaved Quercus forests are the potential climax vegetation, with a humid climate, unfavourable for fire occurrence. The ecological and forestry literature have analysed fire occurrence with complex models of fuel accumulation and vegetation structure, combined with no less complex climatic models to explain why this region has such a high fire occurrence. Economists have concentrated on the relationship between income and fire. Historians, sociologists and political scientists have long ago demonstrated that several conflicts over land use and property are behind most wildfires in this region, but there is little interaction between these fields. Here I use official statistics about fire frequency and wood production to test whether fire frequency is associated to the use of pyrophytic species. I found that fire frequency in NW Spain can be predicted by the amount of eucalypt biomass accumulated in forest plantations. I further explore the relationships between intensive sylviculture and fire risk at a regional scale (the North of the Iberian Peninsula) and a large scale, the Mediterranean countries. NW Iberia peasants have traditionally used fire to manage their common lands, and used the same techniques to oppose forest policies implemented by Franco’s dictatorship, which continued until now with little changes. The use of highly pyrophytic species like eucalypts and some pines has exacerbated this problem, as suggested by the positive correlation between eucalypt plantations and fire frequency at the local, regional and Mediterranean scales.

scholarly journals Large scale eucalypt plantations associated to increased fire risk

2017 ◽  
Author(s):  
Adolfo Cordero Rivera
Keyword(s):  
Iberian Peninsula ◽  
Large Scale ◽  
Regional Scale ◽  
Fire Risk ◽  
Fire Frequency ◽  
Forest Plantations ◽  
Human Society ◽  
Fire Occurrence ◽  
Mediterranean Countries ◽  
Eucalypt Plantations

To satisfy the high timber demands of human society, forest plantations, especially with fast growing species like pines and eucalypts, are increasing worldwide. In some European countries, the number of wildfires has been augmenting since the second half of the XX century, in parallel with these tree plantations. The record for wildfires in Europe is paradoxically found in the NW of the Iberian Peninsula, a region where broadleaved Quercus forests are the potential climax vegetation, with a humid climate, unfavourable for fire occurrence. The ecological and forestry literature have analysed fire occurrence with complex models of fuel accumulation and vegetation structure, combined with no less complex climatic models to explain why this region has such a high fire occurrence. Economists have concentrated on the relationship between income and fire. Historians, sociologists and political scientists have long ago demonstrated that several conflicts over land use and property are behind most wildfires in this region, but there is little interaction between these fields. Here I use official statistics about fire frequency and wood production to test whether fire frequency is associated to the use of pyrophytic species. I found that fire frequency in NW Spain can be predicted by the amount of eucalypt biomass accumulated in forest plantations. I further explore the relationships between intensive sylviculture and fire risk at a regional scale (the North of the Iberian Peninsula) and a large scale, the Mediterranean countries. NW Iberia peasants have traditionally used fire to manage their common lands, and used the same techniques to oppose forest policies implemented by Franco’s dictatorship, which continued until now with little changes. The use of highly pyrophytic species like eucalypts and some pines has exacerbated this problem, as suggested by the positive correlation between eucalypt plantations and fire frequency at the local, regional and Mediterranean scales.

scholarly journals Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine

2020 ◽  
Vol 13 (1) ◽  
pp. 10
Author(s):  
Andrea Sulova ◽  
Jamal Jokar Arsanjani
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Random Forest ◽  
Google Earth ◽  
Summer Season ◽  
Driving Factors ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Training Dataset ◽  
Google Earth Engine

Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others.

scholarly journals Are Climate Factors Driving the Contemporary Wildfire Occurrence in China?

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 392
Author(s):  
Zige Lan ◽  
Zhangwen Su ◽  
Meng Guo ◽  
Ernesto C. Alvarado ◽  
Futao Guo ◽  
...  
Keyword(s):  
Human Factors ◽  
Large Scale ◽  
Fire Occurrence ◽  
Climate Factors ◽  
Wildfire Occurrence ◽  
Prediction And Prevention ◽  
Data Resolution ◽  
Main Driver ◽  
Landscape Factors ◽  
Modelling Approach

Understanding the drivers of wildfire occurrence is of great value for fire prevention and management, but due to the variation in research methods, data sources, and data resolution of those studies, it is challenging to conduct a large-scale comprehensive comparative qualitative analysis on the topic. China has diverse vegetation types and topography, and has undergone rapid economic and social development, but experiences a high frequency of wildfires, making it one of the ideal locations for wildfire research. We applied the Random Forests modelling approach to explore the main types of wildfire drivers (climate factors, landscape factors and human factors) in three high wildfire density regions (Northeast (NE), Southwest (SW), and Southeast (SE)) of China. The results indicate that climate factors were the main driver of wildfire occurrence in the three regions. Precipitation and temperature significantly impacted the fire occurrence in the three regions due to the direct influence on the moisture content of forest fuel. However, wind speed had important influence on fire occurrence in the SE and SW. The explanation power of the landscape and human factors varied significantly between regions. Human factors explained 40% of the fire occurrence in the SE but only explained less than 10% of the fire occurrence in the NE and SW. The density of roads was identified as the most important human factor driving fires in all three regions, but railway density had more explanation power on fire occurrence in the SE than in the other regions. The landscape factors showed nearly no influence on fire occurrence in the NE but explained 46.4% and 20.6% in the SE and SW regions, respectively. Amongst landscape factors, elevation had the highest average explanation power on fire occurrence in the three regions, particularly in the SW. In conclusion, this study provides useful insights into targeted fire prediction and prevention, which should be more precise and effective under climate change and socio-economic development.

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