Integrating geospatial information into fire risk assessment

2014 ◽  
Vol 23 (5) ◽  
pp. 606 ◽  
Author(s):  
E. Chuvieco ◽  
I. Aguado ◽  
S. Jurdao ◽  
M. L. Pettinari ◽  
M. Yebra ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Moisture Content ◽  
Information Technologies ◽  
National Level ◽  
Fire Risk ◽  
Assessment System ◽  
Fuel Moisture ◽  
Fire Occurrence ◽  
Fire Risk Assessment ◽  
Fuel Moisture Content

Fire risk assessment should take into account the most relevant components associated to fire occurrence. To estimate when and where the fire will produce undesired effects, we need to model both (a) fire ignition and propagation potential and (b) fire vulnerability. Following these ideas, a comprehensive fire risk assessment system is proposed in this paper, which makes extensive use of geographic information technologies to offer a spatially explicit evaluation of fire risk conditions. The paper first describes the conceptual model, then the methods to generate the different input variables, the approaches to merge those variables into synthetic risk indices and finally the validation of the outputs. The model has been applied at a national level for the whole Spanish Iberian territory at 1-km2 spatial resolution. Fire danger included human factors, lightning probability, fuel moisture content of both dead and live fuels and propagation potential. Fire vulnerability was assessed by analysing values-at-risk and landscape resilience. Each input variable included a particular accuracy assessment, whereas the synthetic indices were validated using the most recent fire statistics available. Significant relations (P < 0.001) with fire occurrence were found for the main synthetic danger indices, particularly for those associated to fuel moisture content conditions.

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 Effects of Live Fuel Moisture Content on Wildfire Occurrence in Fire-Prone Regions over Southwest China

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 887 ◽  
Author(s):  
Kaiwei Luo ◽  
Xingwen Quan ◽  
Binbin He ◽  
Marta Yebra
Keyword(s):  
Moisture Content ◽  
Southwest China ◽  
Burned Area ◽  
Fuel Moisture ◽  
Fire Occurrence ◽  
Subtropical Zone ◽  
Wildfire Occurrence ◽  
Live Fuel Moisture ◽  
Grassland Fire ◽  
Fuel Moisture Content

Previous studies have shown that Live Fuel Moisture Content (LFMC) is a crucial driver affecting wildfire occurrence worldwide, but the effect of LFMC in driving wildfire occurrence still remains unexplored over the southwest China ecosystem, an area historically vulnerable to wildfires. To this end, we took 10-years of LFMC dynamics retrieved from Moderate Resolution Imaging Spectrometer (MODIS) reflectance product using the physical Radiative Transfer Model (RTM) and the wildfire events extracted from the MODIS Burned Area (BA) product to explore the relations between LFMC and forest/grassland fire occurrence across the subtropical highland zone (Cwa) and humid subtropical zone (Cwb) over southwest China. The statistical results of pre-fire LFMC and cumulative burned area show that distinct pre-fire LFMC critical thresholds were identified for Cwa (151.3%, 123.1%, and 51.4% for forest, and 138.1%, 72.8%, and 13.1% for grassland) and Cwb (115.0% and 54.4% for forest, and 137.5%, 69.0%, and 10.6% for grassland) zones. Below these thresholds, the fire occurrence and the burned area increased significantly. Additionally, a significant decreasing trend on LFMC dynamics was found during the days prior to two large fire events, Qiubei forest fire and Lantern Mountain grassland fire that broke during the 2009/2010 and 2015/2016 fire seasons, respectively. The minimum LFMC values reached prior to the fires (49.8% and 17.3%) were close to the lowest critical LFMC thresholds we reported for forest (51.4%) and grassland (13.1%). Further LFMC trend analysis revealed that the regional median LFMC dynamics for the 2009/2010 and 2015/2016 fire seasons were also significantly lower than the 10-year LFMC of the region. Hence, this study demonstrated that the LFMC dynamics explained wildfire occurrence in these fire-prone regions over southwest China, allowing the possibility to develop a new operational wildfire danger forecasting model over this area by considering the satellite-derived LFMC product.

Conversion of fuel moisture content values to ignition potential for integrated fire danger assessment

2004 ◽  
Vol 34 (11) ◽  
pp. 2284-2293 ◽  
Author(s):  
Emilio Chuvieco ◽  
Inmaculada Aguado ◽  
Alexandros P Dimitrakopoulos
Keyword(s):  
Moisture Content ◽  
Fire Danger ◽  
Assessment System ◽  
Fuel Moisture ◽  
Fire Propagation ◽  
Web Mapping ◽  
Danger Assessment ◽  
Fuel Moisture Content ◽  
In Fire ◽  
Fire Ignition

Fuel moisture content (FMC) estimation is a critical part of any fire danger rating system, since fuel water status is determinant in fire ignition and fire propagation. However, FMC alone does not provide a comprehensive assessment of fire danger, since other factors related to fire ignition (lightning, human factors) or propagation (wind, slope) also need to be taken into account. The problem in integrating all these factors is finding a common scale of danger rating that will make it possible to derive synthetic indices. This paper reviews the importance of FMC in fire ignition and fire propagation, as well as the most common methods of estimating FMC values. A simple method to convert FMC values to danger ratings is proposed, based on computing ignition potential from thresholds of moisture of extinction adapted to each fuel. The method has been tested for the Madrid region (central Spain), where a fire danger assessment system has been built. All the variables related to fire danger were integrated into a dedicated geographic information system and information provided to fire managers through a web mapping server.

scholarly journals An Integrated Method for Fire Risk Assessment in Residential Buildings

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Hongfu Mi ◽  
Yaling Liu ◽  
Wenhe Wang ◽  
Guoqing Xiao
Keyword(s):  
Risk Assessment ◽  
Residential Buildings ◽  
Residential Building ◽  
Fire Risk ◽  
Assessment System ◽  
Risk Level ◽  
Mathematical Framework ◽  
Building Fires ◽  
Building Fire ◽  
Fire Risk Assessment

Building fires are characterized by high uncertainty, so their fire risk assessment is a very challenging task. Many indexes and parameters related to building fires are ambiguous and uncertain; as a result, a flexible and robust method is needed to process quantitative or qualitative data and update existing information when new data are available. This paper presents a novel model to deal with the uncertainty of the residential building fire risk and systematically optimize its performance effectiveness. The model includes fuzzy theory, evidence reasoning theory, and expected utility methods. Fuzzy analysis hierarchy process is applied to analyze the residential building fire risk index system and determine the weights of the risk indexes, while the evidence reasoning operator is used to synthesize them. Three buildings were selected as a case study to illustrate the proposed fire risk model. The results show that the fire risk level of three buildings corresponds to “moderate” or below which is consistent with the previous study. These results also truly reflect the actual situation of fire safety in these residential buildings. The application of this model provides a powerful mathematical framework for cooperative modeling of the fire risk assessment system and allows data to be analyzed step by step in a systematic manner. It is expected that the proposed model could provide managers and researchers with flexible and transparent tools to effectively reduce the fire risk in the system.

scholarly journals Data assimilation of dead fuel moisture observations from remote automated weather stations

2016 ◽  
Vol 25 (5) ◽  
pp. 558 ◽  
Author(s):  
Martin Vejmelka ◽  
Adam K. Kochanski ◽  
Jan Mandel
Keyword(s):  
Data Assimilation ◽  
Moisture Content ◽  
Wildland Fire ◽  
Time Lag ◽  
Assessment System ◽  
Major Influence ◽  
Surface Model ◽  
Fuel Moisture ◽  
Weather Stations ◽  
Fuel Moisture Content

Fuel moisture has a major influence on the behaviour of wildland fires and is an important underlying factor in fire risk assessment. We propose a method to assimilate dead fuel moisture content (FMC) observations from remote automated weather stations (RAWS) into a time lag fuel moisture model. RAWS are spatially sparse and a mechanism is needed to estimate fuel moisture content at locations potentially distant from observational stations. This is arranged using a trend surface model (TSM), which allows us to account for the effects of topography and atmospheric state on the spatial variability of FMC. At each location of interest, the TSM provides a pseudo-observation, which is assimilated via Kalman filtering. The method is tested with the time lag fuel moisture model in the coupled weather-fire code WRF–SFIRE on 10-h FMC observations from Colorado RAWS in 2013. Using leave-one-out testing we show that the TSM compares favourably with inverse squared distance interpolation as used in the Wildland Fire Assessment System. Finally, we demonstrate that the data assimilation method is able to improve on FMC estimates in unobserved fuel classes.

Monitoring herbaceous fuel moisture content with SPOT VEGETATION time-series for fire risk prediction in savanna ecosystems

2007 ◽  
Vol 108 (4) ◽  
pp. 357-368 ◽  
Author(s):  
J. Verbesselt ◽  
B. Somers ◽  
S. Lhermitte ◽  
I. Jonckheere ◽  
J. van Aardt ◽  
...  
Keyword(s):  
Time Series ◽  
Moisture Content ◽  
Risk Prediction ◽  
Fire Risk ◽  
Fuel Moisture ◽  
Fuel Moisture Content
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