Reconstructing historical forest fire risk in the non-satellite era using the improved forest fire danger index and long short-term memory deep learning-a case study in Sichuan Province,southwestern China

Historical forest fire risk databases are vital for evaluating the effectiveness of past forest management approaches,enhancing forest fire warnings and emergency response capabilities,and accurately budgeting potential carbon emissions resulting from fires.However,due to the unavailability of spatial information technology,such databases are extremely difficult to build reliably and completely in the non-satellite era.This study presented an improved forest fire risk reconstruction framework that integrates a deep learning-based time series prediction model and spatial interpolation to address the challenge in Sichuan Province,southwestern China.First,the forest fire danger index(FFDI)was improved by supplementing slope and aspect information.We compared the performances of three time series models,namely,the autoregressive integrated moving average(ARIMA),Prophet and long short-term memory(LSTM)in predicting the modified forest fire danger index(MFFDI).The bestperforming model was used to retrace the MFFDI of individual stations from 1941 to 1970.Following this,the Anusplin spatial interpolation method was used to map the distributions of the MFFDI at five-year intervals,which were then subjected to weighted overlay with the distance-to-river layer to generate forest fire risk maps for reconstructing the forest fire danger database.The results revealed LSTM as the most accurate in fitting and predicting the historical MFFDI,with a fitting determination coefficient(R^2)of 0.709,mean square error(MSE)of0.047,and validation R^2 and MSE of 0.508 and 0.11,respectively.Independent validation of the predicted forest fire risk maps indicated that 5 out of 7 historical forest fire events were located in forest fire-prone areas,which is higher than the results determined from the original FFDI(2 out of 7).This proves the effectiveness of the improved MFFDI and indicates a high level of reliability of the historical forest fire risk reconstruction method proposed in this study.

Fire Risk Quantitative Assessment of Offshore Supported Platform

The living area of an offshore platform is the main living place for operators in offshore oil and gas fields.Fire risk assessment plays an important role in the safety of personnel in offshore platforms.In this paper,a fire risk assessment mathematical model for offshore platfoms is proposed based on a comprehensive safety assessment method.The concept of danger time is presented according to the evaluation criteria of safe evacuation.The fire risk of offshore platforms is assessed by combining probability statistics with numerical simulation.The fire risk is quantitatively assessed by using an N500 deep water semi-submersible support platform as an example.According to the FN curve,fire frequency,fire escalation probability,and casualty probability,the rationality of marine general layout is analyzed,and the general layout design could be optimized to reduce the fire risk.

Forest fire risk zone mapping from satellite images and GIS for Baihe Forestry Bureau, Jilin, China

A forest fire can be a real ecological disaster regardless of whether it is caused by natural forces or human activities, it is possible to map forest fire risk zones to minimize the frequency of fires, avert damage, etc. A method integrating remote sensing and GIS was developed and applied to forest fire risk zone mapping for Baihe forestry bureau in this paper. Satellite images were interpreted and classified to generate vegetation type layer and land use layers （roads, settlements and farmlands）. Topographic layers （slope, aspect and altitude） were derived from DEM. The thematic and topographic information was analyzed by using ARC/INFO GIS software. Forest fire risk zones were delineated by assigning subjective weights to the classes of all the layers （vegetation type, slope, aspect, altitude and distance from r3ads, farmlands and settlements） according to their sensitivity to fire or their fire-inducing capability. Five categories of forest fire risk ranging from very high to very low were derived automatically. The mapping result of the study area was found to be in strong agreement with actual fire-affected sites.

RS AND GIS-BASED FOREST FIRE RISK ZONE MAPPING IN DA HINGGAN MOUNTAINS

The Da Hinggan Mountains is one of the most important forest areas in China, but forest fire there is also of high frequency. So it is completely necessary to map forest fire risk zones in order to effectively manage and protect the forest resources. Two forest farms of Tuqiang Forest Bureau (53 degrees 34'-52 degrees 15'N,124 degrees 05'-122 degrees 18'E) were chosen as typical areas in this study. Remote sensing (RS) and Geographic Information System (GIS) play a vital role and can be used effectively to obtain and combine different forest-fire-causing factors for demarcating the forest fire risk zone map. Forest fire risk zones were described by assigning subjective weights to the classes of all the coverage layers according to their sensitivity to fire, using the ARC/INFO GIS software. Four classes of forest fire risk ranging from low to extremely high were generated automatically in ARC/INFO. The results showed that about 60.33% of the study area were predicted to be upper moderate risk zones, indicating that the forest fire management task in this area is super onerous. The RS and GIS-based forest fire risk model of the study area was found to be highly compatible with the actual fire-affected sites in 1987. Therefore the forest fire risk zone map can be used for guidance of forest fire management, and as basis for fire prevention strategies.

Comparative analysis of multi-criteria probabilistic FR and AHP models for forest fire risk(FFR)mapping in Melghat Tiger Reserve(MTR)forest

A comparative study of Frequency Ratio(FR)and Analytic Hierarchy Process(AHP)models are performed for forest fire risk(FFR)mapping in Melghat Tiger Reserve forest,central India.Identification of FFR depends on various hydrometeorological parameters altitude,slope,aspect,topographic position index,normalized differential vegetation index,rainfall,air temperature,land surface temperature,wind speed,distance to settlements,and distance by road are integrated using a GIS platform.The results from FR and AHP show similar trends.The FR model was significantly higher accurate(overall accuracy of 81.3%,kappa statistic 0.78)than the AHP model(overall accuracy 79.3%,kappa statistic 0.75).The FR model total forest fire risk areas were classified into five classes:very low(7.1%),low(22.2%),moderate(32.3%),high(26.9%),and very high(11.5%).The AHP fire risk classes were very low(6.7%),low(21.7%),moderate(34.0%),high(26.7%),and very high(10.9%).Sensitivity analyses were performed for AHP and FR models.The results of the two different models are compared and justified concerning the forest fire sample points(Forest Survey of India)and burn images(2010-2016).These results help in designing more effective fire management plans to improve the allocation of resources across a landscape framework.

An easy,accurate and efficient procedure to create forest fire risk maps using the SEXTANTE plugin Modeler

To prevent, detect, and protect against forest fires, forest personnel need to define rules for determining forest fire risk. In Portugal, all municipalities must annually produce forest fire risk （FFR） maps. To produce more reliable FFR maps more easily, we developed an open source model using the Modeler plugin of SEXTANTE in the program QGIS version 2.0 Dufour. The model provides all the maps involved in the FFR model （susceptibility map, hazard map, vulnerability map, economic value map, and potential loss map） and was produced according to Portuguese Forest Authority＇s （AFN, Autoridade Florestal Nacional） rules for determining the FFR. This model was tested for the Portuguese municipality Santa Maria da Feira, where 40 % of the total municipality area falls in the category ＂very high＂ or ＂high＂ fire risk. The ＂very high＂ fire risk area is mainly classified as broad-leaved forest and has the steepest slopes （〉15 %）. The distance of burned areas to roads was also analyzed; the proportion of burned areas increased with increasing distance to the main roads. In addition, 92.6 % of the ＂high＂ and ＂very high＂ risk zones were located in areas with lower elevation. These results confirmed that forest fire is strongly influenced not only by environmental factors but also by anthropogenic factors. The procedure implemented here was compared with our open source application already available in QGIS and also to the same procedure implemented in GIS pro- prietary software. Although the results were obviously the same, the model developed here presents several advan- tages over the other two approaches. Besides being faster, it is easy to change the model parameters according to user needs （i.e., to the rules of different countries）, and can be modified and adapted to other variables and other areas to create risk maps for different natural phenomena （e.g., floods, earthquakes, landslides）. The model is easy to use and to create risk and hazard maps rapidly in a free, open source environment that does not require any programming knowledge.

A Forest Fire Risk Assessment Using ASTER Images in Peninsular Malaysia

Based on the physical concept of heat energy of pre-ignition,a new fire susceptibility index (FSI) is used to estimate forest fire risk. This physical basis allows calculation of ignition probabilities and comparisons of fire risk across eco-regions. The computation of the index requires inputs of fuel temperature and fuel moisture content (FMC),both of which can be estimated using remote sensing data. While ASTER data for land surface temperatures (LST) was used as proxys for fuel temperatures,fuel moisture content is estimated by regression technique utilizing the ratio NDVI/LST of ASTER data. FSIs are computed in peninsular Malaysia for nine days before the fires of 2004 and 2005 and validated with fire occurrence data. Results show that the FSI increases as the day approaches the fire day. This trend can be observed clearly about four days before the day of fire. It suggests that FSI can be a good estimator of fire risk. The physical basis provides a more meaningful FSI,allows calculation of ignition probabilities and facilitates the development of a future class of fire risk models. FSI can be used to compare fire risk across different eco-regions and time periods. FSI retains the flexibility to be localized to a vegetation type or eco-regions for improved performance.

An Information Diffusion Technique for Fire Risk Analysis

There are many kinds of fires occurring under different conditions. For a specific site, it is difficult to collect sufficient data for analyzing the fire risk. In this paper, we suggest an information diffusion technique to analyze fire risk with a small sample. The information distribution method is applied to change crisp observations into fuzzy sets, and then to effectively construct a fuzzy relationship between fire and surroundings. With the data of Shanghai in winter, we show how to use the technique to analyze the fire risk.

Fire risk evaluation research on fully mechanized coalface based on the uncertainty measure theory

A relatively perfect coalmine fire risk-evaluating and order-arranging model that includes sixteen influential factors was established according to the statistical information of the fully mechanized coalface ground on the uncertainty measure theory. Then the single-index measure function of sixteen influential factors and the calculation method of computing the index weight ground on entropy theory were respectively established. The value assignment of sixteen influential factors was carried out by the qualitative analysis and observational data, respectively, in succession. The sequence of fire danger class of four experimental coalfaces could be obtained by the computational aids of Matlab according to the confidence level criterion. Some conclusions that the fire danger class of the No.l, No.2 and No.3 coalface belongs to high criticality can be obtained. But the fire danger class of the No.4 coalface belongs to higher criticality. The fire danger class of the No.4 coalface is more than that of the No.2 coalface. The fire danger class of the No.2 coalface is more than that of the No.1 coalface. Finally, the fire danger class of the No.1 coalface is more than that of the No.3 coalface.

Forest fire risk indices and zoning of hazardous areas in Sorocaba,Sao Paulo state,Brazil

This study compares the performance of three fire risk indices for accuracy in predicting fires in semideciduous forest fragments,creates a fire risk map by integrating historical fire occurrences in a probabilistic density surface using the Kernel density estimator(KDE)in the municipality of Sorocaba,Sao Paulo state,Brazil.The logarithmic Telicyn index,Monte Alegre formula(MAF)and enhanced Monte Alegre formula(MAF+)were employed using data for the period 1 January 2005 to 31 December 2016.Meteorological data and numbers of fire occurrences were obtained from the National Institute of Meteorology(INMET)and the Institute for Space Research(INPE),respectively.Two performance measures were calculated:Heidke skill score(SS)and success rate(SR).The MAF+index was the most accurate,with values of SS and SR of 0.611%and 62.8%,respectively.The fire risk map revealed two most susceptible areas with high(63 km^2)and very high(47 km^2)risk of fires in the municipality.Identification of the best risk index and the generation of fire risk maps can contribute to better planning and cost reduction in preventing and fighting forest fires.

Study of Burning Behaviors and Fire Risk of Flame Retardant Plywood by Cone Calorimeter and TG Test

A flame retardant composition was prepared by using phosphoguanidine,guanidine sulfamate,disodium octaborate tetrahydrate and dodecyl dimethyl benzyl ammonium chloride.Veneers were immersed in such flame retardant mixture to prepare plywood.The combustion characteristics and thermal stability of plywood were assessed using a cone calorimeter and TG.Results showed that:(1)High concentration and loading of flame retardant were beneficial for the fire resistance of the plywood.(2)The limiting oxygen index(LOI)and residual mass of plywood processed using the flame retardant was increased by 87.52%and 58.66%compared to those of the untreated plywood,while the average heat release rate(av-HRR),total heat release(THR),effective heat of combustion(EHC),total smoke release(TSR),CO yield(COY),CO_(2) yield(CO_(2)Y)and oxygen consumption were decreased by 44.3%,82.9%,47.0%,86.0%,89.9%,50.1%and 83.1%,respectively.(3)Treated plywood which had a low fire growth index(FGI)displayed a later combustion heat release rate peak and slower flame spread than observed for the untreated material.Combustion of treated plywood displayed a higher fire performance index(FPI),indicating a longer time to ignition.This suggests that burning structures from this material would be subject to a longer time for escape from the structure and would present lower fire risk than similar structures containing treated plywood.(4)TG results demonstrated that the presence of the flame retardant can decrease the pyrolysis temperature for hemicellulose and cellulose,change the decomposition and reaction progress for plywood degradation and promote dehydration carbonization and accelerated charformation.Moreover,the formed char was more stable than that combustion of untreated plywood.(5)The flame retardant contains nitrogen(N),phosphorus(P),boron(B),chlorine(Cl)and guanidine(Gu)compounds.The adhesive also contains N and P compounds.These substances display flame resistance and supplement each other to generate flame retardance than any one used alone.By changing the thermolysis and thermal decomposition processes,the heat release and smoke release from plywood,undergoing combustion was reduced.This controlled generation of combustible substances and promoted dehydration and carbonization to form char.As a result,the flame resistance of plywood was improved significantly.The probability of smoke asphyxia or poisoning death of those trapped in structures containing treated plywood during fire accidents can be decreased dramatically.

Elaborate Forecast about Fire Risk Grade in Forest and Grassland of Inner Mongolia Based on Intelligent Grid

Based on the fire and meteorological data of forest and grassland in Inner Mongolia in recent 30 years,a forest and grassland fire risk grade forecast model is established,and a refined forest and grassland fire risk level forecast system based on smart grid is developed. The results show that predictors are determined about forest and grassland fire risk grade,such as precipitation,minimum relative humidity,maximum temperature,maximum wind speed,number of sunny or rainy days,and forest and grassland combustible stock. According to fire risk division conclusion,forest and grassland areas are divided into 5 forecast areas. By using discriminant analysis and weighted factor overlay method,an elaborate fire risk grade forecast model is established in different forecast areas of Inner Mongolia forest and grassland. By using smart grid forecast field data,an elaborate fire risk grade forecasting system is established for making fire risk grade forecast during 24,48 and 72 h.

Fire Risk Assessment of CBD in Binhai New Area of Tianjin

Central business district(CBD)construction is in rapid development phase at present, therefore, the firefighting work in CBD becomes an important issue for safety. In this paper, a fire risk assessment index system is established from the perspective of regional characteristics, possible sources and factors which influence the occurrence of fire. Analytic hierarchy process(AHP)is used to obtain the weights of different indexes so as to reflect their effects on the final fire risk assessment. Then, the fire risk of CBD in Binhai New Area of Tianjin is assessed with the help of the proposed model and Arc GIS technique. Finally, the fire station layout is optimized based on the discrete location model, realizing the reasonable allocation of firefighting resources. According to the analysis, super high-rise buildings and underground spaces are main factors that cause high fire risk; furthermore, five firstlevel fire stations can satisfy the requirement of rescue response time.

Development of a Fuzzy Fire Risk Evaluation Model for Building Construction Sites in Hong Kong

Earlier research works on fire risk evaluation indicated that an objective,reliable,comprehensive,and practical fire risk evaluation model is essential for mitigating fire occurrence in building construction sites.Nevertheless,real empirical studies in this research area are quite limited.This journal paper gives an account of the second stage of a research study aiming at developing a fuzzy fire risk evaluation model for building construction sites in Hong Kong.The empirical research findings showed that the overall fire risk level of building construction sites is 3.6427,which can be interpreted as“moderate risk”.Also,the survey respondents perceived that“Restrictions for On-Site Personnel”is the most vital fire risk factor;with“Storage of Flammable Liquids or Dangerous Goods”being the second;and“Attitude of Main Contractor”the third.The proposed fuzzy fire risk evaluation model for building construction sites can be used to assess the overall fire risk level for a building construction site,and to identify improvement areas needed.Although the fuzzy fire risk evaluation model was developed domestically in Hong Kong,the research could be reproduced in other nations to develop similar models for international comparisons.Such an extension would provide a deeper understanding of the fire risk management on building construction sites.

Forest fire risk assessment in parts of Northeast India using geospatial tools

Forest fire is a major cause of changes in forest structure and function. Among various floristic regions, the northeast region of India suffers maximum from the fires due to age-old practice of shifting cultivation and spread of fires from jhum fields. For proper mitigation and management, an early warning of forest fires through risk modeling is required. The study results demonstrate the potential use of remote sensing and Geographic Information System （GIS） in identifying forest fire prone areas in Manipur, southeastern part of Northeast India. Land use land cover （LULC）, vegetation type, Digital elevation model （DEM）, slope, aspect and proximity to roads and settlements, factors that influence the behavior of fire, were used to model the forest fire risk zones. Each class of the layers was given weight according to their fire inducing capability and their sensitivity to fire. Weighted sum modeling and ISODATA clustering was used to classify the fire zones. TO validate the results, Along Track Scanning Radiometer （ATSR）, the historical fire hotspots data was used to check the occurrence points and modeled forest fire locations. The forest risk zone map has 55-63% of agreement with ATSR dataset.

Determination of fire risk to assist fire management for insular areas: the case of a small Greek island

Forest fire risk estimation constitutes an essential process to prevent high-intensity fires which are associated with severe implications to the natural and cultural environment. The primary aim of this research was to determine fire risk levels based on the local features of an island,namely, the impact of fuel structures, slope, aspects, as well as the impact of the road network and inhabited regions. The contribution of all the involved factors to forest fires ignition and behavior highlight certain regions which are highly vulnerable. In addition, the influence of both natural and anthropogenic factors to forest fire phenomena is explored. In this study, natural factors play a dominant role compared to anthropogenic factors. Hence essential preventative measures must focus on specific areas and established immediately. Indicative measures may include: the optimal allocation of watchtowers as well as the spatial optimization of mobile firefighting vehicles;and, forest fuel treatments in areas characterized by extremely high fire risk. The added value of this fire prediction tool is that it is highly flexible and could be adopted elsewhere with the necessary adjustments to local characteristics.

Study on Forecasting Method of Forest Fire Risk Grade in Putian City, China

From January 1, 2014, the basic stations of meteorological observation countries have changed from small evaporation observations to large-scale evaporation observations. National general weather stations have canceled observations on evaporation, but small evaporation is very important for forest fire risk prediction. In order to make the prediction of forest fire risk level objectively, weather data in Putian City, China and the multi-linear regression analysis method is used to calculate the daily evaporation amount in the more advanced SPSS16.0 software (English version), and the data of the last 5 years of each site are selected and fitted. Results showed that we accurately calculated the evaporation of the next day to make up for the lack of data due to the adjustment of the evaporation observation project. According to the forest fire risk weather index corresponding to many meteorological factors such as evaporation, temperature, humidity, sunshine and wind speed, the forest fire risk meteorological grade standard was designed to make a more accurate forest fire risk grade forecast.

Fire Risks and Prevention Strategies for Architecture at Historic Sites

This paper mainly discusses the features of China’s architecture at historic sites with regard to fire protection, the causes of fire since 1949, reviewing their weaknesses in fire protection, and exploring modern technologies for fire prevention that are applicable to ancient buildings. We put forward suggestions to improve fire prevention and management: eliminating potential problems of fire, improving fire protection and establishing a better fire security system, which is especially important to protect ancient buildings.

Probability Models of Fire Risk Based on Forest Fire Indices in Contrasting Climates over China

Fire weather indices have been widely applied to predict fire risk in many regions of the world. The objectives of this study were to establish fire risk probability models based on fire indices over different climatic regions in China. We linked the indices adopted in Canadian, US, and Australia with location, time, altitude, vegetation and fire characteristics during 1998-2007 in four regions using semi- parametric logistic （SPL） regression models. Different combinations of fire risk indices were selected as explanatory variables for specific regional probability model. SPL regression models of probability of fire ignition and large fire events were established to describe the non-linear relationship between fire risk indices and fire risk probabilities in the four regions. Graphs of observed versus estimated probabilities, fire risk maps, graphs of numbers of large fire events were produced from the probability models to assess the skill of these models. Fire ignition in all regions showed a significant link with altitude and NDVI. Indices of fuel moisture are important factors influencing fire occurrence in northern China. The fuel indices of organic material are significant indicators of fire risk in southern China. Besides the well skill of predicting fire risk, the probability models are a useful method to assess the utility of the fire risk indices in estimating fire events. The analysis presents some of the dynamics of climate-fire interactions and their value for management systems.

Urban Fire Risk Clustering Method Based on Fire Statistics

Fire statistics and fire analysis have become important ways for us to understand the law of fire, prevent the occurrence of fire, and improve the ability to control fire. According to existing fire statistics, the weighted fire risk calculating method characterized by the number of fire occurrence, direct economic losses, and fire casualties was put forward. On the basis of this method, meanwhile having improved K-mean clustering arithmetic, this paper established fire risk K-mean clustering model, which could better resolve the automatic classifying problems towards fire risk. Fire risk cluster should be classified by the absolute distance of the target instead of the relative distance in the traditional cluster arithmetic. Finally, for applying the established model, this paper carried out fire risk clustering on fire statistics from January 2000 to December 2004 of Shenyang in China. This research would provide technical support for urban fire management.