scholarly journals PRIORITIZATION OF FOREST FIRE HAZARD RISK SIMULATION USING HYBRID GREY RELATIVITY ANALYSIS (HGRA) AND FUZZY ANALYTICAL HIERARCHY PROCESS (FAHP) COUPLED WITH MULTICRITERIA DECISION ANALYSIS (MCDA) TECHNIQUES – A COMPARATIVE STUDY ANALYSIS

2021 ◽  
Vol 47 (3) ◽  
pp. 147-161
Author(s):  
Michael Stanley Peprah ◽  
Bernard Kumi-Boateng ◽  
Edwin Kojo Larbi
Keyword(s):  
Forest Fire ◽  
Risk Model ◽  
Fire Risk ◽  
Fuzzy Analytical Hierarchy Process ◽  
Weighted Overlay ◽  
Causative Factors ◽  
Major Criterion ◽  
Gis Environment ◽  
Hierarchy Process ◽  
Forest Fire Risk

Forests are important dynamic systems which are widely attracted by wild fires worldwide. Due to the complexity and non-linearity of the causative forest fire problems, employing sophisticated hybrid evolutionary algorithms is a logical task to achieve a reliable approximation of this environmental threats. This estimate will provide the outline of priority areas for preventing activities and allocation of fire fighters’ stations, seeking to minimize possible damages caused by fires. This study aims at prioritizing the forest fire risk of Wassa West district of Ghana. The study considered static causative factors such as Land use and land cover (which include forest, built-ups and settlement areas), slope, aspect, linear features (water bodies and roads) and dynamic causative factors such as wind speed, precipitation, and temperature were used. The methods employed include a Hybrid Grey Relativity Analysis (HGRA) and Fuzzy Analytical Hierarchy Process (FAHP) techniques. The fuzzy sets integrated with AHP in a decision-making algorithm using geographic information system (GIS) was used to model the fire risk in the study area. FAHP and HGRA methods were used for estimating the importance (weights) of the effective factors in forest fire modelling. Based on their modelling methods, the expert ideas were used to express the relative importance and priority of the major criteria and sub-criteria in forest fire risk in the study area. The expert ideas were analyzed based on FAHP and HGRA. The major criteria models and fire risk model were presented based on these FAHP and HGRA weights. On the other hand, the spatial data of the sub criteria were provided and assembled in GIS environment to obtain the sub-criteria maps. Each sub-criterion map was converted to raster format and it was reclassified based on risks of its classes to fire occurrence. The maps of each major criterion were obtained by weighted overlay of its sub criteria maps considering to major criterion model in GIS environment. Finally, the map of fire risk was obtained by weighted overlay of major criteria maps considering to fire risk model in GIS. The results showed that the FAHP model showed superiority than HGRA in prioritizing forest fire risk of the study area in terms of statistical analysis with a standard deviation of 0.09277 m as compared to 0.1122 m respectively. The obtained fire risk map can be used as a decision support system for predicting of the future trends in the study area. The optimized structures of the proposed models could serve as a good alternative to traditional forest predictive models, and this can be a promisingly testament used for future planning and decision making in the proposed areas.

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 Forest fire risk mapping using analytical hierarchy process (AHP) and earth observation datasets: a case study in the mountainous terrain of Northeast India

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Ridalin Lamat ◽  
Mukesh Kumar ◽  
Arnab Kundu ◽  
Deepak Lal
Keyword(s):  
High Risk ◽  
Forest Fire ◽  
Analytical Hierarchy Process ◽  
Fire Risk ◽  
Control Measures ◽  
Fire Control ◽  
Risk Zones ◽  
Hierarchy Process ◽  
Forest Fire Risk ◽  
Very High

AbstractThis study presents a geospatial approach in conjunction with a multi-criteria decision-making (MCDM) tool for mapping forest fire risk zones in the district of Ri-Bhoi, Meghalaya, India which is very rich in biodiversity. Analytical hierarchy process (AHP)-based pair-wise comparison matrix was constructed to compare the selected parameters against each other based on their impact/influence (equal, moderate, strong, very strong, and extremely strong) on a forest fire. The final output delineated fire risk zones in the study area in four categories that include very high-risk, high-risk, moderate-risk, and low-risk zones. The delineated fire risk zones were found to be in close agreement with actual fire points obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) fire data for the study area. Results indicated that Ri-Bhoi’s 804.31 sq. km. (32.86%) the area was under ‘very high’ fire susceptibility. This was followed by 583.10 sq. km. (23.82%), 670.47 sq. km. (27.39%), and 390.12 sq. km. (15.93%) the area under high, moderate, and low fire risk categories, respectively. These results can be used effectively to plan fire control measures in advance and the methodology suggested in this study can be adopted in other areas too for delineating potential fire risk zones.

scholarly journals Integrating Remote Sensing and GIS to Model Forest Fire Rik in Virunga Massif, Central - Eastern Africa

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
C.B Kayijamahe ◽  
G Rwanyiziri ◽  
M Mugabowindekwe ◽  
J Tuyishimire
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Risk Model ◽  
Fire Risk ◽  
Ground Truth ◽  
Eastern Africa ◽  
Topographic Factors ◽  
Illegal Activities ◽  
Virunga Massif ◽  
Forest Fire Risk

This study aimed at developing a forest fire risk model using a combination of GIS and Remote sensing techniques, which helped to identify the level of forest fire vulnerability in Virunga Massif, located at the edge of central and eastern Africa. The Analytic Hierarchical Process (AHP) approach was employed to rank and weigh the key variables and combine them into different fire risk input factors which were later integrated into the main forest fire risk model. The main input datasets, which were linked with a potential source of a forest fire, include the land cover (specifically vegetation type data generated through the Landsat 8 image classification); topographic variables such as slope, elevation and aspect retrieved from the existing Digital Elevation Model (DEM) of Rwanda; the concentration of illegal activities and proximity to beehives sites; as well as visibility from the road and human settlements. Input factor maps were generated, assigned weights and combined in a GIS environment to produce a Virunga massif fire risk model map, which was validated using the existing burnt areas map, and ground truth points recorded using GPS. The study found that the ignition factors are the most forest fire triggering factors in Virunga massif, followed by topographic factors which play a major role in the fire spreading across the ecosystem. The high forest fire risk areas were found in steep slope location around the peaks of the volcanoes, whereas areas with the lowest risk of forest fire were found inside the forest at gentle slopes. The model was validated at 75% accuracy using ground truth data. The study proposes measure to halt the ignition factors through prevention of illegal activities in the Virunga massif for the successful prevention of the forest fire risk in the ecosystem, with much effort invested during the dry season, along with the relocation of beehives to a farther distance from the ecosystem’s edge. Keywords: Forest Fire Risk Modelling, Biodiversity, Illegal Activities, Ignition Factors, Topographic Factors, Analytic Hierarchy Process

scholarly journals MODELING OF PARAMETERS FOR FOREST FIRE RISK ZONE MAPPING

2018 ◽  
Vol XLII-5 ◽  
pp. 299-304 ◽  
Author(s):  
K. Pandey ◽  
S. K. Ghosh
Keyword(s):  
Forest Fire ◽  
Risk Model ◽  
Fire Risk ◽  
Landsat 8 ◽  
Biophysical Parameters ◽  
Risk Zone ◽  
Subjective Weight ◽  
Other Information ◽  
Erdas Imagine ◽  
Forest Fire Risk

<p><strong>Abstract.</strong> Forest fire has been regarded as one of the major reasons for the loss of biodiversity and dreadful conditions of environment. Global warming is also increasing the incidence of forest fire at an alarming rate. That’s why, one need to understand the complex biophysical parameters, which are responsible for this disaster. As it is difficult to predict forest fire, fire risk zone map can be useful for combating the forest fire. So the main aim of this study is to generate a Fire risk model to map fire risk zone using Remote Sensing &amp; GIS technique. Pauri Garhwal District, located in Uttarakhand, India, has been selected for this study as it continually faces the problem of forest fire. Landsat-8 data of 18th April, 2016 have been used for land use land cover mapping. Slope and other information have been derived from topographic maps and field information. For thematic and topographic information analysis ArcGIS and ERDAS Imagine software have been used. Forest fire risk model was generated by using AHP method, where each category was assigned subjective weight according to their sensitivity to fire. Three categories of forest fire risk ranging from very high to low were derived. The generated forest fire risk model was found to be in strong agreement with actual fire-affected sites.</p>

scholarly journals GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing

2021 ◽  
Vol 13 (18) ◽  
pp. 3704
Author(s):  
Pengcheng Zhao ◽  
Fuquan Zhang ◽  
Haifeng Lin ◽  
Shuwen Xu
Keyword(s):  
High Risk ◽  
Forest Fire ◽  
Risk Model ◽  
Fire Risk ◽  
Low Risk ◽  
Assessment Model ◽  
National Forest ◽  
Risk Map ◽  
Forest Park ◽  
Forest Fire Risk

Fire risk prediction is significant for fire prevention and fire resource allocation. Fire risk maps are effective methods for quantifying regional fire risk. Laoshan National Forest Park has many precious natural resources and tourist attractions, but there is no fire risk assessment model. This paper aims to construct the forest fire risk map for Nanjing Laoshan National Forest Park. The forest fire risk model is constructed by factors (altitude, aspect, topographic wetness index, slope, distance to roads and populated areas, normalized difference vegetation index, and temperature) which have a great influence on the probability of inducing fire in Laoshan. Since the importance of factors in different study areas is inconsistent, it is necessary to calculate the significance of each factor of Laoshan. After the significance calculation is completed, the fire risk model of Laoshan can be obtained. Then, the fire risk map can be plotted based on the model. This fire risk map can clarify the fire risk level of each part of the study area, with 16.97% extremely low risk, 48.32% low risk, 17.35% moderate risk, 12.74% high risk and 4.62% extremely high risk, and it is compared with the data of MODIS fire anomaly point. The result shows that the accuracy of the risk map is 76.65%.

scholarly journals Modelagem espacial de risco de incêndio florestal para o município de Inhambupe, Bahia, Brasil

2016 ◽  
Vol 36 (85) ◽  
pp. 41 ◽  
Author(s):  
Larissa Alves Secundo White ◽  
Benjamin Leonardo Alves White ◽  
Genésio Tâmara Ribeiro
Keyword(s):  
Forest Fire ◽  
Fire Risk ◽  
Risk Map ◽  
Map Algebra ◽  
Analytic Hierarchy ◽  
Land Occupation ◽  
Forest Protection ◽  
Burned Areas ◽  
Hierarchy Process ◽  
Forest Fire Risk

A modelagem do risco espacial de incêndios florestais tem o objetivo de determinar as regiões mais susceptíveis ao fogo, baseando-se em variáveis que representam a facilidade de ignição e de propagação do fogo. Nesse contexto, utilizando-se das variáveis: sistema viário, densidade demográfica, uso e ocupação do solo, malha hidrográfica, inclinação e orientação das encostas, foram elaborados mapas de riscos preliminares, que, posteriormente à ponderações das mesmas pelo método AHP, foram integradas por meio da calculadora Raster em um mapa final de risco de incêndio florestal para o município de Inhambupe, Bahia, Brasil. Com base no modelo utilizado, 75,46% da área de estudo apresenta-se classificada como de maior risco, representado pelas classes “alto”, “muito alto” e “extremo”. Ao comparar o mapa final do risco de incêndio florestal para a área de estudo com o histórico de áreas queimadas, verificou-se que 94,83% dos registros de incêndios florestais estão alocados nas áreas de maior risco.Spatial modeling of forest fire risk for the Municipality of Inhambupe, Bahia State, BrazilSpatial modeling of forest fire risk has the aim to determine areas most susceptible to fire based on variables that represent facility of ignition and propagation. This work developed a forest fire risk map for the Municipality of Inhambupe, Bahia State, Brazil, by elaborating thematic maps of the following variables: road system, population density, land occupation and use, watershed network, slope and aspect. These were evaluated by the analytic hierarchy process and integrated with map algebra. Based on the developed model, 75.46% of the studied area was classified as “high”, “very high” and “extreme high” fire risk. When comparing the forest fire risk map with historical data of burned areas, 95% of the fires were in these areas.Index terms: Forest protection; Fire susceptibility; Risk map

scholarly journals Forest Fire Risk Assessment using Fuzzy Analytic Hierarchy Process

2018 ◽  
Vol 13 (3) ◽  
pp. 307-316 ◽  
Author(s):  
DIVYA MEHTA ◽  
PARMINDER KAUR BAWEJA ◽  
R K AGGARWAL
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
High Risk ◽  
Analytic Hierarchy Process ◽  
Forest Fire ◽  
Fire Risk ◽  
Fuzzy Analytic Hierarchy Process ◽  
Analytic Hierarchy ◽  
Hierarchy Process ◽  
Forest Fire Risk

Forest fires in the mid hills of Himachal Pradesh are mostly related to human activities. More than 90% of fires are originated from either deliberate or involuntary causes. The purpose of study is linked to identification of forest fire risk factors in 19 villages under Nauni and Oachhghat Panchayats. The methodology paradigm applied here is based on knowledge and fuzzy analytic hierarchy process (FAHP) techniques. Knowledge-based criteria involve socio-economic and biophysical themes for risk assessment. The risk factors are identified according to past occurrence of fire. Fuel type scores highest weight (0.3109) followed by aspect (0.2487), agricultural workers (0.1865), nutritional density (0.1244), population density (0.0622), elevation (0.0311), literacy rate (0.0207) and distance from road (0.0155) in descending order. In the study area applying FAHP, 24.96% of total area was classified under high-risk prone area, 21.69% area classified under high-risk, 34.63% area under moderate risk, while 18.61% area under low risk. The results were in accordance with actual fire occurrences in the past years.

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