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 Forest Fire Hazards Vulnerability and Risk Assessment in Sirmaur District Forest of Himachal Pradesh (India): A Geospatial Approach

2021 ◽  
Vol 10 (7) ◽  
pp. 447
Author(s):  
Jagpal Singh Tomar ◽  
Nikola Kranjčić ◽  
Bojan Đurin ◽  
Shruti Kanga ◽  
Suraj Kumar Singh
Keyword(s):  
High Risk ◽  
Forest Fire ◽  
Spatial Scales ◽  
Fire Risk ◽  
Himachal Pradesh ◽  
Hybrid Strategy ◽  
Risk Zones ◽  
The Impact ◽  
Forest Fire Risk ◽  
Very High

The Himachal Pradesh district’s biggest natural disaster is the forest fire. Forest fire threat evaluation, model construction, and forest management using geographic information system techniques will be important in this proposed report. A simulation was conducted to evaluate the driving forces of fires and their movement, and a hybrid strategy for wildfire control and geostatistics was developed to evaluate the impact on forests. The various methods we included herein are those based on information, such as knowledge-based AHP-crisp for figuring out forest-fire risk, using such variables as forest type, topography, land-use and land cover, geology, geomorphology, settlement, drainage, and road. The models for forest-fire ignition, progression, and action are built on various spatial scales, which are three-dimensional layers. To create a forest fire risk model using three different methods, a study was made to find out how much could be lost in a certain amount of time using three samples. Precedent fire mapping validation was used to produce the risk maps, and ground truths were used to verify them. The accuracy was highest in the form of using “knowledge base” methods, and the predictive value was lowest in the use of an analytic hierarchy process or AHP (crisp). Half of the area, about 53.92%, was in the low-risk to no-risk zones. Very-high- to high-risk zones cover about 24.66% of the area of the Sirmaur district. The middle to northwest regions are in very-high- to high-risk zones for forest fires. These effects have been studied for forest fire suppression and management. Management, planning, and abatement steps for the future were offered as suitable solutions.

scholarly journals Mapping the Forest Fire Risk Zones Using Artificial Intelligence with Risk Factors Data

2022 ◽  
Author(s):  
Volkan Sevinc
Keyword(s):  
Artificial Intelligence ◽  
High Risk ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Risk ◽  
Geographical Information ◽  
Data Set ◽  
Risk Zone ◽  
Risk Zones ◽  
Forest Fire Risk

Abstract Geographical information system data has been used in forest fire risk zone mapping studies commonly. However, forest fires are caused by many factors, which cannot be explained only by geographical and meteorological reasons. Human-induced factors also play an important role in occurrence of forest fires and these factors depend on various social and economic conditions. This article aims to prepare a fire risk zone map by using a data set consisting of nine human-induced factors, three natural factors, and a temperature factor causing forest fires. Moreover, an artificial intelligence method, k-means, clustering algorithm was employed in preparation of the fire risk zone map. Turkey was selected as the study area as there are social and economic varieties among its zones. Therefore, the forestry zones in Turkey were separated into three groups as low, moderate, and high-risk categories and a map was provided for these risk zones. The map reveals that the forestry zones on the west coast of Turkey are under high risk of forest fire while the moderate risk zones mostly exist in the southeastern zones. The zones located in the interior parts, in the east, and on the north coast of Turkey have comparatively lower forest fire risks.

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.

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 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 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%.

A computer-system that classifies the prefectures of Greece in forest fire risk zones using fuzzy sets

2002 ◽  
Vol 4 (1) ◽  
pp. 43-54 ◽  
Author(s):  
Lazaros S. Iliadis ◽  
Anastasios K. Papastavrou ◽  
Panagiotis D. Lefakis
Keyword(s):  
Fuzzy Sets ◽  
Computer System ◽  
Forest Fire ◽  
Fire Risk ◽  
Risk Zones ◽  
Forest Fire Risk ◽  
Of Greece

scholarly journals Flash Flood Susceptibility Assessment and Zonation Using an Integrating Analytic Hierarchy Process and Frequency Ratio Model for the Chitral District, Khyber Pakhtunkhwa, Pakistan

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1650
Author(s):  
Hassan Waqas ◽  
Linlin Lu ◽  
Aqil Tariq ◽  
Qingting Li ◽  
Muhammad Fahad Baqa ◽  
...  
Keyword(s):  
High Risk ◽  
Frequency Ratio ◽  
Flash Flood ◽  
Drainage Density ◽  
Low Risk ◽  
Physical Parameters ◽  
Observation Data ◽  
Risk Zones ◽  
Hierarchy Process ◽  
Very High

Pakistan is a flood-prone country and almost every year, it is hit by floods of varying magnitudes. This study was conducted to generate a flash flood map using analytical hierarchy process (AHP) and frequency ratio (FR) models in the ArcGIS 10.6 environment. Eight flash-flood-causing physical parameters were considered for this study. Five parameters were based on the digital elevation model (DEM), Advanced Land Observation Satellite (ALOS), and Sentinel-2 satellite, including distance from the river and drainage density slope, elevation, and land cover, respectively. Two other parameters were geology and soil, consisting of different rock and soil formations, respectively, where both layers were classified based on their resistance against water percolation. One parameter was rainfall. Rainfall observation data obtained from five meteorological stations exist close to the Chitral District, Pakistan. According to its significant importance in the occurrence of a flash flood, each criterion was allotted an estimated weight with the help of AHP and FR. In the end, all the parameters were integrated using weighted overlay analysis in which the influence value of the drainage density was given the highest value. This gave the output in terms of five flood risk zones: very high risk, high risk, moderate risk, low risk, and very low risk. According to the results, 1168 km2, that is, 8% of the total area, showed a very high risk of flood occurrence. Reshun, Mastuj, Booni, Colony, and some other villages were identified as high-risk zones of the study area, which have been drastically damaged many times by flash floods. This study is pioneering in its field and provides policy guidelines for risk managers, emergency and disaster response services, urban and infrastructure planners, hydrologists, and climate scientists.

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