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One of the initial steps in management of forests and reduction of fire damages is to identify areas susceptible to this phenomenon. In this study, zonation of fire hazard was performed in Babahur forest area of Dorud city of Iran using statistical modeling method and analytical hierarchy process. Then, performance of the two methods was evaluated....
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... In recent years, due to its many advantages, AHP has been widespread used in many areas in the business world, such as science, health services, planning, transportation, production facility location, manufacturing sector, engineering, management, energy, and education. Some application studies are as follows: tourism management [98], environmental mapping [99], organizational evaluation [100], food safety assessment [101], forest management [102,103], construction management [104], road selection [105], software selection [106], inventory management [107,108], economic assessment [109], sustainability assessment [110], site selection studies [111][112][113][114][115], and land consolidation [116,117]. Additionally, AHP has been widely used to evaluate various choice problems in mining. ...
The aim of this study is to present hazard classifications of location, type, and causes of accidents in Turkish coal-extracting industries using Analytic Hierarchy Process (AHP). In this study, fatal mine accidents were analyzed by pairwise comparison and weighting, and the most important hazards were identified by determining the effective main and sub-criteria among location, type, and causes of accidents. For this purpose, 1st, 2nd, and 3rd level hazards consisting of location, type, and causes of accident were determined by using the data of fatal mine accidents in underground and open-pit coal mining in Turkey between the years of 2010 and 2020. And then the data were categorized, and each category was prioritized using the AHP technique. According to the weights calculated by AHP, the most dangerous main criteria at 1st level hazards in underground coal mining is ventilation operations (0.363), while the most dangerous sub-criterion at 2nd level hazards is firedamp explosions and other gas accidents (0.409). On the other hand, in open-pit coal mining, the most dangerous main criteria at 1st level hazards are slopes and benches (0.575), while the most dangerous sub-criterion at 2nd level hazards are landslides (0.429).
... The methodological framework was developed considering already established and applied methodologies EMSN041, EMSN059, and FireHub [30,32], and an extensive literature review [8,33,34]. Building upon this foundation, the methodology employed in this study utilizes a next-day fire risk model, which has been trained on historical data spanning from 2010 to 2018 and has been applied for daily predictions during the years 2019 to 2021, enabling precise assessment of ignition hazards within 100 × 100 m subareas of the area of interest (AOI). Furthermore, an integrated custom Flamap model is employed to simulate potential wildfire behavior originating from locations identified by the fire risk model. ...
... The methodological framework was developed considering already e applied methodologies EMSN041, EMSN059, and FireHub [30,32], and an ature review [8,33,34]. Building upon this foundation, the methodology em study utilizes a next-day fire risk model, which has been trained on histor ning from 2010 to 2018 and has been applied for daily predictions during to 2021, enabling precise assessment of ignition hazards within 100 × 100 the area of interest (AOI). Furthermore, an integrated custom Flamap mod to simulate potential wildfire behavior originating from locations identifi risk model. ...
Forest fires can result in loss of life, damage to infrastructure, and adverse environmental impacts. This study showcases an integrated approach for conducting high-detail fire risk assessment and supporting strategic planning and management of fire events in peri-urban areas that are susceptible to forest fires. The presented methodology encompasses fire hazard modeling, vulnerability and exposure assessment, and in situ observations. Numerous fire hazard scenarios were tested, simulating the spatiotemporal spread of fire events under different wind characteristics. The vulnerability of the studied areas was assessed by combining population data (density and age) and building characteristics, while the exposure parameter employed land value (EUR/m 2) as an indicator for qualitatively estimating potential economic effects in the study area. Field campaigns facilitated the identification and recording of critical areas and points, including high-risk buildings and population gathering areas, which subsequently informed the mitigation and fire management planning suggestions. Moreover, field recordings acted as an iterative process for validating and updating the fire risk maps. This research work utilizes state-of-the-art techniques to achieve an analysis of fire risk at a building-block level. Overall, the study presents an applied and end-to-end methodology for effectively addressing forest fire risk in peri-urban areas.
... Considering the fact that forest fires are a major hazard with a notable impact on the environment, society, and the economy (Singh, Maharjan, and Thapa 2020), it is necessary to study forest fire patterns. In this regard, one of the primary steps in managing forest resources and reducing the fire risk is to identify the regions that are vulnerable to forest fires (Darvishi, Daryaei, and Kouchi 2020). Undoubtedly, complete prevention of forest fires is not feasibles because forest fires are caused by the interaction of multiple factors, including climate and topography as well as ecological and anthropogenic indicators (Jain et al. 2020). ...
... Their study also revealed that only 22.07% of the fires occurred in the forestry areas and that the rest were in non-natural areas. Darvishi, Daryaei, and Kouchi (2020) also used GIScience capabilities to evaluate the fire risk in the Babahur forest area in Lorestan province of Iran. They conducted their research using the hierarchical analysis method (AHP), satellite images, and field observation data. ...
Forest fires are a multidimensional phenomenon that affects many parts of the world, including the Zagros region of Iran. They are often caused by various factors that can have natural-, anthropogenic-, or combined origins. Considering the significant environmental and socio-economic impacts of forest fires, it is essential to take necessary measures to identify the areas that are prone to forest fires and develop plans and policies for crisis management and risk mitigation accordingly. In this study, we applied an integrated geoinformation (remote sensing and GIScience) approach to analyze and map forest fire risk in Gachsaran, Iran, which is highly prone to forest fires. For the forest fire risk mapping (FFRM), we employed a GIS-based multi-criteria decision analysis method in combination with fuzzy and analytical network process (ANP) methods to identify the forest areas with a high fire risk. To distinguish the vulnerable sites, we employed 13 independent variables encompassing geomorphological factors, land surface characteristics, climatological factors, and anthropological factors. To develop initial criteria maps, we determined the criteria weights using the ANP and used the fuzzy technique for standardization. Finally, the forest fire risk map was produced using the multi-layer perceptron artificial neural network. Our results were also validated against the historical forest fire data using the operating characteristics. Our results showed that 18.417% of the province is subject to a very high forest fire risk. These are areas that should be prioritized when designing precautionary and protective measures. Among the criteria examined in this study, the land surface temperature, soil moisture, and distance from historical forest fire sites received the highest scores in the ANP. The results of this study can be used to identify vulnerable areas, take appropriate planning measures to deal with forest fire risk, and make informed decisions regarding the allocation of facilities in high-risk areas.
... The approach (Figure 2) is a fusion of the research team's expertise (EMSN041, 2017 20 ; EMSN059, 2019 21 ; FireHub 22 ; Girtsou et al. 2021) and of extensive literature review (Darvishi et al. 2020;Gheshlaghi et al. 2019). To assess the fire risk, were taken into account: i. Selected fire hazard scenarios, ii. ...
O período entre 2018 e 2022 mostrou-nos que o problema dos incêndios à escala global não está a diminuir, antes pelo contrário. Parece que as consequências das alterações climáticas já estão a afectar a ocorrência de incêndios florestais em várias partes do Mundo, de uma forma que só esperaríamos que acontecesse vários anos mais tarde. Em muitos países do Sul da Europa, bem como em algumas regiões dos EUA, Canadá e Austrália, onde estamos habituados a enfrentar a presença de incêndios muito grandes e devastadores, continuamos a ter eventos que quebram recordes. Alguns países, como os da Europa Central e do Norte, que não estavam habituados a ter grandes incêndios, experimentaram-nos durante estes anos. Os anos anteriores foram muito exigentes para todo o Mundo, também noutros aspectos que nos afectaram a todos. Referimo-nos às restrições impostas pela pandemia que limitaram as nossas reuniões e viagens, afectando em muitos casos a saúde dos membros da Comunidade Científica Wildfire. Felizmente, conseguimos encontrar novas formas de comunicação, ultrapassar essas limitações e manter-nos em contacto uns com os outros. Durante semanas e meses, para muitos de nós, as reuniões pessoais e o trabalho de grupo foram substituídos por ligações em linha. Apesar da economia de dinheiro e tempo, e da facilidade de reunir uma grande variedade de pessoas que estas reuniões desde que nos apercebêssemos de que não substituem as reuniões presenciais, que trazem consigo outras dimensões inestimáveis, que fazem parte da comunicação pessoal e ajudam a construir uma comunidade científica.
The Analytical Hierarchy Process (AHP) is a reliable, rigorous, and robust method for eliciting and quantifying subjective judgments in multi-criteria decision-making (MCDM). Despite the many benefits, the complications of the pairwise comparison process and the limitations of consistency in AHP are challenges that have been the subject of extensive research. AHP revolutionized how we resolve complex decision problems and has evolved substantially over three decades. We recap this evolution by introducing five new hybrid methods that combine AHP with popular weighting methods in MCDM. The proposed methods are described and evaluated systematically by implementing a widely used example in the AHP literature. We show that (i) the hybrid methods proposed in this study require fewer expert judgments than AHP but deliver the same ranking, (ii) a higher degree of involvement in the hybrid voting AHP methods leads to higher acceptability of the results when experts are also the decision-makers, and (iii) experts are more motivated and attentive in methods requiring fewer pairwise comparisons and less interaction, resulting in a more efficient process and higher acceptability.
We used three state-of-the-art machine learning techniques (boosted regression tree, random forest, and support vector machine) to produce a multi-hazard (MHR) map illustrating areas susceptible to flooding, gully erosion, forest fires, and earthquakes in Kohgiluyeh and Boyer-Ahmad Province, Iran. The earthquake hazard map was derived from a probabilistic seismic hazard analysis. The mean decrease Gini (MDG) method was implemented to determine the relative importance of effective factors on the spatial occurrence of each of the four hazards. Area under the curve (AUC) plots, based on a validation dataset, were created for the maps generated using the three algorithms to compare the results. The random forest model had the highest predictive accuracy, with AUC values of 0.994, 0.982, and 0.885 for gully erosion, flooding, and forest fires, respectively. Approximately 41%, 40%, 28%, and 3% of the study area are at risk of forest fires, earthquakes, floods, and gully erosion, respectively.
