GIS-based forest fire risk determination for Milas district, Turkey

Çetin, Mehmet; Pekkan, Özge Isik; Özenen-Kavlak, Mehtap; Atmaca, Ilker; Nasery, Suhrabuddin; Derakhshandeh, Masoud; Çabuk, Saye Nihan

doi:10.1007/s11069-022-05601-7

Cited by 31 publications

(2 citation statements)

References 60 publications

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“…Therefore, lower elevations were considered to have a higher risk, while higher elevations were considered to have a lower risk. As the elevation increases, the likelihood of precipitation increases due to increased humidity and decreased temperature, resulting in lower fire risk than that at lower elevations [14,17,22,29,39]. Therefore, lower elevations were considered to have a higher risk, while higher elevations were considered to have a lower risk.…”

Section: Research Site and Data Collectionmentioning

confidence: 99%

Assessing Fire Risk in Wildland–Urban Interface Regions Using a Machine Learning Method and GIS data: The Example of Istanbul’s European Side

Aksoy,

Kocer,

Yilmaz

et al. 2023

Fire

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Like many places around the world, the wildland–urban interface areas surrounding urban regions are subject to variable levels of fire risk, threatening the natural habitats they contact. This risk has been assessed by various authors using many different methods and numerical models. Among these approaches, machine learning models have been successfully applied to determine the weights of criteria in risk assessment and risk prediction studies. In Istanbul, data have been collected for areas that are yet to be urbanized but are foreseen to be at risk using geographic information systems (GIS) and remote sensing technologies based on fires that occurred between 2000 and 2021. Here, the land use/land cover (LULC) characteristics of the region were examined, and machine learning techniques, including random forest (RF), extreme gradient boosting (XGB), and light gradient boosting (LGB) models, were applied to classify the factors that affect fires. The RF model yielded the best results, with an accuracy of 0.70, an F1 score of 0.71, and an area under the curve (AUC) value of 0.76. In the RF model, the grouping between factors that initiate fires and factors that influence the spread of fires was distinct, and this distinction was also somewhat observable in the other two models. Risk scores were generated through the multiplication of the variable importance values of the factors and their respective layer values, culminating in a risk map for the region. The distribution of risk is in alignment with the number of fires that have previously occurred, and the risk in wildland–urban interface areas was found to be significantly higher than the risk in wildland areas alone.

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Section: Research Site and Data Collectionmentioning

confidence: 99%

Assessing Fire Risk in Wildland–Urban Interface Regions Using a Machine Learning Method and GIS data: The Example of Istanbul’s European Side

Aksoy,

Kocer,

Yilmaz

et al. 2023

Fire

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“…Currently, the data used in research on emergency management and sustainable urban development mainly include geographic information data (Çetin 2015a , b ; Cetin et al 2022 ), remote sensing data (Cetin et al 2023 ; Degerli and Çetin 2022a , b ), and temperature (Bozdogan Sert et al 2021 ; Adiguzel et al 2022 ; Degerli and Çetin 2022a , b ), etc. With the rapid development and popularization of social media, users constitute a vast social sensor network that can dynamically generate enormous amounts of data in real time.…”

Section: Introductionmentioning

confidence: 99%

Social media-based urban disaster recovery and resilience analysis of the Henan deluge

Shan

Zhao

2023

Nat Hazards

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Measuring disaster resilience from the perspective of long-term recovery ability is important for the planning and construction of urban sustainability, whereas short-term resilient recovery can better reflect a city’s ability to recover quickly after a disaster occurs. This study proposes an analytical framework for urban disaster recovery and resilience based on social media data that can analyze short-term disaster recovery and assess disaster resilience from the perspectives of infrastructure and people’s psychological states. We consider the downpour in Henan, China, in July 2021. The results show that (1) social media data can effectively reflect short-term disaster recovery, (2) disaster resilience can be assessed using social media data combined with rainfall and damage data, and (3) the framework can quantitatively reflect the differences in disaster recovery and resilience across regions. The findings can facilitate better decision-making in disaster emergency management for precise and effective post-disaster reconstruction and psychological intervention, and provide references for cities to improve disaster resilience.

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A comprehensive taxonomy for forest fire risk assessment: bridging methodological gaps and proposing future directions

Özcan,

Caglayan,

Kabak

2024

Environ Monit Assess

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GIS-based forest fire risk determination for Milas district, Turkey

Cited by 31 publications

References 60 publications

Assessing Fire Risk in Wildland–Urban Interface Regions Using a Machine Learning Method and GIS data: The Example of Istanbul’s European Side

Assessing Fire Risk in Wildland–Urban Interface Regions Using a Machine Learning Method and GIS data: The Example of Istanbul’s European Side

Social media-based urban disaster recovery and resilience analysis of the Henan deluge

A comprehensive taxonomy for forest fire risk assessment: bridging methodological gaps and proposing future directions

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