Geospatial Wildfire Risk Assessment from Social, Infrastructural and Environmental Perspectives: A Case Study in Queensland Australia
Although it is hard to predict wildfires, risky areas can be systematically assessed and managed. Some of the factors for decision-making are hazard, vulnerability, and risk maps, which are the end product of wildfire mapping. This study deals with wildfire risk analysis in Queensland, Australia. A...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Fire |
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| Online Access: | https://www.mdpi.com/2571-6255/6/1/22 |
| _version_ | 1797442769983111168 |
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| author | Mahyat Shafapourtehrany |
| author_facet | Mahyat Shafapourtehrany |
| author_sort | Mahyat Shafapourtehrany |
| collection | DOAJ |
| description | Although it is hard to predict wildfires, risky areas can be systematically assessed and managed. Some of the factors for decision-making are hazard, vulnerability, and risk maps, which are the end product of wildfire mapping. This study deals with wildfire risk analysis in Queensland, Australia. A review of the previous studies focusing on each aspect has been done and used with wildfire records from 2011 to 2019 in Queensland, Australia, to compile the required input models to detect risky wildfire regions. Machine learning (ML) methods of Decision Tree (DT) and Support Vector Machine (SVM) were used to perform hazard assessment. The reason was to select the most accurate outcomes for the rest of the analysis. Among accuracy assessment techniques, the Area Under Curvature (AUC) method was used to evaluate the hazard maps. Prediction rates of 89.21% and 83.78% were obtained for DT and SVM, respectively. The DT prediction value showed that the DT-hazard map was more accurate than the SVM-hazard map. Vulnerability analysis was implemented by assigning weights to each factor according to the literature. Lastly, in order to create the wildfire risk map, the hazard and vulnerability indices were combined. The risk map showed that particularly dense urbanization regions are under future wildfire risk. To perform preliminary land use planning, this output can be used by local governmental authorities. |
| first_indexed | 2024-03-09T12:46:48Z |
| format | Article |
| id | doaj.art-60c0c7a3f31b423ea97f41731ec4fb95 |
| institution | Directory Open Access Journal |
| issn | 2571-6255 |
| language | English |
| last_indexed | 2024-03-09T12:46:48Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fire |
| spelling | doaj.art-60c0c7a3f31b423ea97f41731ec4fb952023-11-30T22:11:39ZengMDPI AGFire2571-62552023-01-01612210.3390/fire6010022Geospatial Wildfire Risk Assessment from Social, Infrastructural and Environmental Perspectives: A Case Study in Queensland AustraliaMahyat Shafapourtehrany0Kandilli Observatory and Earthquake Research Institute, Department of Geodesy, Bogazici University, Cengelkoy, Istanbul 34680, TurkeyAlthough it is hard to predict wildfires, risky areas can be systematically assessed and managed. Some of the factors for decision-making are hazard, vulnerability, and risk maps, which are the end product of wildfire mapping. This study deals with wildfire risk analysis in Queensland, Australia. A review of the previous studies focusing on each aspect has been done and used with wildfire records from 2011 to 2019 in Queensland, Australia, to compile the required input models to detect risky wildfire regions. Machine learning (ML) methods of Decision Tree (DT) and Support Vector Machine (SVM) were used to perform hazard assessment. The reason was to select the most accurate outcomes for the rest of the analysis. Among accuracy assessment techniques, the Area Under Curvature (AUC) method was used to evaluate the hazard maps. Prediction rates of 89.21% and 83.78% were obtained for DT and SVM, respectively. The DT prediction value showed that the DT-hazard map was more accurate than the SVM-hazard map. Vulnerability analysis was implemented by assigning weights to each factor according to the literature. Lastly, in order to create the wildfire risk map, the hazard and vulnerability indices were combined. The risk map showed that particularly dense urbanization regions are under future wildfire risk. To perform preliminary land use planning, this output can be used by local governmental authorities.https://www.mdpi.com/2571-6255/6/1/22wildfirehazardrisk assessmentmachine learninggeospatialAustralia |
| spellingShingle | Mahyat Shafapourtehrany Geospatial Wildfire Risk Assessment from Social, Infrastructural and Environmental Perspectives: A Case Study in Queensland Australia Fire wildfire hazard risk assessment machine learning geospatial Australia |
| title | Geospatial Wildfire Risk Assessment from Social, Infrastructural and Environmental Perspectives: A Case Study in Queensland Australia |
| title_full | Geospatial Wildfire Risk Assessment from Social, Infrastructural and Environmental Perspectives: A Case Study in Queensland Australia |
| title_fullStr | Geospatial Wildfire Risk Assessment from Social, Infrastructural and Environmental Perspectives: A Case Study in Queensland Australia |
| title_full_unstemmed | Geospatial Wildfire Risk Assessment from Social, Infrastructural and Environmental Perspectives: A Case Study in Queensland Australia |
| title_short | Geospatial Wildfire Risk Assessment from Social, Infrastructural and Environmental Perspectives: A Case Study in Queensland Australia |
| title_sort | geospatial wildfire risk assessment from social infrastructural and environmental perspectives a case study in queensland australia |
| topic | wildfire hazard risk assessment machine learning geospatial Australia |
| url | https://www.mdpi.com/2571-6255/6/1/22 |
| work_keys_str_mv | AT mahyatshafapourtehrany geospatialwildfireriskassessmentfromsocialinfrastructuralandenvironmentalperspectivesacasestudyinqueenslandaustralia |