Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models
Wildfires are one of the gravest and most momentous hazards affecting rich forest biomes worldwide; India is one of the hotspots due to its diverse forest types and human-induced reasons. This research aims to identify wildfire risk zones in two contrasting climate zones, the Wayanad Wildlife Sanctu...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
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| Series: | Fire |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-6255/6/2/44 |
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| author | Aishwarya Sinha Suresh Nikhil Rajendran Shobha Ajin Jean Homian Danumah Sunil Saha Romulus Costache Ambujendran Rajaneesh Kochappi Sathyan Sajinkumar Kolangad Amrutha Alfred Johny Fahad Marzook Pratheesh Chacko Mammen Kamal Abdelrahman Mohammed S. Fnais Mohamed Abioui |
| author_facet | Aishwarya Sinha Suresh Nikhil Rajendran Shobha Ajin Jean Homian Danumah Sunil Saha Romulus Costache Ambujendran Rajaneesh Kochappi Sathyan Sajinkumar Kolangad Amrutha Alfred Johny Fahad Marzook Pratheesh Chacko Mammen Kamal Abdelrahman Mohammed S. Fnais Mohamed Abioui |
| author_sort | Aishwarya Sinha |
| collection | DOAJ |
| description | Wildfires are one of the gravest and most momentous hazards affecting rich forest biomes worldwide; India is one of the hotspots due to its diverse forest types and human-induced reasons. This research aims to identify wildfire risk zones in two contrasting climate zones, the Wayanad Wildlife Sanctuary in the Western Ghats and the Kedarnath Wildlife Sanctuary in the Himalayas, using geospatial tools, analytical hierarchy process (AHP), and fuzzy-AHP models to assess the impacts of various conditioning factors and compare the efficacy of the two models. Both of the wildlife sanctuaries were severely battered by fires in the past, with more than 100 fire incidences considered for this modeling. This analysis found that both natural and anthropogenic factors are responsible for the fire occurrences in both of the two sanctuaries. The validation of the risk maps, utilizing the receiver operating characteristic (ROC) method, proved that both models have outstanding prediction accuracy for the training and validation datasets, with the F-AHP model having a slight edge over the other model. The results of other statistical validation matrices such as sensitivity, accuracy, and Kappa index also confirmed that F-AHP is better than the AHP model. According to the F-AHP model, about 22.49% of Kedarnath and 17.12% of Wayanad fall within the very-high risk zones. The created models will serve as a tool for implementing effective policies intended to reduce the impact of fires, even in other protected areas with similar forest types, terrain, and climatic conditions. |
| first_indexed | 2024-03-11T08:50:37Z |
| format | Article |
| id | doaj.art-3542ca94970f40dfb0510b2ad042729b |
| institution | Directory Open Access Journal |
| issn | 2571-6255 |
| language | English |
| last_indexed | 2024-03-11T08:50:37Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fire |
| spelling | doaj.art-3542ca94970f40dfb0510b2ad042729b2023-11-16T20:27:06ZengMDPI AGFire2571-62552023-01-01624410.3390/fire6020044Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP ModelsAishwarya Sinha0Suresh Nikhil1Rajendran Shobha Ajin2Jean Homian Danumah3Sunil Saha4Romulus Costache5Ambujendran Rajaneesh6Kochappi Sathyan Sajinkumar7Kolangad Amrutha8Alfred Johny9Fahad Marzook10Pratheesh Chacko Mammen11Kamal Abdelrahman12Mohammed S. Fnais13Mohamed Abioui14Symbiosis Institute of Geoinformatics, Pune 411016, IndiaKerala State Emergency Operations Centre, Kerala State Disaster Management Authority, Thiruvananthapuram 695033, IndiaKerala State Emergency Operations Centre, Kerala State Disaster Management Authority, Thiruvananthapuram 695033, IndiaCentre Universitaire de Recherche et d’Application en Télédétection (CURAT), Université Félix Houphouët Boigny, Abidjan 00225, Côte d’IvoireDepartment of Geography, University of Gour Banga, Malda 732101, IndiaNational Institute of Hydrology and Water Management, 013686 Bucharest, RomaniaDepartment of Geology, University of Kerala, Thiruvananthapuram 695581, IndiaDepartment of Geology, University of Kerala, Thiruvananthapuram 695581, IndiaKerala State Emergency Operations Centre, Kerala State Disaster Management Authority, Thiruvananthapuram 695033, IndiaKerala State Emergency Operations Centre, Kerala State Disaster Management Authority, Thiruvananthapuram 695033, IndiaKerala State Emergency Operations Centre, Kerala State Disaster Management Authority, Thiruvananthapuram 695033, IndiaKerala State Emergency Operations Centre, Kerala State Disaster Management Authority, Thiruvananthapuram 695033, IndiaDepartment of Geology & Geophysics, College of Science, King Saud University, Riyadh 11451, Saudi ArabiaDepartment of Geology & Geophysics, College of Science, King Saud University, Riyadh 11451, Saudi ArabiaDepartment of Earth Sciences, Faculty of Sciences, Ibn Zohr University, Agadir 80000, MoroccoWildfires are one of the gravest and most momentous hazards affecting rich forest biomes worldwide; India is one of the hotspots due to its diverse forest types and human-induced reasons. This research aims to identify wildfire risk zones in two contrasting climate zones, the Wayanad Wildlife Sanctuary in the Western Ghats and the Kedarnath Wildlife Sanctuary in the Himalayas, using geospatial tools, analytical hierarchy process (AHP), and fuzzy-AHP models to assess the impacts of various conditioning factors and compare the efficacy of the two models. Both of the wildlife sanctuaries were severely battered by fires in the past, with more than 100 fire incidences considered for this modeling. This analysis found that both natural and anthropogenic factors are responsible for the fire occurrences in both of the two sanctuaries. The validation of the risk maps, utilizing the receiver operating characteristic (ROC) method, proved that both models have outstanding prediction accuracy for the training and validation datasets, with the F-AHP model having a slight edge over the other model. The results of other statistical validation matrices such as sensitivity, accuracy, and Kappa index also confirmed that F-AHP is better than the AHP model. According to the F-AHP model, about 22.49% of Kedarnath and 17.12% of Wayanad fall within the very-high risk zones. The created models will serve as a tool for implementing effective policies intended to reduce the impact of fires, even in other protected areas with similar forest types, terrain, and climatic conditions.https://www.mdpi.com/2571-6255/6/2/44anthropogenic factorsAHPF-AHPROCwildfireswildlife sanctuaries |
| spellingShingle | Aishwarya Sinha Suresh Nikhil Rajendran Shobha Ajin Jean Homian Danumah Sunil Saha Romulus Costache Ambujendran Rajaneesh Kochappi Sathyan Sajinkumar Kolangad Amrutha Alfred Johny Fahad Marzook Pratheesh Chacko Mammen Kamal Abdelrahman Mohammed S. Fnais Mohamed Abioui Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models Fire anthropogenic factors AHP F-AHP ROC wildfires wildlife sanctuaries |
| title | Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models |
| title_full | Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models |
| title_fullStr | Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models |
| title_full_unstemmed | Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models |
| title_short | Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models |
| title_sort | wildfire risk zone mapping in contrasting climatic conditions an approach employing ahp and f ahp models |
| topic | anthropogenic factors AHP F-AHP ROC wildfires wildlife sanctuaries |
| url | https://www.mdpi.com/2571-6255/6/2/44 |
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