A Machine Learning Algorithm Approach to Map Wildfire Probability Based on Static Parameters
Wildfires are occurring throughout the world, causing more damage to plant and animal species, humans, and the environment. Fire danger indices are useful for forecasting fire danger, and these indices involve the integration of both static and dynamic indices. The static indicators, such as vegetat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Environmental Sciences Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4931/13/1/10 |
| _version_ | 1797611890383257600 |
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| author | Suresh Babu KV Vernon Visser Glenn Moncrieff Jasper Slingsby Res Altwegg |
| author_facet | Suresh Babu KV Vernon Visser Glenn Moncrieff Jasper Slingsby Res Altwegg |
| author_sort | Suresh Babu KV |
| collection | DOAJ |
| description | Wildfires are occurring throughout the world, causing more damage to plant and animal species, humans, and the environment. Fire danger indices are useful for forecasting fire danger, and these indices involve the integration of both static and dynamic indices. The static indicators, such as vegetation, topographic characteristics, etc., are constant over the study area and are variables that promote the ignition of fires and, therefore, are useful for understanding fire patterns and distribution in the study area. In this study, the Static Fire Danger Index (SFDI) is generated using the MODIS Land cover type (MCD12Q1), the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and the Open Street Map datasets by applying a Random Forest (RF) algorithm. Random Forest (RF) is a machine learning algorithm that can automatically select important variables and flexibly evaluate the complex interactions between variables. The MODIS, TERRA, and AQUA active fire points (MCD14) during 2011–2017 have been used to train the RF algorithm, and fire probability maps are generated for the years 2018 and 2019. The fire probability maps are categorized into five fire danger classes, i.e., very low, low, medium, high, and very high, on the basis of the RF prediction probability values. The active fire points (MCD14) have been used to validate the SFDI, and accuracy is found to be 85.74% and 87.91% for the years 2018 and 2019, respectively. Thus, the machine learning algorithm is successfully applied for generating the wildfire susceptibility maps. |
| first_indexed | 2024-03-11T06:35:04Z |
| format | Article |
| id | doaj.art-899f6feb7be14d249661602076d52ae9 |
| institution | Directory Open Access Journal |
| issn | 2673-4931 |
| language | English |
| last_indexed | 2024-03-11T06:35:04Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Environmental Sciences Proceedings |
| spelling | doaj.art-899f6feb7be14d249661602076d52ae92023-11-17T10:59:00ZengMDPI AGEnvironmental Sciences Proceedings2673-49312021-08-011311010.3390/IECF2021-10806A Machine Learning Algorithm Approach to Map Wildfire Probability Based on Static ParametersSuresh Babu KV0Vernon Visser1Glenn Moncrieff2Jasper Slingsby3Res Altwegg4Centre for Statistics in Ecology, Environment and Conservation, Department of Statistical Sciences, University of Cape Town, Cape Town 7925, South AfricaCentre for Statistics in Ecology, Environment and Conservation, Department of Statistical Sciences, University of Cape Town, Cape Town 7925, South AfricaFynbos Node South African Environmental Observation Network (SAEON), Cape Town 7925, South AfricaFynbos Node South African Environmental Observation Network (SAEON), Cape Town 7925, South AfricaCentre for Statistics in Ecology, Environment and Conservation, Department of Statistical Sciences, University of Cape Town, Cape Town 7925, South AfricaWildfires are occurring throughout the world, causing more damage to plant and animal species, humans, and the environment. Fire danger indices are useful for forecasting fire danger, and these indices involve the integration of both static and dynamic indices. The static indicators, such as vegetation, topographic characteristics, etc., are constant over the study area and are variables that promote the ignition of fires and, therefore, are useful for understanding fire patterns and distribution in the study area. In this study, the Static Fire Danger Index (SFDI) is generated using the MODIS Land cover type (MCD12Q1), the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and the Open Street Map datasets by applying a Random Forest (RF) algorithm. Random Forest (RF) is a machine learning algorithm that can automatically select important variables and flexibly evaluate the complex interactions between variables. The MODIS, TERRA, and AQUA active fire points (MCD14) during 2011–2017 have been used to train the RF algorithm, and fire probability maps are generated for the years 2018 and 2019. The fire probability maps are categorized into five fire danger classes, i.e., very low, low, medium, high, and very high, on the basis of the RF prediction probability values. The active fire points (MCD14) have been used to validate the SFDI, and accuracy is found to be 85.74% and 87.91% for the years 2018 and 2019, respectively. Thus, the machine learning algorithm is successfully applied for generating the wildfire susceptibility maps.https://www.mdpi.com/2673-4931/13/1/10wildfiresRFMODISSFDImachine learning |
| spellingShingle | Suresh Babu KV Vernon Visser Glenn Moncrieff Jasper Slingsby Res Altwegg A Machine Learning Algorithm Approach to Map Wildfire Probability Based on Static Parameters Environmental Sciences Proceedings wildfires RF MODIS SFDI machine learning |
| title | A Machine Learning Algorithm Approach to Map Wildfire Probability Based on Static Parameters |
| title_full | A Machine Learning Algorithm Approach to Map Wildfire Probability Based on Static Parameters |
| title_fullStr | A Machine Learning Algorithm Approach to Map Wildfire Probability Based on Static Parameters |
| title_full_unstemmed | A Machine Learning Algorithm Approach to Map Wildfire Probability Based on Static Parameters |
| title_short | A Machine Learning Algorithm Approach to Map Wildfire Probability Based on Static Parameters |
| title_sort | machine learning algorithm approach to map wildfire probability based on static parameters |
| topic | wildfires RF MODIS SFDI machine learning |
| url | https://www.mdpi.com/2673-4931/13/1/10 |
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