Atmospheric Modeling for Wildfire Prediction
Machine learning and artificial intelligence models have become popular for climate change prediction. Forested regions in California and Western Australia are increasingly facing intense wildfires, while other parts of the world face various climate-related challenges. To address these issues, mach...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/4/441 |
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| author | Fathima Nuzla Ismail Brendon J. Woodford Sherlock A. Licorish |
| author_facet | Fathima Nuzla Ismail Brendon J. Woodford Sherlock A. Licorish |
| author_sort | Fathima Nuzla Ismail |
| collection | DOAJ |
| description | Machine learning and artificial intelligence models have become popular for climate change prediction. Forested regions in California and Western Australia are increasingly facing intense wildfires, while other parts of the world face various climate-related challenges. To address these issues, machine learning and artificial intelligence models have been developed to predict wildfire risks and support mitigation strategies. Our study focuses on developing wildfire prediction models using one-class classification algorithms. These include Support Vector Machine, Isolation Forest, AutoEncoder, Variational AutoEncoder, Deep Support Vector Data Description, and Adversarially Learned Anomaly Detection. The models were validated through five-fold cross-validation to minimize bias in selecting training and testing data. The results showed that these one-class machine learning models outperformed two-class machine learning models based on the same ground truth data, achieving mean accuracy levels between 90% and 99%. Additionally, we employed Shapley values to identify the most significant features affecting the wildfire prediction models, contributing a novel perspective to wildfire prediction research. When analyzing models trained on the California dataset, seasonal maximum and mean dew point temperatures were critical factors. These insights can significantly improve wildfire mitigation strategies. Furthermore, we have made these models accessible and user-friendly by operationalizing them through a REST API using Python Flask 1.1.2 and developing a web-based tool. |
| format | Article |
| id | doaj-art-bec99107c6bb4016a6da8f15a4e57b13 |
| institution | OA Journals |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-bec99107c6bb4016a6da8f15a4e57b132025-08-20T02:17:24ZengMDPI AGAtmosphere2073-44332025-04-0116444110.3390/atmos16040441Atmospheric Modeling for Wildfire PredictionFathima Nuzla Ismail0Brendon J. Woodford1Sherlock A. Licorish2Department of Mathematics, State University of New York at Buffalo, Buffalo, NY 14260, USASchool of Computing, University of Otago, 362 Leith Street, Dunedin North 9016, Otago, New ZealandSchool of Computing, University of Otago, 362 Leith Street, Dunedin North 9016, Otago, New ZealandMachine learning and artificial intelligence models have become popular for climate change prediction. Forested regions in California and Western Australia are increasingly facing intense wildfires, while other parts of the world face various climate-related challenges. To address these issues, machine learning and artificial intelligence models have been developed to predict wildfire risks and support mitigation strategies. Our study focuses on developing wildfire prediction models using one-class classification algorithms. These include Support Vector Machine, Isolation Forest, AutoEncoder, Variational AutoEncoder, Deep Support Vector Data Description, and Adversarially Learned Anomaly Detection. The models were validated through five-fold cross-validation to minimize bias in selecting training and testing data. The results showed that these one-class machine learning models outperformed two-class machine learning models based on the same ground truth data, achieving mean accuracy levels between 90% and 99%. Additionally, we employed Shapley values to identify the most significant features affecting the wildfire prediction models, contributing a novel perspective to wildfire prediction research. When analyzing models trained on the California dataset, seasonal maximum and mean dew point temperatures were critical factors. These insights can significantly improve wildfire mitigation strategies. Furthermore, we have made these models accessible and user-friendly by operationalizing them through a REST API using Python Flask 1.1.2 and developing a web-based tool.https://www.mdpi.com/2073-4433/16/4/441one-class machine learning modelswildfire predictionCaliforniaWestern Australiawildfire prediction tool |
| spellingShingle | Fathima Nuzla Ismail Brendon J. Woodford Sherlock A. Licorish Atmospheric Modeling for Wildfire Prediction Atmosphere one-class machine learning models wildfire prediction California Western Australia wildfire prediction tool |
| title | Atmospheric Modeling for Wildfire Prediction |
| title_full | Atmospheric Modeling for Wildfire Prediction |
| title_fullStr | Atmospheric Modeling for Wildfire Prediction |
| title_full_unstemmed | Atmospheric Modeling for Wildfire Prediction |
| title_short | Atmospheric Modeling for Wildfire Prediction |
| title_sort | atmospheric modeling for wildfire prediction |
| topic | one-class machine learning models wildfire prediction California Western Australia wildfire prediction tool |
| url | https://www.mdpi.com/2073-4433/16/4/441 |
| work_keys_str_mv | AT fathimanuzlaismail atmosphericmodelingforwildfireprediction AT brendonjwoodford atmosphericmodelingforwildfireprediction AT sherlockalicorish atmosphericmodelingforwildfireprediction |