Advanced Wildfire Prediction in Morocco: Developing a Deep Learning Dataset From Multisource Observations
This study introduces a novel dataset for wildfire prediction in Morocco, integrating multisource observations to address the country’s unique geographical and climatic challenges. We compile essential environmental indicators and employ state-of-the-art machine learning (ML) and deep lea...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10798445/ |
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| author | Ayoub Jadouli Chaker El El Amrani |
| author_facet | Ayoub Jadouli Chaker El El Amrani |
| author_sort | Ayoub Jadouli |
| collection | DOAJ |
| description | This study introduces a novel dataset for wildfire prediction in Morocco, integrating multisource observations to address the country’s unique geographical and climatic challenges. We compile essential environmental indicators and employ state-of-the-art machine learning (ML) and deep learning (DL) algorithms to predict next-day wildfire occurrences. Our best-performing models achieve an accuracy of up to 90%, significantly improving upon traditional approaches. The key contributions include: 1) A localized dataset tailored to Morocco’s conditions; 2) benchmarking of advanced ML and DL algorithms; and 3) open sharing of the dataset and codebase. We discuss the potential applicability of our methodology to other regions and highlight future research directions. This work advances dataset creation techniques and emphasizes the importance of localized research for effective wildfire management strategies in underrepresented areas. |
| format | Article |
| id | doaj-art-36394c7823cb4e5fa11f90a481619290 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-36394c7823cb4e5fa11f90a4816192902024-12-21T00:01:01ZengIEEEIEEE Access2169-35362024-01-011219173319174710.1109/ACCESS.2024.351678410798445Advanced Wildfire Prediction in Morocco: Developing a Deep Learning Dataset From Multisource ObservationsAyoub Jadouli0https://orcid.org/0009-0002-6222-9575Chaker El El Amrani1Department of Computer Science and Smart Systems, Faculty of Sciences and Technology, Abdelmalek Essaâdi University, Tangier, MoroccoDepartment of Computer Science and Smart Systems, Faculty of Sciences and Technology, Abdelmalek Essaâdi University, Tangier, MoroccoThis study introduces a novel dataset for wildfire prediction in Morocco, integrating multisource observations to address the country’s unique geographical and climatic challenges. We compile essential environmental indicators and employ state-of-the-art machine learning (ML) and deep learning (DL) algorithms to predict next-day wildfire occurrences. Our best-performing models achieve an accuracy of up to 90%, significantly improving upon traditional approaches. The key contributions include: 1) A localized dataset tailored to Morocco’s conditions; 2) benchmarking of advanced ML and DL algorithms; and 3) open sharing of the dataset and codebase. We discuss the potential applicability of our methodology to other regions and highlight future research directions. This work advances dataset creation techniques and emphasizes the importance of localized research for effective wildfire management strategies in underrepresented areas.https://ieeexplore.ieee.org/document/10798445/Deep learningmachine learningwildfire predictionsatellite observationsground stationsMorocco |
| spellingShingle | Ayoub Jadouli Chaker El El Amrani Advanced Wildfire Prediction in Morocco: Developing a Deep Learning Dataset From Multisource Observations IEEE Access Deep learning machine learning wildfire prediction satellite observations ground stations Morocco |
| title | Advanced Wildfire Prediction in Morocco: Developing a Deep Learning Dataset From Multisource Observations |
| title_full | Advanced Wildfire Prediction in Morocco: Developing a Deep Learning Dataset From Multisource Observations |
| title_fullStr | Advanced Wildfire Prediction in Morocco: Developing a Deep Learning Dataset From Multisource Observations |
| title_full_unstemmed | Advanced Wildfire Prediction in Morocco: Developing a Deep Learning Dataset From Multisource Observations |
| title_short | Advanced Wildfire Prediction in Morocco: Developing a Deep Learning Dataset From Multisource Observations |
| title_sort | advanced wildfire prediction in morocco developing a deep learning dataset from multisource observations |
| topic | Deep learning machine learning wildfire prediction satellite observations ground stations Morocco |
| url | https://ieeexplore.ieee.org/document/10798445/ |
| work_keys_str_mv | AT ayoubjadouli advancedwildfirepredictioninmoroccodevelopingadeeplearningdatasetfrommultisourceobservations AT chakerelelamrani advancedwildfirepredictioninmoroccodevelopingadeeplearningdatasetfrommultisourceobservations |