Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables
Climate change will intensify the danger of wildfires, significantly impacting human life. Deep Learning (DL) has been extensively applied in wildfire prediction research. In the realm of wildfire prediction, previous deep learning methods have overlooked the inherent differences between static posi...
Ausführliche Beschreibung
Autor*in: |
Yuheng Ji [verfasserIn] Dan Wang [verfasserIn] Qingliang Li [verfasserIn] Taihui Liu [verfasserIn] Yu Bai [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
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2024 |
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Übergeordnetes Werk: |
In: Forests - MDPI AG, 2010, 15(2024), 1, p 216 |
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Übergeordnetes Werk: |
volume:15 ; year:2024 ; number:1, p 216 |
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DOI / URN: |
10.3390/f15010216 |
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Katalog-ID: |
DOAJ096361662 |
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10.3390/f15010216 doi (DE-627)DOAJ096361662 (DE-599)DOAJ411e68690fa045b9ad1163fc7eed6af5 DE-627 ger DE-627 rakwb eng QK900-989 Yuheng Ji verfasserin aut Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Climate change will intensify the danger of wildfires, significantly impacting human life. Deep Learning (DL) has been extensively applied in wildfire prediction research. In the realm of wildfire prediction, previous deep learning methods have overlooked the inherent differences between static positional information and dynamic variables. Additionally, most existing deep learning models have not integrated the global system characteristics of the Earth’s features and teleconnection during the learning phase. Here, we propose a static location-aware ConvLSTM (SLA-ConvLSTM) model that is aware of static positional elements and interconnected with global information and teleconnection. The model we propose can discern the influence of dynamic variables across various geographical locations on predictive outcomes. Compared with other deep learning models, our SLA-ConvLSTM model has achieved commendable performance. The outcomes indicate that the collaborative interplay of spatiotemporal features and the extraction of static positional information present a promising technique for wildfire prediction. Moreover, the incorporation of climate indices and global feature variables enhances the predictive capability of the model in wildfire prediction. deep learning wildfire spatiotemporal SLA-ConvLSTM Plant ecology Dan Wang verfasserin aut Qingliang Li verfasserin aut Taihui Liu verfasserin aut Yu Bai verfasserin aut In Forests MDPI AG, 2010 15(2024), 1, p 216 (DE-627)614095689 (DE-600)2527081-3 19994907 nnns volume:15 year:2024 number:1, p 216 https://doi.org/10.3390/f15010216 kostenfrei https://doaj.org/article/411e68690fa045b9ad1163fc7eed6af5 kostenfrei https://www.mdpi.com/1999-4907/15/1/216 kostenfrei https://doaj.org/toc/1999-4907 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 15 2024 1, p 216 |
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10.3390/f15010216 doi (DE-627)DOAJ096361662 (DE-599)DOAJ411e68690fa045b9ad1163fc7eed6af5 DE-627 ger DE-627 rakwb eng QK900-989 Yuheng Ji verfasserin aut Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Climate change will intensify the danger of wildfires, significantly impacting human life. Deep Learning (DL) has been extensively applied in wildfire prediction research. In the realm of wildfire prediction, previous deep learning methods have overlooked the inherent differences between static positional information and dynamic variables. Additionally, most existing deep learning models have not integrated the global system characteristics of the Earth’s features and teleconnection during the learning phase. Here, we propose a static location-aware ConvLSTM (SLA-ConvLSTM) model that is aware of static positional elements and interconnected with global information and teleconnection. The model we propose can discern the influence of dynamic variables across various geographical locations on predictive outcomes. Compared with other deep learning models, our SLA-ConvLSTM model has achieved commendable performance. The outcomes indicate that the collaborative interplay of spatiotemporal features and the extraction of static positional information present a promising technique for wildfire prediction. Moreover, the incorporation of climate indices and global feature variables enhances the predictive capability of the model in wildfire prediction. deep learning wildfire spatiotemporal SLA-ConvLSTM Plant ecology Dan Wang verfasserin aut Qingliang Li verfasserin aut Taihui Liu verfasserin aut Yu Bai verfasserin aut In Forests MDPI AG, 2010 15(2024), 1, p 216 (DE-627)614095689 (DE-600)2527081-3 19994907 nnns volume:15 year:2024 number:1, p 216 https://doi.org/10.3390/f15010216 kostenfrei https://doaj.org/article/411e68690fa045b9ad1163fc7eed6af5 kostenfrei https://www.mdpi.com/1999-4907/15/1/216 kostenfrei https://doaj.org/toc/1999-4907 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 15 2024 1, p 216 |
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10.3390/f15010216 doi (DE-627)DOAJ096361662 (DE-599)DOAJ411e68690fa045b9ad1163fc7eed6af5 DE-627 ger DE-627 rakwb eng QK900-989 Yuheng Ji verfasserin aut Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Climate change will intensify the danger of wildfires, significantly impacting human life. Deep Learning (DL) has been extensively applied in wildfire prediction research. In the realm of wildfire prediction, previous deep learning methods have overlooked the inherent differences between static positional information and dynamic variables. Additionally, most existing deep learning models have not integrated the global system characteristics of the Earth’s features and teleconnection during the learning phase. Here, we propose a static location-aware ConvLSTM (SLA-ConvLSTM) model that is aware of static positional elements and interconnected with global information and teleconnection. The model we propose can discern the influence of dynamic variables across various geographical locations on predictive outcomes. Compared with other deep learning models, our SLA-ConvLSTM model has achieved commendable performance. The outcomes indicate that the collaborative interplay of spatiotemporal features and the extraction of static positional information present a promising technique for wildfire prediction. Moreover, the incorporation of climate indices and global feature variables enhances the predictive capability of the model in wildfire prediction. deep learning wildfire spatiotemporal SLA-ConvLSTM Plant ecology Dan Wang verfasserin aut Qingliang Li verfasserin aut Taihui Liu verfasserin aut Yu Bai verfasserin aut In Forests MDPI AG, 2010 15(2024), 1, p 216 (DE-627)614095689 (DE-600)2527081-3 19994907 nnns volume:15 year:2024 number:1, p 216 https://doi.org/10.3390/f15010216 kostenfrei https://doaj.org/article/411e68690fa045b9ad1163fc7eed6af5 kostenfrei https://www.mdpi.com/1999-4907/15/1/216 kostenfrei https://doaj.org/toc/1999-4907 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 15 2024 1, p 216 |
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10.3390/f15010216 doi (DE-627)DOAJ096361662 (DE-599)DOAJ411e68690fa045b9ad1163fc7eed6af5 DE-627 ger DE-627 rakwb eng QK900-989 Yuheng Ji verfasserin aut Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Climate change will intensify the danger of wildfires, significantly impacting human life. Deep Learning (DL) has been extensively applied in wildfire prediction research. In the realm of wildfire prediction, previous deep learning methods have overlooked the inherent differences between static positional information and dynamic variables. Additionally, most existing deep learning models have not integrated the global system characteristics of the Earth’s features and teleconnection during the learning phase. Here, we propose a static location-aware ConvLSTM (SLA-ConvLSTM) model that is aware of static positional elements and interconnected with global information and teleconnection. The model we propose can discern the influence of dynamic variables across various geographical locations on predictive outcomes. Compared with other deep learning models, our SLA-ConvLSTM model has achieved commendable performance. The outcomes indicate that the collaborative interplay of spatiotemporal features and the extraction of static positional information present a promising technique for wildfire prediction. Moreover, the incorporation of climate indices and global feature variables enhances the predictive capability of the model in wildfire prediction. deep learning wildfire spatiotemporal SLA-ConvLSTM Plant ecology Dan Wang verfasserin aut Qingliang Li verfasserin aut Taihui Liu verfasserin aut Yu Bai verfasserin aut In Forests MDPI AG, 2010 15(2024), 1, p 216 (DE-627)614095689 (DE-600)2527081-3 19994907 nnns volume:15 year:2024 number:1, p 216 https://doi.org/10.3390/f15010216 kostenfrei https://doaj.org/article/411e68690fa045b9ad1163fc7eed6af5 kostenfrei https://www.mdpi.com/1999-4907/15/1/216 kostenfrei https://doaj.org/toc/1999-4907 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 15 2024 1, p 216 |
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QK900-989 Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables deep learning wildfire spatiotemporal SLA-ConvLSTM |
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Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables |
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Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables |
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Climate change will intensify the danger of wildfires, significantly impacting human life. Deep Learning (DL) has been extensively applied in wildfire prediction research. In the realm of wildfire prediction, previous deep learning methods have overlooked the inherent differences between static positional information and dynamic variables. Additionally, most existing deep learning models have not integrated the global system characteristics of the Earth’s features and teleconnection during the learning phase. Here, we propose a static location-aware ConvLSTM (SLA-ConvLSTM) model that is aware of static positional elements and interconnected with global information and teleconnection. The model we propose can discern the influence of dynamic variables across various geographical locations on predictive outcomes. Compared with other deep learning models, our SLA-ConvLSTM model has achieved commendable performance. The outcomes indicate that the collaborative interplay of spatiotemporal features and the extraction of static positional information present a promising technique for wildfire prediction. Moreover, the incorporation of climate indices and global feature variables enhances the predictive capability of the model in wildfire prediction. |
abstractGer |
Climate change will intensify the danger of wildfires, significantly impacting human life. Deep Learning (DL) has been extensively applied in wildfire prediction research. In the realm of wildfire prediction, previous deep learning methods have overlooked the inherent differences between static positional information and dynamic variables. Additionally, most existing deep learning models have not integrated the global system characteristics of the Earth’s features and teleconnection during the learning phase. Here, we propose a static location-aware ConvLSTM (SLA-ConvLSTM) model that is aware of static positional elements and interconnected with global information and teleconnection. The model we propose can discern the influence of dynamic variables across various geographical locations on predictive outcomes. Compared with other deep learning models, our SLA-ConvLSTM model has achieved commendable performance. The outcomes indicate that the collaborative interplay of spatiotemporal features and the extraction of static positional information present a promising technique for wildfire prediction. Moreover, the incorporation of climate indices and global feature variables enhances the predictive capability of the model in wildfire prediction. |
abstract_unstemmed |
Climate change will intensify the danger of wildfires, significantly impacting human life. Deep Learning (DL) has been extensively applied in wildfire prediction research. In the realm of wildfire prediction, previous deep learning methods have overlooked the inherent differences between static positional information and dynamic variables. Additionally, most existing deep learning models have not integrated the global system characteristics of the Earth’s features and teleconnection during the learning phase. Here, we propose a static location-aware ConvLSTM (SLA-ConvLSTM) model that is aware of static positional elements and interconnected with global information and teleconnection. The model we propose can discern the influence of dynamic variables across various geographical locations on predictive outcomes. Compared with other deep learning models, our SLA-ConvLSTM model has achieved commendable performance. The outcomes indicate that the collaborative interplay of spatiotemporal features and the extraction of static positional information present a promising technique for wildfire prediction. Moreover, the incorporation of climate indices and global feature variables enhances the predictive capability of the model in wildfire prediction. |
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Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables |
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|
score |
7.399806 |