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...
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Autor*in:	Yuheng Ji [verfasserIn] Dan Wang [verfasserIn] Qingliang Li [verfasserIn] Taihui Liu [verfasserIn] Yu Bai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	deep learning wildfire spatiotemporal SLA-ConvLSTM

Übergeordnetes Werk:	In: Forests - MDPI AG, 2010, 15(2024), 1, p 216
Übergeordnetes Werk:	volume:15 ; year:2024 ; number:1, p 216

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/f15010216

Katalog-ID:	DOAJ096361662

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520			\|a 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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language	English
source	In Forests 15(2024), 1, p 216 volume:15 year:2024 number:1, p 216
sourceStr	In Forests 15(2024), 1, p 216 volume:15 year:2024 number:1, p 216
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	deep learning wildfire spatiotemporal SLA-ConvLSTM Plant ecology
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container_title	Forests
authorswithroles_txt_mv	Yuheng Ji @@aut@@ Dan Wang @@aut@@ Qingliang Li @@aut@@ Taihui Liu @@aut@@ Yu Bai @@aut@@
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callnumber-first	Q - Science
author	Yuheng Ji
spellingShingle	Yuheng Ji misc QK900-989 misc deep learning misc wildfire misc spatiotemporal misc SLA-ConvLSTM misc Plant ecology Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables
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topic_title	QK900-989 Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables deep learning wildfire spatiotemporal SLA-ConvLSTM
topic	misc QK900-989 misc deep learning misc wildfire misc spatiotemporal misc SLA-ConvLSTM misc Plant ecology
topic_unstemmed	misc QK900-989 misc deep learning misc wildfire misc spatiotemporal misc SLA-ConvLSTM misc Plant ecology
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title	Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables
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title_full	Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables
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title_sort	global wildfire danger predictions based on deep learning taking into account static and dynamic variables
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title_auth	Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables
abstract	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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title_short	Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables
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