Articles | Volume 23, issue 5
https://doi.org/10.5194/nhess-23-1755-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-23-1755-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 May 2023
Research article |
| 12 May 2023
| 12 May 2023
Reduced-order digital twin and latent data assimilation for global wildfire prediction
Caili Zhong
Department of Earth Science and Engineering, Imperial College London, London, United Kingdom
Sibo Cheng
CORRESPONDING AUTHOR
Data Science Institute, Imperial College London, London, United
Kingdom
Matthew Kasoar
Department of Physics, Imperial College London, London, United Kingdom
Rossella Arcucci
Department of Earth Science and Engineering, Imperial College London, London, United Kingdom
Viewed
Total article views: 2,174 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 10 Nov 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,700 | 401 | 73 | 2,174 | 45 | 45 |
- HTML: 1,700
- PDF: 401
- XML: 73
- Total: 2,174
- BibTeX: 45
- EndNote: 45
Total article views: 1,655 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 12 May 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,333 | 265 | 57 | 1,655 | 41 | 42 |
- HTML: 1,333
- PDF: 265
- XML: 57
- Total: 1,655
- BibTeX: 41
- EndNote: 42
Total article views: 519 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 10 Nov 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 367 | 136 | 16 | 519 | 4 | 3 |
- HTML: 367
- PDF: 136
- XML: 16
- Total: 519
- BibTeX: 4
- EndNote: 3
Viewed (geographical distribution)
Total article views: 2,174 (including HTML, PDF, and XML)
Thereof 2,100 with geography defined
and 74 with unknown origin.
Total article views: 1,655 (including HTML, PDF, and XML)
Thereof 1,594 with geography defined
and 61 with unknown origin.
Total article views: 519 (including HTML, PDF, and XML)
Thereof 506 with geography defined
and 13 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
17 citations as recorded by crossref.
- Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities Z. Ma et al. 10.1007/s10462-024-10764-9
- FUZ-SMO: A fuzzy slime mould optimizer for mitigating false alarm rates in the classification of underwater datasets using deep convolutional neural networks D. liang Zhang et al. 10.1016/j.heliyon.2024.e28681
- A Generative Model for Surrogates of Spatial-Temporal Wildfire Nowcasting S. Cheng et al. 10.1109/TETCI.2023.3298535
- Digital Twins in Agriculture and Forestry: A Review A. Tagarakis et al. 10.3390/s24103117
- Evaluation of Flooding Disaster Risks for Subway Stations Based on the PSR Cloud Model J. Liu et al. 10.3390/su152115552
- Integrating Web-Based Weather Data into Building Information Modeling Models through Robot Process Automation E. Atencio et al. 10.3390/app14199109
- Modeling of Wildfire Digital Twin: Research Progress in Detection, Simulation, and Prediction Techniques Y. Huang et al. 10.3390/fire7110412
- Digital twin-based decision support systems for natural disaster management: A systematic review of current trends and approaches S. Inyang & F. Taghikhah 10.1016/j.sctalk.2024.100406
- R-CNN and YOLOV4 based Deep Learning Model for intelligent detection of weaponries in real time video K. Vijayakumar et al. 10.3934/mbe.2023956
- AI-Assisted Fatigue and Stamina Control for Performance Sports on IMU-Generated Multivariate Times Series Datasets A. Biró et al. 10.3390/s24010132
- Next Generation Computing and Communication Hub for First Responders in Smart Cities O. Shaposhnyk et al. 10.3390/s24072366
- Latent space-based machine learning prediction of coupled flame-flow fields in a hydrogen-enriched syngas combustor Y. Yang et al. 10.1016/j.ijhydene.2024.11.103
- Digital post-disaster risk management twinning: A review and improved conceptual framework U. Lagap & S. Ghaffarian 10.1016/j.ijdrr.2024.104629
- AI-Driven precision in solar forecasting: Breakthroughs in machine learning and deep learning A. Nadeem et al. 10.3934/geosci.2024035
- Design of a reinforcement learning-based intelligent car transfer planning system for parking lots F. Guo et al. 10.3934/mbe.2024044
- Hierarchical Autoencoder-Based Lossy Compression for Large-Scale High-Resolution Scientific Data H. Le & J. Tao 10.69709/CAIC.2024.193132
- A textual and visual features-jointly driven hybrid intelligent system for digital physical education teaching quality evaluation B. Zeng et al. 10.3934/mbe.2023606
16 citations as recorded by crossref.
- Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities Z. Ma et al. 10.1007/s10462-024-10764-9
- FUZ-SMO: A fuzzy slime mould optimizer for mitigating false alarm rates in the classification of underwater datasets using deep convolutional neural networks D. liang Zhang et al. 10.1016/j.heliyon.2024.e28681
- A Generative Model for Surrogates of Spatial-Temporal Wildfire Nowcasting S. Cheng et al. 10.1109/TETCI.2023.3298535
- Digital Twins in Agriculture and Forestry: A Review A. Tagarakis et al. 10.3390/s24103117
- Evaluation of Flooding Disaster Risks for Subway Stations Based on the PSR Cloud Model J. Liu et al. 10.3390/su152115552
- Integrating Web-Based Weather Data into Building Information Modeling Models through Robot Process Automation E. Atencio et al. 10.3390/app14199109
- Modeling of Wildfire Digital Twin: Research Progress in Detection, Simulation, and Prediction Techniques Y. Huang et al. 10.3390/fire7110412
- Digital twin-based decision support systems for natural disaster management: A systematic review of current trends and approaches S. Inyang & F. Taghikhah 10.1016/j.sctalk.2024.100406
- R-CNN and YOLOV4 based Deep Learning Model for intelligent detection of weaponries in real time video K. Vijayakumar et al. 10.3934/mbe.2023956
- AI-Assisted Fatigue and Stamina Control for Performance Sports on IMU-Generated Multivariate Times Series Datasets A. Biró et al. 10.3390/s24010132
- Next Generation Computing and Communication Hub for First Responders in Smart Cities O. Shaposhnyk et al. 10.3390/s24072366
- Latent space-based machine learning prediction of coupled flame-flow fields in a hydrogen-enriched syngas combustor Y. Yang et al. 10.1016/j.ijhydene.2024.11.103
- Digital post-disaster risk management twinning: A review and improved conceptual framework U. Lagap & S. Ghaffarian 10.1016/j.ijdrr.2024.104629
- AI-Driven precision in solar forecasting: Breakthroughs in machine learning and deep learning A. Nadeem et al. 10.3934/geosci.2024035
- Design of a reinforcement learning-based intelligent car transfer planning system for parking lots F. Guo et al. 10.3934/mbe.2024044
- Hierarchical Autoencoder-Based Lossy Compression for Large-Scale High-Resolution Scientific Data H. Le & J. Tao 10.69709/CAIC.2024.193132
Latest update: 13 Dec 2024
Short summary
This paper introduces a digital twin fire model using machine learning techniques to improve the efficiency of global wildfire predictions. The proposed model also manages to efficiently adjust the prediction results thanks to data assimilation techniques. The proposed digital twin runs 500 times faster than the current state-of-the-art physics-based model.
This paper introduces a digital twin fire model using machine learning techniques to improve the...
Special issue
Altmetrics
Final-revised paper
Preprint