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
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- Nonlinear model order reduction of engineering turbulence using data-assisted neural networks C. Zhu et al. 10.1016/j.cpc.2025.109501
- A Generative Model for Surrogates of Spatial-Temporal Wildfire Nowcasting S. Cheng et al. 10.1109/TETCI.2023.3298535
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- 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
- Digital Twins in Agriculture and Forestry: A Review A. Tagarakis et al. 10.3390/s24103117
- Non-Convex Metric Learning-Based Trajectory Clustering Algorithm X. Lei & H. Wang 10.3390/math13030387
- Advanced Deep Learning Approaches for Forecasting High-Resolution Fire Weather Index (FWI) over CONUS: Integration of GNN-LSTM, GNN-TCNN, and GNN-DeepAR S. Shahriar et al. 10.3390/rs17030515
- 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
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23 citations as recorded by crossref.
- Feasibility Study on Contactless Feature Analysis for Early Drowsiness Detection in Driving Scenarios Y. Choi et al. 10.3390/electronics14040662
- 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
- Nonlinear model order reduction of engineering turbulence using data-assisted neural networks C. Zhu et al. 10.1016/j.cpc.2025.109501
- A Generative Model for Surrogates of Spatial-Temporal Wildfire Nowcasting S. Cheng et al. 10.1109/TETCI.2023.3298535
- PoachNet: Predicting Poaching Using an Ontology-Based Knowledge Graph N. Hamed et al. 10.3390/s24248142
- Impact of Data Capture Methods on 3D Reconstruction with Gaussian Splatting D. Rangelov et al. 10.3390/jimaging11020065
- 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
- Next Generation Computing and Communication Hub for First Responders in Smart Cities O. Shaposhnyk et al. 10.3390/s24072366
- Design of a reinforcement learning-based intelligent car transfer planning system for parking lots F. Guo et al. 10.3934/mbe.2024044
- 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
- Digital Twins in Agriculture and Forestry: A Review A. Tagarakis et al. 10.3390/s24103117
- Non-Convex Metric Learning-Based Trajectory Clustering Algorithm X. Lei & H. Wang 10.3390/math13030387
- Advanced Deep Learning Approaches for Forecasting High-Resolution Fire Weather Index (FWI) over CONUS: Integration of GNN-LSTM, GNN-TCNN, and GNN-DeepAR S. Shahriar et al. 10.3390/rs17030515
- 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
- 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
- Error-based efficient parameter space partitioning for mesh adaptation and local reduced order models S. Bhat et al. 10.1016/j.cma.2024.117649
- Hierarchical Autoencoder-Based Lossy Compression for Large-Scale High-Resolution Scientific Data H. Le & J. Tao 10.69709/CAIC.2024.193132
Latest update: 02 Mar 2025
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...
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