Imperial College London

DrRossellaArcucci

Faculty of EngineeringDepartment of Earth Science & Engineering

Senior Lecturer in Data Science and Machine Learning
 
 
 
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Contact

 

r.arcucci Website

 
 
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Location

 

Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Publication Type
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83 results found

Zhou H, Cheng S, Arcucci R, 2024, Multi-fidelity physics constrained neural networks for dynamical systems, Computer Methods in Applied Mechanics and Engineering, Vol: 420, ISSN: 0045-7825

Physics-constrained neural networks are commonly employed to enhance prediction robustness compared to purely data-driven models, achieved through the inclusion of physical constraint losses during the model training process. However, one of the major challenges of physics-constrained neural networks consists of the training complexity especially for high-dimensional systems. In fact, conventional physics-constrained models rely on singular-fidelity data necessitating the assessment of physical constraints within high-dimensional fields, which introduces computational difficulties. Furthermore, due to the fixed input size of the neural networks, employing multi-fidelity training data can also be cumbersome. In this paper, we propose the Multi-Scale Physics-Constrained Neural Network (MSPCNN), which offers a novel methodology for incorporating data with different levels of fidelity into a unified latent space through a customised multi-fidelity autoencoder. Additionally, multiple decoders are concurrently trained to map latent representations of inputs into various fidelity physical spaces. As a result, during the training of predictive models, physical constraints can be evaluated within low-fidelity spaces, yielding a trade-off between training efficiency and accuracy. In addition, unlike conventional methods, MSPCNN also manages to employ multi-fidelity data to train the predictive model. We assess the performance of MSPCNN in two fluid dynamics problems, namely a two-dimensional Burgers’ system and a shallow water system. Numerical results clearly demonstrate the enhancement of prediction accuracy and noise robustness when introducing physical constraints in low-fidelity fields. On the other hand, as expected, the training complexity can be significantly reduced by computing physical constraint loss in the low-fidelity field rather than the high-fidelity one.

Journal article

Hu J, Zhu K, Cheng S, Kovalchuk NM, Soulsby A, Simmons MJH, Matar OK, Arcucci Ret al., 2024, Explainable AI models for predicting drop coalescence in microfluidics device, Chemical Engineering Journal, Vol: 481, ISSN: 1385-8947

In the field of chemical engineering, understanding the dynamics and probability of drop coalescence is not just an academic pursuit, but a critical requirement for advancing process design by applying energy only where it is needed to build necessary interfacial structures, increasing efficiency towards Net Zero manufacture. This research applies machine learning predictive models to unravel the sophisticated relationships embedded in the experimental data on drop coalescence in a microfluidics device. Through the deployment of SHapley Additive exPlanations values, critical features relevant to coalescence processes are consistently identified. Comprehensive feature ablation tests further delineate the robustness and susceptibility of each model. Furthermore, the incorporation of Local Interpretable Model-agnostic Explanations for local interpretability offers an elucidative perspective, clarifying the intricate decision-making mechanisms inherent to each model's predictions. As a result, this research provides the relative importance of the features for the outcome of drop interactions. It also underscores the pivotal role of model interpretability in reinforcing confidence in machine learning predictions of complex physical phenomena that are central to chemical engineering applications.

Journal article

Cheng S, Liu C, Guo Y, Arcucci Ret al., 2024, Efficient deep data assimilation with sparse observations and time-varying sensors, Journal of Computational Physics, Vol: 496, ISSN: 0021-9991

Variational Data Assimilation (DA) has been broadly used in engineering problems for field reconstruction and prediction by performing a weighted combination of multiple sources of noisy data. In recent years, the integration of deep learning (DL) techniques in DA has shown promise in improving the efficiency and accuracy in high-dimensional dynamical systems. Nevertheless, existing deep DA approaches face difficulties in dealing with unstructured observation data, especially when the placement and number of sensors are dynamic over time. We introduce a novel variational DA scheme, named Voronoi-tessellation Inverse operator for VariatIonal Data assimilation (VIVID), that incorporates a DL inverse operator into the assimilation objective function. By leveraging the capabilities of the Voronoi-tessellation and convolutional neural networks, VIVID is adept at handling sparse, unstructured, and time-varying sensor data. Furthermore, the incorporation of the DL inverse operator establishes a direct link between observation and state space, leading to a reduction in the number of minimization steps required for DA. Additionally, VIVID can be seamlessly integrated with Proper Orthogonal Decomposition (POD) to develop an end-to-end reduced-order DA scheme, which can further expedite field reconstruction. Numerical experiments in a fluid dynamics system demonstrate that VIVID can significantly outperform existing DA and DL algorithms. The robustness of VIVID is also accessed through the application of various levels of prior error, the utilization of varying numbers of sensors, and the misspecification of error covariance in DA.

Journal article

Kalaiarasan G, Kumar P, Tomson M, Zavala-Reyes JC, Porter AE, Young G, Sephton MA, Abubakar-Waziri H, Pain CC, Adcock IM, Mumby S, Dilliway C, Fang F, Arcucci R, Chung KFet al., 2024, Particle number size distribution in three different microenvironments of London, Atmosphere, Vol: 15, ISSN: 2073-4433

We estimated the particle number distributions (PNDs), particle number concentrations (PNCs), physicochemical characteristics, meteorological effects, and respiratory deposition doses (RDD) in the human respiratory tract for three different particle modes: nucleation (N6–30), accumulation (N30–300), and coarse (N300–10,000) modes. This study was conducted in three different microenvironments (MEs) in London (indoor, IN; traffic intersection, TI; park, PK) measuring particles in the range of 6 nm–10,000 nm using an electrical low-pressure impactor (ELPI+). Mean PNCs were 1.68 ± 1.03 × 104 #cm−3, 7.00 ± 18.96 × 104 #cm−3, and 0.76 ± 0.95 × 104 #cm−3 at IN, TI, and PK, respectively. The PNDs were high for nucleation-mode particles at the TI site, especially during peak traffic hours. Wind speeds ranging from 0 to 6 ms−1 exhibit higher PNCs for nucleation- and accumulation-mode particles at TI and PK sites. Physicochemical characterisation shows trace metals, including Fe, O, and inorganic elements, that were embedded in a matrix of organic material in some samples. Alveolar RDD was higher for the nucleation and accumulation modes than the coarse-mode particles. The chemical signatures from the physicochemical characterisation indicate the varied sources at different MEs. These findings enhance our understanding of the different particle profiles at each ME and should help devise ways of reducing personal exposure at each ME.

Journal article

Liu C, Cheng S, Ding W, Arcucci Ret al., 2023, Spectral Cross-Domain Neural Network With Soft-Adaptive Threshold Spectral Enhancement., IEEE Trans Neural Netw Learn Syst, Vol: PP

Electrocardiography (ECG) signals can be considered as multivariable time series (TS). The state-of-the-art ECG data classification approaches, based on either feature engineering or deep learning techniques, treat separately spectral and time domains in machine learning systems. No spectral-time domain communication mechanism inside the classifier model can be found in current approaches, leading to difficulties in identifying complex ECG forms. In this article, we proposed a novel deep learning model named spectral cross-domain neural network (SCDNN) with a new block called soft-adaptive threshold spectral enhancement (SATSE), to simultaneously reveal the key information embedded in spectral and time domains inside the neural network. More precisely, the domain-cross information is captured by a general convolutional neural network (CNN) backbone, and different information sources are merged by a self-adaptive mechanism to mine the connection between time and spectral domains. In SATSE, the knowledge from time and spectral domains is extracted via the fast Fourier transformation (FFT) with soft trainable thresholds in modified sigmoid functions. The proposed SCDNN is tested with several classification tasks implemented on the public ECG databases PTB-XL and CPSC2018. SCDNN outperforms the state-of-the-art approaches with a low computational cost regarding a variety of metrics in all classification tasks on both databases, by finding appropriate domains from the infinite spectral mapping. The convergence of the trainable thresholds in the spectral domain is also numerically investigated in this article. The robust performance of SCDNN provides a new perspective to exploit knowledge across deep learning models from time and spectral domains. The code repository can be found: https://github.com/DL-WG/SCDNN-TS.

Journal article

Cheng S, Guo Y, Arcucci R, 2023, A Generative Model for Surrogates of Spatial-Temporal Wildfire Nowcasting, IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, ISSN: 2471-285X

Journal article

Bonavita M, Schneider R, Arcucci R, Chantry M, Chrust M, Geer A, Le Saux B, Vitolo Cet al., 2023, 2022 ECMWF-ESA workshop report: current status, progress and opportunities in machine learning for Earth System observation and prediction, NPJ CLIMATE AND ATMOSPHERIC SCIENCE, Vol: 6, ISSN: 2397-3722

Journal article

Zhu K, Cheng S, Kovalchuk N, Simmons M, Guo Y-K, Matar OK, Arcucci Ret al., 2023, Analyzing drop coalescence in microfluidic devices with a deep learning generative model, Physical Chemistry Chemical Physics, Vol: 25, Pages: 15744-15755, ISSN: 1463-9076

Predicting drop coalescence based on process parameters is crucial for experimental design in chemical engineering. However, predictive models can suffer from the lack of training data and more importantly, the label imbalance problem. In this study, we propose the use of deep learning generative models to tackle this bottleneck by training the predictive models using generated synthetic data. A novel generative model, named double space conditional variational autoencoder (DSCVAE) is developed for labelled tabular data. By introducing label constraints in both the latent and the original space, DSCVAE is capable of generating consistent and realistic samples compared to the standard conditional variational autoencoder (CVAE). Two predictive models, namely random forest and gradient boosting classifiers, are enhanced on synthetic data and their performances are evaluated based on real experimental data. Numerical results show that a considerable improvement in prediction accuracy can be achieved by using synthetic data and the proposed DSCVAE clearly outperforms the standard CVAE. This research clearly provides more insights into handling imbalanced data for classification problems, especially in chemical engineering.

Journal article

Xia Z, Ma K, Cheng S, Blackburn T, Peng Z, Zhu K, Zhang W, Xiao D, Knowles AJ, Arcucci Ret al., 2023, Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys, PHYSICAL CHEMISTRY CHEMICAL PHYSICS, Vol: 25, Pages: 15970-15987, ISSN: 1463-9076

Journal article

Cheng S, Quilodran-Casas C, Ouala S, Farchi A, Liu C, Tandeo P, Fablet R, Lucor D, Iooss B, Brajard J, Xiao D, Janjic T, Ding W, Guo Y, Carrassi A, Bocquet M, Arcucci Ret al., 2023, Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review, IEEE-CAA JOURNAL OF AUTOMATICA SINICA, Vol: 10, Pages: 1361-1387, ISSN: 2329-9266

Journal article

Zhong C, Cheng S, Kasoar M, Arcucci Ret al., 2023, Reduced-order digital twin and latent data assimilation for global wildfire prediction, NATURAL HAZARDS AND EARTH SYSTEM SCIENCES, Vol: 23, Pages: 1755-1768, ISSN: 1561-8633

Journal article

Arcucci R, Xiao D, Fang F, Navon IM, Wu P, Pain CC, Guo Y-Ket al., 2023, A reduced order with data assimilation model: Theory and practice, Computers and Fluids, Vol: 257, Pages: 1-12, ISSN: 0045-7930

Numerical simulations are extensively used as a predictive tool to better understand complex air flows and pollution transport on the scale of individual buildings, city blocks and entire cities. Fast-running Non-Intrusive Reduced Order Model (NIROM) for predicting the turbulent air flows has been proved to be an efficient method to provide numerical forecasting results. However, due to the reduced space on which the model operates, the solution includes uncertainties. Additionally, any computational methodology contributes to uncertainty due to finite precision and the consequent accumulation and amplification of round-off errors. Taking into account these uncertainties is essential for the acceptance of any numerical simulation. In this paper we combine the NIROM method with Data Assimilation (DA), the main question is how to incorporate data (e.g. from physical measurements) in models in a suitable way, in order to improve model predictions and quantify prediction uncertainty. Here, the focus is on the prediction of nonlinear dynamical systems (the classical application example being weather forecasting). DA is an uncertainty quantification technique used to incorporate observed data into a prediction model in order to improve numerical forecasted results. The Reduced Order Data Assimilation (RODA) model we propose in this paper achieves both efficiency and accuracy including Variational DA into NIROM. The model we present is applied to the pollutant dispersion within an urban environment.

Journal article

Nathanael K, Cheng S, Kovalchuk NM, Arcucci R, Simmons MJHet al., 2023, Optimization of microfluidic synthesis of silver nanoparticles: A generic approach using machine learning, CHEMICAL ENGINEERING RESEARCH & DESIGN, Vol: 193, Pages: 65-74, ISSN: 0263-8762

Journal article

Quilodran-Casas C, Arcucci R, 2023, A data-driven adversarial machine learning for 3D surrogates of unstructured computational fluid dynamic simulations, PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, Vol: 615, ISSN: 0378-4371

Journal article

Cheng S, Pain CC, Guo Y-K, Arcucci Ret al., 2023, Real-time updating of dynamic social networks for COVID-19 vaccination strategies., J Ambient Intell Humaniz Comput, Pages: 1-14, ISSN: 1868-5137

Vaccination strategy is crucial in fighting the COVID-19 pandemic. Since the supply is still limited in many countries, contact network-based interventions can be most powerful to set an efficient strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamic systems where contact networks are highly time-variant. Furthermore, the numerous mutations of SARS-CoV-2 have a significant impact on the infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamic networks collected in a high school, followed by sequential multi-layer networks generated relying on the Barabasi-Albert model emulating large-scale social networks with several communities.

Journal article

Chen S, Tai Z, Liu J, 2023, Barriers, Facilitators, and Sustainers in Tai Ji Quan Practice: A Mixed-Methods RE-AIM Assessment of College Students Versus the General Population, JOURNAL OF PHYSICAL ACTIVITY & HEALTH, Vol: 20, Pages: 239-249, ISSN: 1543-3080

Journal article

Chen J, Anastasiou C, Cheng S, Basha NM, Kahouadji L, Arcucci R, Angeli P, Matar OKet al., 2023, Computational fluid dynamics simulations of phase separation in dispersed oil-water pipe flows, Chemical Engineering Science, Vol: 267, Pages: 1-18, ISSN: 0009-2509

The separation of liquid–liquid dispersions in horizontal pipes is common in many industrial sectors. It remains challenging, however, to predict the separation characteristics of the flow evolution due to the complex flow mechanisms. In this work, Computational Fluid Dynamics (CFD) simulations of the silicone oil and water two-phase flow in a horizontal pipe are performed. Several cases are explored with different mixture velocities and oil fractions (15%-60%). OpenFOAM (version 8.0) is used to perform Eulerian-Eulerian simulations coupled with population balance models. The ‘blending factor’ in the multiphaseEulerFoam solver captures the retardation of the droplet rising and coalescing due to the complex flow behaviour in the dense packed layer (DPL). The blending treatment provides a feasible compensation mechanism for the mesoscale uncertainties of droplet flow and coalescence through the DPL and its adjacent layers. In addition, the influence of the turbulent dispersion force is also investigated, which can improve the prediction of the radial distribution of concentrations but worsen the separation characteristics along the flow direction. Although the simulated concentration distribution and layer heights agree with the experiments only qualitatively, this work demonstrates how improvements in drag and coalescence modelling can be made to enhance the prediction accuracy.

Journal article

Fu J, Xiao D, Fu R, Li C, Zhu C, Arcucci R, Navon IMet al., 2023, Physics-data combined machine learning for parametric reduced-order modelling of nonlinear dynamical systems in small-data regimes, COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, Vol: 404, ISSN: 0045-7825

Journal article

Cheng S, Chen J, Anastasiou C, Angeli P, Matar OKK, Guo Y-K, Pain CCC, Arcucci Ret al., 2023, Generalised latent assimilation in heterogeneous reduced spaces with machine learning surrogate models, Journal of Scientific Computing, Vol: 94, Pages: 1-37, ISSN: 0885-7474

Reduced-order modelling and low-dimensional surrogate models generated using machine learning algorithms have been widely applied in high-dimensional dynamical systems to improve the algorithmic efficiency. In this paper, we develop a system which combines reduced-order surrogate models with a novel data assimilation (DA) technique used to incorporate real-time observations from different physical spaces. We make use of local smooth surrogate functions which link the space of encoded system variables and the one of current observations to perform variational DA with a low computational cost. The new system, named generalised latent assimilation can benefit both the efficiency provided by the reduced-order modelling and the accuracy of data assimilation. A theoretical analysis of the difference between surrogate and original assimilation cost function is also provided in this paper where an upper bound, depending on the size of the local training set, is given. The new approach is tested on a high-dimensional (CFD) application of a two-phase liquid flow with non-linear observation operators that current Latent Assimilation methods can not handle. Numerical results demonstrate that the proposed assimilation approach can significantly improve the reconstruction and prediction accuracy of the deep learning surrogate model which is nearly 1000 times faster than the CFD simulation.

Journal article

Liu C, Cheng S, Chen C, Qiao M, Zhang W, Shah A, Bai W, Arcucci Ret al., 2023, M-FLAG: Medical Vision-Language Pre-training with Frozen Language Models and Latent Space Geometry Optimization, Pages: 637-647, ISSN: 0302-9743

Medical vision-language models enable co-learning and integrating features from medical imaging and clinical text. However, these models are not easy to train and the latent representation space can be complex. Here we propose a novel way for pre-training and regularising medical vision-language models. The proposed method, named Medical vision-language pre-training with Frozen language models and Latent spAce Geometry optimization (M-FLAG), leverages a frozen language model for training stability and efficiency and introduces a novel orthogonality loss to harmonize the latent space geometry. We demonstrate the potential of the pre-trained model on three downstream tasks: medical image classification, segmentation, and object detection. Extensive experiments across five public datasets demonstrate that M-FLAG significantly outperforms existing medical vision-language pre-training approaches and reduces the number of parameters by 78%. Notably, M-FLAG achieves outstanding performance on the segmentation task while using only 1% of the RSNA dataset, even outperforming ImageNet pre-trained models that have been fine-tuned using 100% of the data. The code can be found in https://github.com/cheliu-computation/M-FLAG-MICCAI2023.

Conference paper

Fan H, Cheng S, de Nazelle AJ, Arcucci Ret al., 2023, An Efficient ViT-Based Spatial Interpolation Learner for Field Reconstruction, Computational Science – ICCS 2023, Publisher: Springer Nature Switzerland, Pages: 430-437, ISBN: 9783031360268

Book chapter

Lever J, Cheng S, Arcucci R, 2023, Human-Sensors & Physics Aware Machine Learning for Wildfire Detection and Nowcasting, Computational Science – ICCS 2023, Publisher: Springer Nature Switzerland, Pages: 422-429, ISBN: 9783031360268

Book chapter

Gong H, Cheng S, Chen Z, Li Q, Quilodran-Casas C, Xiao D, Arcucci Ret al., 2022, An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics, ANNALS OF NUCLEAR ENERGY, Vol: 179, ISSN: 0306-4549

Journal article

Chagot L, Quilodran-Casas C, Kalli M, Kovalchuk NM, Simmons MJH, Matar OK, Arcucci R, Angeli Pet al., 2022, Surfactant-laden droplet size prediction in a flow-focusing microchannel: a data-driven approach, LAB ON A CHIP, Vol: 22, Pages: 3848-3859, ISSN: 1473-0197

Journal article

Zhuang Y, Cheng S, Kovalchuk N, Simmons M, Matar OK, Guo Y-K, Arcucci Ret al., 2022, Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device, Lab on a Chip: miniaturisation for chemistry, physics, biology, materials science and bioengineering, Vol: 22, Pages: 3187-3202, ISSN: 1473-0189

A major challenge in the field of microfluidics is to predict and control drop interactions. This work develops an image-based data-driven model to forecast drop dynamics based on experiments performed on a microfluidics device. Reduced-order modelling techniques are applied to compress the recorded images into low-dimensional spaces and alleviate the computational cost. Recurrent neural networks are then employed to build a surrogate model of drop interactions by learning the dynamics of compressed variables in the reduced-order space. The surrogate model is integrated with real-time observations using data assimilation. In this paper we developed an ensemble-based latent assimilation algorithm scheme which shows an improvement in terms of accuracy with respect to the previous approaches. This work demonstrates the possibility to create a reliable data-driven model enabling a high fidelity prediction of drop interactions in microfluidics device. The performance of the developed system is evaluated against experimental data (i.e., recorded videos), which are excluded from the training of the surrogate model. The developed scheme is general and can be applied to other dynamical systems.

Journal article

Cheng S, Prentice IC, Huang Y, Jin Y, Guo Y-K, Arcucci Ret al., 2022, Data-driven surrogate model with latent data-assimilation: application to wildfire forecasting, Journal of Computational Physics, Vol: 464, ISSN: 0021-9991

The large and catastrophic wildfires have been increasing across the globe in the recent decade, highlighting the importance of simulating and forecasting fire dynamics in near real-time. This is extremely challenging due to the complexities of physical models and geographical features. Running physics-based simulations for large wildfire events in near real-time are computationally expensive, if not infeasible. In this work, we develop and test a novel data-model integration scheme for fire progression forecasting, that combines Reduced-order modelling, recurrent neural networks (Long-Short-Term Memory), data assimilation, and error covariance tuning. The Reduced-order modelling and the machine learning surrogate model ensure the efficiency of the proposed approach while the data assimilation enables the system to adjust the simulation with observations. We applied this algorithm to simulate and forecast three recent large wildfire events in California from 2017 to 2020. The deep-learning-based surrogate model runs around 1000 times faster than the Cellular Automata simulation which is used to generate training data-sets. The daily fire perimeters derived from satellite observation are used as observation data in Latent Assimilation to adjust the fire forecasting in near real-time. An error covariance tuning algorithm is also performed in the reduced space to estimate prior simulation and observation errors. The evolution of the averaged relative root mean square error (R-RMSE) shows that data assimilation and covariance tuning reduce the RMSE by about 50% and considerably improves the forecasting accuracy. As a first attempt at a reduced order wildfire spread forecasting, our exploratory work showed the potential of data-driven machine learning models to speed up fire forecasting for various applications.

Journal article

Lever J, Arcucci R, 2022, Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting, Journal of Computational Social Science, Vol: 5, Pages: 1427-1465, ISSN: 2432-2717

The intensity of wildfires and wildfire season length is increasing due to climate change, causing a greater threat to the local population. Much of this population are increasingly adopting social media, and sites like Twitter are increasingly being used as a real-time human-sensor network during natural disasters; detecting, tracking and documenting events. The human-sensor concept is currently largely omitted by wildfire models, representing a potential loss of information. By including Twitter data as a source in our models, we aim to help disaster managers make more informed, socially driven decisions, by detecting and monitoring online social media sentiment over the course of a wildfire event. This paper implements machine learning in a wildfire prediction model, using social media and geophysical data sources with Sentiment Analysis to predict wildfire characteristics with high accuracy. We also use wildfire-specific attributes to predict online social dynamics, as this has been shown to be indicative of localised disaster severity. This may be useful for disaster management teams in identifying areas of immediate danger. We combine geophysical satellite data from the Global Fire Atlas with social data provided by Twitter. We perform data collection and subsequent analysis & visualisation, and compare regional differences in online social sentiment expression. Following this, we compare and contrast different machine learning models for predicting wildfire attributes. We demonstrate social media is a predictor of wildfire activity, and present models which accurately model wildfire attributes. This work develops the concept of the human sensor in the context of wildfires, using users’ Tweets as noisy subjective sentimental accounts of current localised conditions. This work contributes to the development of more socially conscious wildfire models, by incorporating social media data into wildfire prediction and modelling.

Journal article

Cheng S, Jin Y, Harrison S, QuilodrĂ¡n Casas C, Prentice C, Guo Y-K, Arcucci Ret al., 2022, Parameter flexible wildfire prediction using machine learning techniques: forward and inverse modelling, Remote Sensing, Vol: 14, ISSN: 2072-4292

Parameter identification for wildfire forecasting models often relies on case-by-case tuning or posterior diagnosis/analysis, which can be computationally expensive due to the complexity of the forward prediction model. In this paper, we introduce an efficient parameter flexible fire prediction algorithm based on machine learning and reduced order modelling techniques. Using a training dataset generated by physics-based fire simulations, the method forecasts burned area at different time steps with a low computational cost. We then address the bottleneck of efficient parameter estimation by developing a novel inverse approach relying on data assimilation techniques (latent assimilation) in the reduced order space. The forward and the inverse modellings are tested on two recent large wildfire events in California. Satellite observations are used to validate the forward prediction approach and identify the model parameters. By combining these forward and inverse approaches, the system manages to integrate real-time observations for parameter adjustment, leading to more accurate future predictions.

Journal article

Lever J, Arcucci R, 2022, Towards Social Machine Learning for Natural Disasters, Computational Science – ICCS 2022 22nd International Conference, London, UK, June 21–23, 2022, Proceedings, Part III, Publisher: Springer, Pages: 756-769, ISBN: 9783031087561

The four-volume set LNCS 13350, 13351, 13352, and 13353 constitutes the proceedings of the 22ndt International Conference on Computational Science, ICCS 2022, held in London, UK, in June 2022.* The total of 175 full papers and 78 short ...

Book chapter

Schneider R, Bonavita M, Geer A, Arcucci R, Dueben P, Vitolo C, Le Saux B, Demir B, Mathieu P-Pet al., 2022, ESA-ECMWF Report on recent progress and research directions in machine learning for Earth System observation and prediction, NPJ CLIMATE AND ATMOSPHERIC SCIENCE, Vol: 5, ISSN: 2397-3722

Journal article

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