Observability for optimal sensor locations in data assimilation

King, Sarah; Kang, Wei; Xu, Liang

doi:10.1007/s40435-014-0120-7

Cited by 13 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our results suggest that while a high observation density is essential for improving the performance of a model with data assimilation, it is crucial to consider other factors such as the quality of the data and the location of the sensors. Different techniques of observation localization allow optimizing the number of sensors to improve the data assimilation or other data fusion techniques [41][42][43][44]. We highly recommend implementing these techniques in the development of new low-cost monitoring networks.…”

Section: Discussionmentioning

confidence: 99%

Urban Air Quality Modeling Using Low-Cost Sensor Network and Data Assimilation in the Aburrá Valley, Colombia

et al. 2021

View full text Add to dashboard Cite

The use of low air quality networks has been increasing in recent years to study urban pollution dynamics. Here we show the evaluation of the operational Aburrá Valley’s low-cost network against the official monitoring network. The results show that the PM2.5 low-cost measurements are very close to those observed by the official network. Additionally, the low-cost allows a higher spatial representation of the concentrations across the valley. We integrate low-cost observations with the chemical transport model Long Term Ozone Simulation-European Operational Smog (LOTOS-EUROS) using data assimilation. Two different configurations of the low-cost network were assimilated: using the whole low-cost network (255 sensors), and a high-quality selection using just the sensors with a correlation factor greater than 0.8 with respect to the official network (115 sensors). The official stations were also assimilated to compare the more dense low-cost network’s impact on the model performance. Both simulations assimilating the low-cost model outperform the model without assimilation and assimilating the official network. The capability to issue warnings for pollution events is also improved by assimilating the low-cost network with respect to the other simulations. Finally, the simulation using the high-quality configuration has lower error values than using the complete low-cost network, showing that it is essential to consider the quality and location and not just the total number of sensors. Our results suggest that with the current advance in low-cost sensors, it is possible to improve model performance with low-cost network data assimilation.

show abstract

Section: Discussionmentioning

confidence: 99%

Urban Air Quality Modeling Using Low-Cost Sensor Network and Data Assimilation in the Aburrá Valley, Colombia

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Our results suggested that while a high observation density is essential for improving the performance of a model with data assimilation, it is crucial to consider other factors such as quality of the data and the location of the sensors. Different techniques of observation localization allow optimizing the number of sensors to improve the data assimilation or other data fusion techniques (Alexanderian, Petra, Stadler and Ghattas, 2016;King, Kang and Xu, 2015;Mazzoleni, Alfonso and Solomatine, 2017;Yildirim, Chryssostomidis and Karniadakis, 2009). We highly recommend implementing these techniques in the development of a new low-cost network.…”

Section: Discussion and Commentsmentioning

confidence: 99%

Urban Air Quality Modeling Using Low-Cost Sensor Network and Data Assimilation in the Aburra Valley, Colombia

Lopez-Restrepo¹,

Botero²,

Pinel³

et al. 2020

Preprint

View full text Add to dashboard Cite

The use of low air quality networks has been increasing in recent years to study urban pollution dynamics. Here we show the evaluation of the operational Aburrá Valley's low-cost network against the official monitoring network. The results show that the PM 2.5 low-cost measurements are very close to those observed by the official network. Additionally, the low-cost allows a higher spatial representation of the concentrations across the valley. We integrate low-cost observations with the chemical transport model LOTOS-EUROS using data assimilation. Two different configurations of the low-cost network were assimilated: using the whole low-cost network (255 sensors), and a high-quality using just the sensors with a correlation factor greater than 0.8 with respect to the official network (115 sensors). The official stations were also assimilated to compare the more dense low-cost network's impact on the model performance. Both simulations assimilating the low-cost model outperform the model without assimilation and assimilating the official network. The model capability to predict high concentration events' warnings is also improved by assimilating the low-cost network with respect to the other simulations. Finally, the simulation using the high-quality configuration has lower error values than using the complete low-cost network, showing that it is essential to consider the quality and location and not just the total number of sensors. Our results suggest that with the current advance in low-cost sensors, it is possible to improve model performance with low-cost network data assimilation.

show abstract

“…. The empirical observability Gramian is used in model reduction [2], [3] for nonlinear systems, and sensor placement problem [13]- [15].…”

Section: B Empirical Observability Gramianmentioning

confidence: 99%

Observability Gramian for Bayesian Inference in Nonlinear Systems With Its Industrial Application

Kunwoo

Umezu

Konno

et al. 2023

IEEE Control Syst. Lett.

View full text Add to dashboard Cite

In this paper, we present a novel (empirical) observability Gramian for nonlinear stochastic systems in the light of Bayesian inference. First, we define our observability Gramian, which we refer to as the estimability Gramian, based on the relation to the so-called Bayesian Fisher Information Matrix for initial state estimation. Then, we study the fundamental properties of an empirical version of the estimability Gramian. The practical usefulness of the proposed framework is examined through its application to a parameter and initial state estimation in a natural gas engine cylinder.

show abstract

Observability for optimal sensor locations in data assimilation

Cited by 13 publications

References 14 publications

Urban Air Quality Modeling Using Low-Cost Sensor Network and Data Assimilation in the Aburrá Valley, Colombia

Urban Air Quality Modeling Using Low-Cost Sensor Network and Data Assimilation in the Aburrá Valley, Colombia

Urban Air Quality Modeling Using Low-Cost Sensor Network and Data Assimilation in the Aburra Valley, Colombia

Observability Gramian for Bayesian Inference in Nonlinear Systems With Its Industrial Application

Contact Info

Product

Resources

About