Geoscientific Instrumentation, Methods and Data Systems
Geoscientific Instrumentation, Methods and Data Systems
GI
Articles | Volume 10, issue 2
Articles | Volume 10, issue 2
https://doi.org/10.5194/gi-10-265-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gi-10-265-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 2
Research article
 | 
03 Nov 2021
Research article |  | 03 Nov 2021

Evaluation of multivariate time series clustering for imputation of air pollution data

Wedad Alahamade, Iain Lake, Claire E. Reeves, and Beatriz De La Iglesia

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Cited articles

Alahamade, W.: Wedad-O-A/Modelled-concentrations-: Modelled_Concentration_Air_Qaulity (v3.5.2), Zenodo [code and data set], https://doi.org/10.5281/zenodo.5602618, 2021. a
Alahamade, W., Lake, I., Reeves, C. E., and De La Iglesia, B.: Clustering Imputation for Air Pollution Data, in: International Conference on Hybrid Artificial Intelligence Systems, Lecture Notes in Computer Science, 585–597, https://doi.org/10.1007/978-3-030-61705-9_48, Springer, Cham, 2020. a, b
Alahamade, W., Lake, I., Reeves, C. E., and De La Iglesia, B.: A Multi-variate Time Series clustering approach based on Intermediate Fusion A case study in air pollution data imputation, Neurocomputing, in press, 2021. a, b, c
Austin, E., Coull, B. A., Zanobetti, A., and Koutrakis, P.: A framework to spatially cluster air pollution monitoring sites in US based on the PM2.5 composition, Environ. Int., 59, 244–254, 2013. a, b
Carbajal-Hernández, J. J., Sánchez-Fernández, L. P., Carrasco-Ochoa, J. A., and Martínez-Trinidad, J. F.: Assessment and prediction of air quality using fuzzy logic and autoregressive models, Atmos. Environ., 60, 37–50, 2012. a
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The goal is to reduce the uncertainty in air quality assessment by imputing all missing pollutants in monitoring stations and identify where more measurements can be beneficial. The proposed approach is based on spatial or temporal similarity between stations. Our proposed approach enables us to impute and estimate plausible concentrations of multiple pollutants at stations across the UK, and the modelled concentrations from the selected models correlated well with the observed concentrations.
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