Hierarchical Bayesian Spatio-Temporal Modeling for PM10 Prediction
Over the past few years, hierarchical Bayesian models have been extensively used for modeling the joint spatial and temporal dependence of big spatio-temporal data which commonly involves a large number of missing observations. This article represented, assessed, and compared some recently proposed...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2021/8003952 |
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| _version_ | 1841524313529778176 |
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| author | Esam Mahdi Sana Alshamari Maryam Khashabi Alya Alkorbi |
| author_facet | Esam Mahdi Sana Alshamari Maryam Khashabi Alya Alkorbi |
| author_sort | Esam Mahdi |
| collection | DOAJ |
| description | Over the past few years, hierarchical Bayesian models have been extensively used for modeling the joint spatial and temporal dependence of big spatio-temporal data which commonly involves a large number of missing observations. This article represented, assessed, and compared some recently proposed Bayesian and non-Bayesian models for predicting the daily average particulate matter with a diameter of less than 10 (PM10) measured in Qatar during the years 2016–2019. The disaggregating technique with a Markov chain Monte Carlo method with Gibbs sampler are used to handle the missing data. Based on the obtained results, we conclude that the Gaussian predictive processes with autoregressive terms of the latent underlying space-time process model is the best, compared with the Bayesian Gaussian processes and non-Bayesian generalized additive models. |
| format | Article |
| id | doaj-art-ecd9b912706d4be192daad5df1767a8f |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-ecd9b912706d4be192daad5df1767a8f2025-02-03T07:23:27ZengWileyJournal of Applied Mathematics1110-757X1687-00422021-01-01202110.1155/2021/80039528003952Hierarchical Bayesian Spatio-Temporal Modeling for PM10 PredictionEsam Mahdi0Sana Alshamari1Maryam Khashabi2Alya Alkorbi3Department of Mathematics, Statistics and Physics, Qatar University, Doha, QatarDepartment of Mathematics, Statistics and Physics, Qatar University, Doha, QatarDepartment of Mathematics, Statistics and Physics, Qatar University, Doha, QatarDepartment of Mathematics, Statistics and Physics, Qatar University, Doha, QatarOver the past few years, hierarchical Bayesian models have been extensively used for modeling the joint spatial and temporal dependence of big spatio-temporal data which commonly involves a large number of missing observations. This article represented, assessed, and compared some recently proposed Bayesian and non-Bayesian models for predicting the daily average particulate matter with a diameter of less than 10 (PM10) measured in Qatar during the years 2016–2019. The disaggregating technique with a Markov chain Monte Carlo method with Gibbs sampler are used to handle the missing data. Based on the obtained results, we conclude that the Gaussian predictive processes with autoregressive terms of the latent underlying space-time process model is the best, compared with the Bayesian Gaussian processes and non-Bayesian generalized additive models.http://dx.doi.org/10.1155/2021/8003952 |
| spellingShingle | Esam Mahdi Sana Alshamari Maryam Khashabi Alya Alkorbi Hierarchical Bayesian Spatio-Temporal Modeling for PM10 Prediction Journal of Applied Mathematics |
| title | Hierarchical Bayesian Spatio-Temporal Modeling for PM10 Prediction |
| title_full | Hierarchical Bayesian Spatio-Temporal Modeling for PM10 Prediction |
| title_fullStr | Hierarchical Bayesian Spatio-Temporal Modeling for PM10 Prediction |
| title_full_unstemmed | Hierarchical Bayesian Spatio-Temporal Modeling for PM10 Prediction |
| title_short | Hierarchical Bayesian Spatio-Temporal Modeling for PM10 Prediction |
| title_sort | hierarchical bayesian spatio temporal modeling for pm10 prediction |
| url | http://dx.doi.org/10.1155/2021/8003952 |
| work_keys_str_mv | AT esammahdi hierarchicalbayesianspatiotemporalmodelingforpm10prediction AT sanaalshamari hierarchicalbayesianspatiotemporalmodelingforpm10prediction AT maryamkhashabi hierarchicalbayesianspatiotemporalmodelingforpm10prediction AT alyaalkorbi hierarchicalbayesianspatiotemporalmodelingforpm10prediction |