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Delegated Regressor, A Robust Approach for Automated Anomaly Detection in the Soil Radon Time Series Data
We propose a new method based on the idea of delegating regressors for predicting the soil radon gas concentration (SRGC) and anomalies in radon or any other time series data. The proposed method is compared to different traditional boosting e.g., Extreme Gradient Boosting (EGB) and simple regressio...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7033208/ https://www.ncbi.nlm.nih.gov/pubmed/32080258 http://dx.doi.org/10.1038/s41598-020-59881-9 |
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author | Rafique, Muhammad Tareen, Aleem Dad Khan Mir, Adil Aslim Nadeem, Malik Sajjad Ahmed Asim, Khawaja M. Kearfott, Kimberlee Jane |
author_facet | Rafique, Muhammad Tareen, Aleem Dad Khan Mir, Adil Aslim Nadeem, Malik Sajjad Ahmed Asim, Khawaja M. Kearfott, Kimberlee Jane |
author_sort | Rafique, Muhammad |
collection | PubMed |
description | We propose a new method based on the idea of delegating regressors for predicting the soil radon gas concentration (SRGC) and anomalies in radon or any other time series data. The proposed method is compared to different traditional boosting e.g., Extreme Gradient Boosting (EGB) and simple regression methods e.g., support vector regressors with linear kernel and radial kernel in terms of accurate predictions. R language has been used for the statistical analysis of radon time series (RTS) data. The results obtained show that the proposed methodology predicts SRGC more accurately when compared to different traditional boosting and regression methods. The best correlation is found between the actual and predicted radon concentration for window size of 2 i.e., two days before and after the start of seismic activities. RTS data was collected from 05 February 2017 to 16 February 2018, including 7 seismic events recorded during the study period. Findings of study show that the proposed methodology predicts the SRGC with more precision, for all the window sizes, by overlapping predicted with the actual radon time series concentrations. |
format | Online Article Text |
id | pubmed-7033208 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-70332082020-02-28 Delegated Regressor, A Robust Approach for Automated Anomaly Detection in the Soil Radon Time Series Data Rafique, Muhammad Tareen, Aleem Dad Khan Mir, Adil Aslim Nadeem, Malik Sajjad Ahmed Asim, Khawaja M. Kearfott, Kimberlee Jane Sci Rep Article We propose a new method based on the idea of delegating regressors for predicting the soil radon gas concentration (SRGC) and anomalies in radon or any other time series data. The proposed method is compared to different traditional boosting e.g., Extreme Gradient Boosting (EGB) and simple regression methods e.g., support vector regressors with linear kernel and radial kernel in terms of accurate predictions. R language has been used for the statistical analysis of radon time series (RTS) data. The results obtained show that the proposed methodology predicts SRGC more accurately when compared to different traditional boosting and regression methods. The best correlation is found between the actual and predicted radon concentration for window size of 2 i.e., two days before and after the start of seismic activities. RTS data was collected from 05 February 2017 to 16 February 2018, including 7 seismic events recorded during the study period. Findings of study show that the proposed methodology predicts the SRGC with more precision, for all the window sizes, by overlapping predicted with the actual radon time series concentrations. Nature Publishing Group UK 2020-02-20 /pmc/articles/PMC7033208/ /pubmed/32080258 http://dx.doi.org/10.1038/s41598-020-59881-9 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Rafique, Muhammad Tareen, Aleem Dad Khan Mir, Adil Aslim Nadeem, Malik Sajjad Ahmed Asim, Khawaja M. Kearfott, Kimberlee Jane Delegated Regressor, A Robust Approach for Automated Anomaly Detection in the Soil Radon Time Series Data |
title | Delegated Regressor, A Robust Approach for Automated Anomaly Detection in the Soil Radon Time Series Data |
title_full | Delegated Regressor, A Robust Approach for Automated Anomaly Detection in the Soil Radon Time Series Data |
title_fullStr | Delegated Regressor, A Robust Approach for Automated Anomaly Detection in the Soil Radon Time Series Data |
title_full_unstemmed | Delegated Regressor, A Robust Approach for Automated Anomaly Detection in the Soil Radon Time Series Data |
title_short | Delegated Regressor, A Robust Approach for Automated Anomaly Detection in the Soil Radon Time Series Data |
title_sort | delegated regressor, a robust approach for automated anomaly detection in the soil radon time series data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7033208/ https://www.ncbi.nlm.nih.gov/pubmed/32080258 http://dx.doi.org/10.1038/s41598-020-59881-9 |
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