Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function
Abstract To minimize catastrophic impacts of earthquakes, many researchers have directed their efforts to developing novel computational techniques and models that forecast natural disasters based upon earthquake catalogues, anomalies in the ionosphere, and radon time series databases, etc. In this...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mir, Adil Aslam [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2021 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Pure and applied geophysics - Springer International Publishing, 1964, 178(2021), 5 vom: Mai, Seite 1593-1607 |
|---|---|
| Übergeordnetes Werk: |
volume:178 ; year:2021 ; number:5 ; month:05 ; pages:1593-1607 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00024-021-02736-9 |
|---|
| Katalog-ID: |
OLC2126473120 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2126473120 | ||
| 003 | DE-627 | ||
| 005 | 20230505115217.0 | ||
| 007 | tu | ||
| 008 | 230505s2021 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00024-021-02736-9 |2 doi | |
| 035 | |a (DE-627)OLC2126473120 | ||
| 035 | |a (DE-He213)s00024-021-02736-9-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 550 |q VZ |
| 082 | 0 | 4 | |a 550 |q VZ |
| 084 | |a 16,13 |2 ssgn | ||
| 100 | 1 | |a Mir, Adil Aslam |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021 | ||
| 520 | |a Abstract To minimize catastrophic impacts of earthquakes, many researchers have directed their efforts to developing novel computational techniques and models that forecast natural disasters based upon earthquake catalogues, anomalies in the ionosphere, and radon time series databases, etc. In this article, we propose a methodology that categorizes the soil radon gas (SRG) concentration into seismically active and non-active time series by taking the advantage of a stacking ensemble approach and utilizing an automatic anomaly indication function as a post-processing technique. The main idea behind the proposed methodology is of two layers. The first layer uses a stacking ensemble-based approach that incorporates three learners, i.e. a generalized linear model, linear regression and K-nearest neighbors, to train on seismically active and inactive periods to predict SRG concentration. These predictions are then combined with the labeled anomaly data to train a meta-learner, i.e., support vector machine with a radial kernel, that categorizes the series into active and non-active radon time series data. In the second layer, these classifications are then passed to an automatic anomaly indication function that further labels the time series in a group of readings where the level of received indications is greater than or equal to the indication factor. Radon time series (RTS) data has been recorded over a fault line present in Muzaffarabad, a city in the Pakistan territory of Kashmir, ranging from 1 March 2017 to 28 February 2018. Four seismic events occurred during the study period. The conclusion of the study reveals that the proposed methodology accurately locates the anomaly in the RTS data at different window sizes, i.e. with respect to individual days. The evaluation is based purely on the classification after processing of RTS data by both layers of the proposed methodology. | ||
| 650 | 4 | |a Computational techniques | |
| 650 | 4 | |a radon time series | |
| 650 | 4 | |a stacking ensemble | |
| 650 | 4 | |a post-processing technique | |
| 700 | 1 | |a Çelebi, Fatih Vehbi |4 aut | |
| 700 | 1 | |a Rafique, Muhammad |0 (orcid)0000-0002-5216-9380 |4 aut | |
| 700 | 1 | |a Faruque, M. R. I. |4 aut | |
| 700 | 1 | |a Khandaker, Mayeen Uddin |4 aut | |
| 700 | 1 | |a Kearfott, Kimberlee Jane |4 aut | |
| 700 | 1 | |a Ahmad, Pervaiz |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Pure and applied geophysics |d Springer International Publishing, 1964 |g 178(2021), 5 vom: Mai, Seite 1593-1607 |w (DE-627)129538353 |w (DE-600)216719-0 |w (DE-576)014971038 |x 0033-4553 |7 nnns |
| 773 | 1 | 8 | |g volume:178 |g year:2021 |g number:5 |g month:05 |g pages:1593-1607 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s00024-021-02736-9 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-PHY | ||
| 912 | |a SSG-OLC-GEO | ||
| 912 | |a SSG-OPC-GGO | ||
| 912 | |a SSG-OPC-GEO | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_601 | ||
| 951 | |a AR | ||
| 952 | |d 178 |j 2021 |e 5 |c 05 |h 1593-1607 | ||
| author_variant |
a a m aa aam f v ç fv fvç m r mr m r i f mri mrif m u k mu muk k j k kj kjk p a pa |
|---|---|
| matchkey_str |
article:00334553:2021----::nmlcasfctofratqaerdcinnaotmsredtuigtcignat |
| hierarchy_sort_str |
2021 |
| publishDate |
2021 |
| allfields |
10.1007/s00024-021-02736-9 doi (DE-627)OLC2126473120 (DE-He213)s00024-021-02736-9-p DE-627 ger DE-627 rakwb eng 550 VZ 550 VZ 16,13 ssgn Mir, Adil Aslam verfasserin aut Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021 Abstract To minimize catastrophic impacts of earthquakes, many researchers have directed their efforts to developing novel computational techniques and models that forecast natural disasters based upon earthquake catalogues, anomalies in the ionosphere, and radon time series databases, etc. In this article, we propose a methodology that categorizes the soil radon gas (SRG) concentration into seismically active and non-active time series by taking the advantage of a stacking ensemble approach and utilizing an automatic anomaly indication function as a post-processing technique. The main idea behind the proposed methodology is of two layers. The first layer uses a stacking ensemble-based approach that incorporates three learners, i.e. a generalized linear model, linear regression and K-nearest neighbors, to train on seismically active and inactive periods to predict SRG concentration. These predictions are then combined with the labeled anomaly data to train a meta-learner, i.e., support vector machine with a radial kernel, that categorizes the series into active and non-active radon time series data. In the second layer, these classifications are then passed to an automatic anomaly indication function that further labels the time series in a group of readings where the level of received indications is greater than or equal to the indication factor. Radon time series (RTS) data has been recorded over a fault line present in Muzaffarabad, a city in the Pakistan territory of Kashmir, ranging from 1 March 2017 to 28 February 2018. Four seismic events occurred during the study period. The conclusion of the study reveals that the proposed methodology accurately locates the anomaly in the RTS data at different window sizes, i.e. with respect to individual days. The evaluation is based purely on the classification after processing of RTS data by both layers of the proposed methodology. Computational techniques radon time series stacking ensemble post-processing technique Çelebi, Fatih Vehbi aut Rafique, Muhammad (orcid)0000-0002-5216-9380 aut Faruque, M. R. I. aut Khandaker, Mayeen Uddin aut Kearfott, Kimberlee Jane aut Ahmad, Pervaiz aut Enthalten in Pure and applied geophysics Springer International Publishing, 1964 178(2021), 5 vom: Mai, Seite 1593-1607 (DE-627)129538353 (DE-600)216719-0 (DE-576)014971038 0033-4553 nnns volume:178 year:2021 number:5 month:05 pages:1593-1607 https://doi.org/10.1007/s00024-021-02736-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 GBV_ILN_601 AR 178 2021 5 05 1593-1607 |
| spelling |
10.1007/s00024-021-02736-9 doi (DE-627)OLC2126473120 (DE-He213)s00024-021-02736-9-p DE-627 ger DE-627 rakwb eng 550 VZ 550 VZ 16,13 ssgn Mir, Adil Aslam verfasserin aut Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021 Abstract To minimize catastrophic impacts of earthquakes, many researchers have directed their efforts to developing novel computational techniques and models that forecast natural disasters based upon earthquake catalogues, anomalies in the ionosphere, and radon time series databases, etc. In this article, we propose a methodology that categorizes the soil radon gas (SRG) concentration into seismically active and non-active time series by taking the advantage of a stacking ensemble approach and utilizing an automatic anomaly indication function as a post-processing technique. The main idea behind the proposed methodology is of two layers. The first layer uses a stacking ensemble-based approach that incorporates three learners, i.e. a generalized linear model, linear regression and K-nearest neighbors, to train on seismically active and inactive periods to predict SRG concentration. These predictions are then combined with the labeled anomaly data to train a meta-learner, i.e., support vector machine with a radial kernel, that categorizes the series into active and non-active radon time series data. In the second layer, these classifications are then passed to an automatic anomaly indication function that further labels the time series in a group of readings where the level of received indications is greater than or equal to the indication factor. Radon time series (RTS) data has been recorded over a fault line present in Muzaffarabad, a city in the Pakistan territory of Kashmir, ranging from 1 March 2017 to 28 February 2018. Four seismic events occurred during the study period. The conclusion of the study reveals that the proposed methodology accurately locates the anomaly in the RTS data at different window sizes, i.e. with respect to individual days. The evaluation is based purely on the classification after processing of RTS data by both layers of the proposed methodology. Computational techniques radon time series stacking ensemble post-processing technique Çelebi, Fatih Vehbi aut Rafique, Muhammad (orcid)0000-0002-5216-9380 aut Faruque, M. R. I. aut Khandaker, Mayeen Uddin aut Kearfott, Kimberlee Jane aut Ahmad, Pervaiz aut Enthalten in Pure and applied geophysics Springer International Publishing, 1964 178(2021), 5 vom: Mai, Seite 1593-1607 (DE-627)129538353 (DE-600)216719-0 (DE-576)014971038 0033-4553 nnns volume:178 year:2021 number:5 month:05 pages:1593-1607 https://doi.org/10.1007/s00024-021-02736-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 GBV_ILN_601 AR 178 2021 5 05 1593-1607 |
| allfields_unstemmed |
10.1007/s00024-021-02736-9 doi (DE-627)OLC2126473120 (DE-He213)s00024-021-02736-9-p DE-627 ger DE-627 rakwb eng 550 VZ 550 VZ 16,13 ssgn Mir, Adil Aslam verfasserin aut Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021 Abstract To minimize catastrophic impacts of earthquakes, many researchers have directed their efforts to developing novel computational techniques and models that forecast natural disasters based upon earthquake catalogues, anomalies in the ionosphere, and radon time series databases, etc. In this article, we propose a methodology that categorizes the soil radon gas (SRG) concentration into seismically active and non-active time series by taking the advantage of a stacking ensemble approach and utilizing an automatic anomaly indication function as a post-processing technique. The main idea behind the proposed methodology is of two layers. The first layer uses a stacking ensemble-based approach that incorporates three learners, i.e. a generalized linear model, linear regression and K-nearest neighbors, to train on seismically active and inactive periods to predict SRG concentration. These predictions are then combined with the labeled anomaly data to train a meta-learner, i.e., support vector machine with a radial kernel, that categorizes the series into active and non-active radon time series data. In the second layer, these classifications are then passed to an automatic anomaly indication function that further labels the time series in a group of readings where the level of received indications is greater than or equal to the indication factor. Radon time series (RTS) data has been recorded over a fault line present in Muzaffarabad, a city in the Pakistan territory of Kashmir, ranging from 1 March 2017 to 28 February 2018. Four seismic events occurred during the study period. The conclusion of the study reveals that the proposed methodology accurately locates the anomaly in the RTS data at different window sizes, i.e. with respect to individual days. The evaluation is based purely on the classification after processing of RTS data by both layers of the proposed methodology. Computational techniques radon time series stacking ensemble post-processing technique Çelebi, Fatih Vehbi aut Rafique, Muhammad (orcid)0000-0002-5216-9380 aut Faruque, M. R. I. aut Khandaker, Mayeen Uddin aut Kearfott, Kimberlee Jane aut Ahmad, Pervaiz aut Enthalten in Pure and applied geophysics Springer International Publishing, 1964 178(2021), 5 vom: Mai, Seite 1593-1607 (DE-627)129538353 (DE-600)216719-0 (DE-576)014971038 0033-4553 nnns volume:178 year:2021 number:5 month:05 pages:1593-1607 https://doi.org/10.1007/s00024-021-02736-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 GBV_ILN_601 AR 178 2021 5 05 1593-1607 |
| allfieldsGer |
10.1007/s00024-021-02736-9 doi (DE-627)OLC2126473120 (DE-He213)s00024-021-02736-9-p DE-627 ger DE-627 rakwb eng 550 VZ 550 VZ 16,13 ssgn Mir, Adil Aslam verfasserin aut Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021 Abstract To minimize catastrophic impacts of earthquakes, many researchers have directed their efforts to developing novel computational techniques and models that forecast natural disasters based upon earthquake catalogues, anomalies in the ionosphere, and radon time series databases, etc. In this article, we propose a methodology that categorizes the soil radon gas (SRG) concentration into seismically active and non-active time series by taking the advantage of a stacking ensemble approach and utilizing an automatic anomaly indication function as a post-processing technique. The main idea behind the proposed methodology is of two layers. The first layer uses a stacking ensemble-based approach that incorporates three learners, i.e. a generalized linear model, linear regression and K-nearest neighbors, to train on seismically active and inactive periods to predict SRG concentration. These predictions are then combined with the labeled anomaly data to train a meta-learner, i.e., support vector machine with a radial kernel, that categorizes the series into active and non-active radon time series data. In the second layer, these classifications are then passed to an automatic anomaly indication function that further labels the time series in a group of readings where the level of received indications is greater than or equal to the indication factor. Radon time series (RTS) data has been recorded over a fault line present in Muzaffarabad, a city in the Pakistan territory of Kashmir, ranging from 1 March 2017 to 28 February 2018. Four seismic events occurred during the study period. The conclusion of the study reveals that the proposed methodology accurately locates the anomaly in the RTS data at different window sizes, i.e. with respect to individual days. The evaluation is based purely on the classification after processing of RTS data by both layers of the proposed methodology. Computational techniques radon time series stacking ensemble post-processing technique Çelebi, Fatih Vehbi aut Rafique, Muhammad (orcid)0000-0002-5216-9380 aut Faruque, M. R. I. aut Khandaker, Mayeen Uddin aut Kearfott, Kimberlee Jane aut Ahmad, Pervaiz aut Enthalten in Pure and applied geophysics Springer International Publishing, 1964 178(2021), 5 vom: Mai, Seite 1593-1607 (DE-627)129538353 (DE-600)216719-0 (DE-576)014971038 0033-4553 nnns volume:178 year:2021 number:5 month:05 pages:1593-1607 https://doi.org/10.1007/s00024-021-02736-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 GBV_ILN_601 AR 178 2021 5 05 1593-1607 |
| allfieldsSound |
10.1007/s00024-021-02736-9 doi (DE-627)OLC2126473120 (DE-He213)s00024-021-02736-9-p DE-627 ger DE-627 rakwb eng 550 VZ 550 VZ 16,13 ssgn Mir, Adil Aslam verfasserin aut Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021 Abstract To minimize catastrophic impacts of earthquakes, many researchers have directed their efforts to developing novel computational techniques and models that forecast natural disasters based upon earthquake catalogues, anomalies in the ionosphere, and radon time series databases, etc. In this article, we propose a methodology that categorizes the soil radon gas (SRG) concentration into seismically active and non-active time series by taking the advantage of a stacking ensemble approach and utilizing an automatic anomaly indication function as a post-processing technique. The main idea behind the proposed methodology is of two layers. The first layer uses a stacking ensemble-based approach that incorporates three learners, i.e. a generalized linear model, linear regression and K-nearest neighbors, to train on seismically active and inactive periods to predict SRG concentration. These predictions are then combined with the labeled anomaly data to train a meta-learner, i.e., support vector machine with a radial kernel, that categorizes the series into active and non-active radon time series data. In the second layer, these classifications are then passed to an automatic anomaly indication function that further labels the time series in a group of readings where the level of received indications is greater than or equal to the indication factor. Radon time series (RTS) data has been recorded over a fault line present in Muzaffarabad, a city in the Pakistan territory of Kashmir, ranging from 1 March 2017 to 28 February 2018. Four seismic events occurred during the study period. The conclusion of the study reveals that the proposed methodology accurately locates the anomaly in the RTS data at different window sizes, i.e. with respect to individual days. The evaluation is based purely on the classification after processing of RTS data by both layers of the proposed methodology. Computational techniques radon time series stacking ensemble post-processing technique Çelebi, Fatih Vehbi aut Rafique, Muhammad (orcid)0000-0002-5216-9380 aut Faruque, M. R. I. aut Khandaker, Mayeen Uddin aut Kearfott, Kimberlee Jane aut Ahmad, Pervaiz aut Enthalten in Pure and applied geophysics Springer International Publishing, 1964 178(2021), 5 vom: Mai, Seite 1593-1607 (DE-627)129538353 (DE-600)216719-0 (DE-576)014971038 0033-4553 nnns volume:178 year:2021 number:5 month:05 pages:1593-1607 https://doi.org/10.1007/s00024-021-02736-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 GBV_ILN_601 AR 178 2021 5 05 1593-1607 |
| language |
English |
| source |
Enthalten in Pure and applied geophysics 178(2021), 5 vom: Mai, Seite 1593-1607 volume:178 year:2021 number:5 month:05 pages:1593-1607 |
| sourceStr |
Enthalten in Pure and applied geophysics 178(2021), 5 vom: Mai, Seite 1593-1607 volume:178 year:2021 number:5 month:05 pages:1593-1607 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Computational techniques radon time series stacking ensemble post-processing technique |
| dewey-raw |
550 |
| isfreeaccess_bool |
false |
| container_title |
Pure and applied geophysics |
| authorswithroles_txt_mv |
Mir, Adil Aslam @@aut@@ Çelebi, Fatih Vehbi @@aut@@ Rafique, Muhammad @@aut@@ Faruque, M. R. I. @@aut@@ Khandaker, Mayeen Uddin @@aut@@ Kearfott, Kimberlee Jane @@aut@@ Ahmad, Pervaiz @@aut@@ |
| publishDateDaySort_date |
2021-05-01T00:00:00Z |
| hierarchy_top_id |
129538353 |
| dewey-sort |
3550 |
| id |
OLC2126473120 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2126473120</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505115217.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230505s2021 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00024-021-02736-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2126473120</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00024-021-02736-9-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">16,13</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Mir, Adil Aslam</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract To minimize catastrophic impacts of earthquakes, many researchers have directed their efforts to developing novel computational techniques and models that forecast natural disasters based upon earthquake catalogues, anomalies in the ionosphere, and radon time series databases, etc. In this article, we propose a methodology that categorizes the soil radon gas (SRG) concentration into seismically active and non-active time series by taking the advantage of a stacking ensemble approach and utilizing an automatic anomaly indication function as a post-processing technique. The main idea behind the proposed methodology is of two layers. The first layer uses a stacking ensemble-based approach that incorporates three learners, i.e. a generalized linear model, linear regression and K-nearest neighbors, to train on seismically active and inactive periods to predict SRG concentration. These predictions are then combined with the labeled anomaly data to train a meta-learner, i.e., support vector machine with a radial kernel, that categorizes the series into active and non-active radon time series data. In the second layer, these classifications are then passed to an automatic anomaly indication function that further labels the time series in a group of readings where the level of received indications is greater than or equal to the indication factor. Radon time series (RTS) data has been recorded over a fault line present in Muzaffarabad, a city in the Pakistan territory of Kashmir, ranging from 1 March 2017 to 28 February 2018. Four seismic events occurred during the study period. The conclusion of the study reveals that the proposed methodology accurately locates the anomaly in the RTS data at different window sizes, i.e. with respect to individual days. The evaluation is based purely on the classification after processing of RTS data by both layers of the proposed methodology.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computational techniques</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">radon time series</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">stacking ensemble</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">post-processing technique</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Çelebi, Fatih Vehbi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rafique, Muhammad</subfield><subfield code="0">(orcid)0000-0002-5216-9380</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Faruque, M. R. I.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Khandaker, Mayeen Uddin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kearfott, Kimberlee Jane</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ahmad, Pervaiz</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Pure and applied geophysics</subfield><subfield code="d">Springer International Publishing, 1964</subfield><subfield code="g">178(2021), 5 vom: Mai, Seite 1593-1607</subfield><subfield code="w">(DE-627)129538353</subfield><subfield code="w">(DE-600)216719-0</subfield><subfield code="w">(DE-576)014971038</subfield><subfield code="x">0033-4553</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:178</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:5</subfield><subfield code="g">month:05</subfield><subfield code="g">pages:1593-1607</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00024-021-02736-9</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-GEO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GGO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GEO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_601</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">178</subfield><subfield code="j">2021</subfield><subfield code="e">5</subfield><subfield code="c">05</subfield><subfield code="h">1593-1607</subfield></datafield></record></collection>
|
| author |
Mir, Adil Aslam |
| spellingShingle |
Mir, Adil Aslam ddc 550 ssgn 16,13 misc Computational techniques misc radon time series misc stacking ensemble misc post-processing technique Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function |
| authorStr |
Mir, Adil Aslam |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)129538353 |
| format |
Article |
| dewey-ones |
550 - Earth sciences |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0033-4553 |
| topic_title |
550 VZ 16,13 ssgn Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function Computational techniques radon time series stacking ensemble post-processing technique |
| topic |
ddc 550 ssgn 16,13 misc Computational techniques misc radon time series misc stacking ensemble misc post-processing technique |
| topic_unstemmed |
ddc 550 ssgn 16,13 misc Computational techniques misc radon time series misc stacking ensemble misc post-processing technique |
| topic_browse |
ddc 550 ssgn 16,13 misc Computational techniques misc radon time series misc stacking ensemble misc post-processing technique |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Pure and applied geophysics |
| hierarchy_parent_id |
129538353 |
| dewey-tens |
550 - Earth sciences & geology |
| hierarchy_top_title |
Pure and applied geophysics |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)129538353 (DE-600)216719-0 (DE-576)014971038 |
| title |
Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function |
| ctrlnum |
(DE-627)OLC2126473120 (DE-He213)s00024-021-02736-9-p |
| title_full |
Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function |
| author_sort |
Mir, Adil Aslam |
| journal |
Pure and applied geophysics |
| journalStr |
Pure and applied geophysics |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science |
| recordtype |
marc |
| publishDateSort |
2021 |
| contenttype_str_mv |
txt |
| container_start_page |
1593 |
| author_browse |
Mir, Adil Aslam Çelebi, Fatih Vehbi Rafique, Muhammad Faruque, M. R. I. Khandaker, Mayeen Uddin Kearfott, Kimberlee Jane Ahmad, Pervaiz |
| container_volume |
178 |
| class |
550 VZ 16,13 ssgn |
| format_se |
Aufsätze |
| author-letter |
Mir, Adil Aslam |
| doi_str_mv |
10.1007/s00024-021-02736-9 |
| normlink |
(ORCID)0000-0002-5216-9380 |
| normlink_prefix_str_mv |
(orcid)0000-0002-5216-9380 |
| dewey-full |
550 |
| title_sort |
anomaly classification for earthquake prediction in radon time series data using stacking and automatic anomaly indication function |
| title_auth |
Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function |
| abstract |
Abstract To minimize catastrophic impacts of earthquakes, many researchers have directed their efforts to developing novel computational techniques and models that forecast natural disasters based upon earthquake catalogues, anomalies in the ionosphere, and radon time series databases, etc. In this article, we propose a methodology that categorizes the soil radon gas (SRG) concentration into seismically active and non-active time series by taking the advantage of a stacking ensemble approach and utilizing an automatic anomaly indication function as a post-processing technique. The main idea behind the proposed methodology is of two layers. The first layer uses a stacking ensemble-based approach that incorporates three learners, i.e. a generalized linear model, linear regression and K-nearest neighbors, to train on seismically active and inactive periods to predict SRG concentration. These predictions are then combined with the labeled anomaly data to train a meta-learner, i.e., support vector machine with a radial kernel, that categorizes the series into active and non-active radon time series data. In the second layer, these classifications are then passed to an automatic anomaly indication function that further labels the time series in a group of readings where the level of received indications is greater than or equal to the indication factor. Radon time series (RTS) data has been recorded over a fault line present in Muzaffarabad, a city in the Pakistan territory of Kashmir, ranging from 1 March 2017 to 28 February 2018. Four seismic events occurred during the study period. The conclusion of the study reveals that the proposed methodology accurately locates the anomaly in the RTS data at different window sizes, i.e. with respect to individual days. The evaluation is based purely on the classification after processing of RTS data by both layers of the proposed methodology. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021 |
| abstractGer |
Abstract To minimize catastrophic impacts of earthquakes, many researchers have directed their efforts to developing novel computational techniques and models that forecast natural disasters based upon earthquake catalogues, anomalies in the ionosphere, and radon time series databases, etc. In this article, we propose a methodology that categorizes the soil radon gas (SRG) concentration into seismically active and non-active time series by taking the advantage of a stacking ensemble approach and utilizing an automatic anomaly indication function as a post-processing technique. The main idea behind the proposed methodology is of two layers. The first layer uses a stacking ensemble-based approach that incorporates three learners, i.e. a generalized linear model, linear regression and K-nearest neighbors, to train on seismically active and inactive periods to predict SRG concentration. These predictions are then combined with the labeled anomaly data to train a meta-learner, i.e., support vector machine with a radial kernel, that categorizes the series into active and non-active radon time series data. In the second layer, these classifications are then passed to an automatic anomaly indication function that further labels the time series in a group of readings where the level of received indications is greater than or equal to the indication factor. Radon time series (RTS) data has been recorded over a fault line present in Muzaffarabad, a city in the Pakistan territory of Kashmir, ranging from 1 March 2017 to 28 February 2018. Four seismic events occurred during the study period. The conclusion of the study reveals that the proposed methodology accurately locates the anomaly in the RTS data at different window sizes, i.e. with respect to individual days. The evaluation is based purely on the classification after processing of RTS data by both layers of the proposed methodology. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021 |
| abstract_unstemmed |
Abstract To minimize catastrophic impacts of earthquakes, many researchers have directed their efforts to developing novel computational techniques and models that forecast natural disasters based upon earthquake catalogues, anomalies in the ionosphere, and radon time series databases, etc. In this article, we propose a methodology that categorizes the soil radon gas (SRG) concentration into seismically active and non-active time series by taking the advantage of a stacking ensemble approach and utilizing an automatic anomaly indication function as a post-processing technique. The main idea behind the proposed methodology is of two layers. The first layer uses a stacking ensemble-based approach that incorporates three learners, i.e. a generalized linear model, linear regression and K-nearest neighbors, to train on seismically active and inactive periods to predict SRG concentration. These predictions are then combined with the labeled anomaly data to train a meta-learner, i.e., support vector machine with a radial kernel, that categorizes the series into active and non-active radon time series data. In the second layer, these classifications are then passed to an automatic anomaly indication function that further labels the time series in a group of readings where the level of received indications is greater than or equal to the indication factor. Radon time series (RTS) data has been recorded over a fault line present in Muzaffarabad, a city in the Pakistan territory of Kashmir, ranging from 1 March 2017 to 28 February 2018. Four seismic events occurred during the study period. The conclusion of the study reveals that the proposed methodology accurately locates the anomaly in the RTS data at different window sizes, i.e. with respect to individual days. The evaluation is based purely on the classification after processing of RTS data by both layers of the proposed methodology. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 GBV_ILN_601 |
| container_issue |
5 |
| title_short |
Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function |
| url |
https://doi.org/10.1007/s00024-021-02736-9 |
| remote_bool |
false |
| author2 |
Çelebi, Fatih Vehbi Rafique, Muhammad Faruque, M. R. I. Khandaker, Mayeen Uddin Kearfott, Kimberlee Jane Ahmad, Pervaiz |
| author2Str |
Çelebi, Fatih Vehbi Rafique, Muhammad Faruque, M. R. I. Khandaker, Mayeen Uddin Kearfott, Kimberlee Jane Ahmad, Pervaiz |
| ppnlink |
129538353 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00024-021-02736-9 |
| up_date |
2024-07-04T07:08:07.122Z |
| _version_ |
1803631341990838272 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2126473120</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505115217.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230505s2021 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00024-021-02736-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2126473120</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00024-021-02736-9-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">16,13</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Mir, Adil Aslam</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract To minimize catastrophic impacts of earthquakes, many researchers have directed their efforts to developing novel computational techniques and models that forecast natural disasters based upon earthquake catalogues, anomalies in the ionosphere, and radon time series databases, etc. In this article, we propose a methodology that categorizes the soil radon gas (SRG) concentration into seismically active and non-active time series by taking the advantage of a stacking ensemble approach and utilizing an automatic anomaly indication function as a post-processing technique. The main idea behind the proposed methodology is of two layers. The first layer uses a stacking ensemble-based approach that incorporates three learners, i.e. a generalized linear model, linear regression and K-nearest neighbors, to train on seismically active and inactive periods to predict SRG concentration. These predictions are then combined with the labeled anomaly data to train a meta-learner, i.e., support vector machine with a radial kernel, that categorizes the series into active and non-active radon time series data. In the second layer, these classifications are then passed to an automatic anomaly indication function that further labels the time series in a group of readings where the level of received indications is greater than or equal to the indication factor. Radon time series (RTS) data has been recorded over a fault line present in Muzaffarabad, a city in the Pakistan territory of Kashmir, ranging from 1 March 2017 to 28 February 2018. Four seismic events occurred during the study period. The conclusion of the study reveals that the proposed methodology accurately locates the anomaly in the RTS data at different window sizes, i.e. with respect to individual days. The evaluation is based purely on the classification after processing of RTS data by both layers of the proposed methodology.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computational techniques</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">radon time series</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">stacking ensemble</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">post-processing technique</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Çelebi, Fatih Vehbi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rafique, Muhammad</subfield><subfield code="0">(orcid)0000-0002-5216-9380</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Faruque, M. R. I.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Khandaker, Mayeen Uddin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kearfott, Kimberlee Jane</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ahmad, Pervaiz</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Pure and applied geophysics</subfield><subfield code="d">Springer International Publishing, 1964</subfield><subfield code="g">178(2021), 5 vom: Mai, Seite 1593-1607</subfield><subfield code="w">(DE-627)129538353</subfield><subfield code="w">(DE-600)216719-0</subfield><subfield code="w">(DE-576)014971038</subfield><subfield code="x">0033-4553</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:178</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:5</subfield><subfield code="g">month:05</subfield><subfield code="g">pages:1593-1607</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00024-021-02736-9</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-GEO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GGO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GEO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_601</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">178</subfield><subfield code="j">2021</subfield><subfield code="e">5</subfield><subfield code="c">05</subfield><subfield code="h">1593-1607</subfield></datafield></record></collection>
|
| score |
7.402895 |