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Automatic Cluster-oriented Seismicity Prediction Analysis of Earthquake Data Distribution in Indonesia

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@article{IJASEIT7269,
   author = {Ali Ridho Barakbah and Tri Harsono and Amang Sudarsono},
   title = {Automatic Cluster-oriented Seismicity Prediction Analysis of Earthquake Data Distribution in Indonesia},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {2},
   year = {2019},
   pages = {587--593},
   keywords = {seismicity prediction analysis; earthquake prediction; automatic clustering; semantic interpretation.},
   abstract = {Many researchers have analyzed the earthquakes to predict the earthquake period occurrences. However, they commonly faced the difficulty to project the prediction into the region adjusted to the earthquake data distribution and to provide an interpretation of the prediction for the region. This paper presents a new system for cluster-oriented seismicity prediction analysis, and semantic interpretation of the prediction result projected to the region. The system applies our automatic clustering algorithm to detect some clusters automatically depending on the earthquake data distribution and create clusters of the earthquake data for the prediction. The semantic interpretation is presented in the system to provide easier information from the seismicity prediction analysis. The system consists of four main computational functions: (1) Data acquisition and pre-processing, (2) Automatic clustering of earthquake data distribution, (3) Seismicity prediction of earthquake time period occurrence based on cluster with confidence levels of seismic event using the Guttenberg-Richter law, and (4) Region-based seismicity prediction analysis and semantic interpretation of the prediction for each cluster. For experiments, we use earthquake data series provided by the Advanced National Seismic System (ANSS) in the year 1963-2015 with the location of Indonesia. We made a series of experiments for earthquakes in Nias (2005), Yogyakarta (2006), and Padang (2009), with respectively 6.3, 7.6 and 8.7 Richter magnitude level. Our system presented the seismicity prediction analysis from each earthquake cluster and provided an easy interpretation of the prediction probability.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7269},
   doi = {10.18517/ijaseit.9.2.7269}
}

EndNote

%A Barakbah, Ali Ridho
%A Harsono, Tri
%A Sudarsono, Amang
%D 2019
%T Automatic Cluster-oriented Seismicity Prediction Analysis of Earthquake Data Distribution in Indonesia
%B 2019
%9 seismicity prediction analysis; earthquake prediction; automatic clustering; semantic interpretation.
%! Automatic Cluster-oriented Seismicity Prediction Analysis of Earthquake Data Distribution in Indonesia
%K seismicity prediction analysis; earthquake prediction; automatic clustering; semantic interpretation.
%X Many researchers have analyzed the earthquakes to predict the earthquake period occurrences. However, they commonly faced the difficulty to project the prediction into the region adjusted to the earthquake data distribution and to provide an interpretation of the prediction for the region. This paper presents a new system for cluster-oriented seismicity prediction analysis, and semantic interpretation of the prediction result projected to the region. The system applies our automatic clustering algorithm to detect some clusters automatically depending on the earthquake data distribution and create clusters of the earthquake data for the prediction. The semantic interpretation is presented in the system to provide easier information from the seismicity prediction analysis. The system consists of four main computational functions: (1) Data acquisition and pre-processing, (2) Automatic clustering of earthquake data distribution, (3) Seismicity prediction of earthquake time period occurrence based on cluster with confidence levels of seismic event using the Guttenberg-Richter law, and (4) Region-based seismicity prediction analysis and semantic interpretation of the prediction for each cluster. For experiments, we use earthquake data series provided by the Advanced National Seismic System (ANSS) in the year 1963-2015 with the location of Indonesia. We made a series of experiments for earthquakes in Nias (2005), Yogyakarta (2006), and Padang (2009), with respectively 6.3, 7.6 and 8.7 Richter magnitude level. Our system presented the seismicity prediction analysis from each earthquake cluster and provided an easy interpretation of the prediction probability.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7269
%R doi:10.18517/ijaseit.9.2.7269
%J International Journal on Advanced Science, Engineering and Information Technology
%V 9
%N 2
%@ 2088-5334

IEEE

Ali Ridho Barakbah,Tri Harsono and Amang Sudarsono,"Automatic Cluster-oriented Seismicity Prediction Analysis of Earthquake Data Distribution in Indonesia," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 2, pp. 587-593, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.2.7269.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Barakbah, Ali Ridho
AU  - Harsono, Tri
AU  - Sudarsono, Amang
PY  - 2019
TI  - Automatic Cluster-oriented Seismicity Prediction Analysis of Earthquake Data Distribution in Indonesia
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 2
Y2  - 2019
SP  - 587
EP  - 593
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - seismicity prediction analysis; earthquake prediction; automatic clustering; semantic interpretation.
N2  - Many researchers have analyzed the earthquakes to predict the earthquake period occurrences. However, they commonly faced the difficulty to project the prediction into the region adjusted to the earthquake data distribution and to provide an interpretation of the prediction for the region. This paper presents a new system for cluster-oriented seismicity prediction analysis, and semantic interpretation of the prediction result projected to the region. The system applies our automatic clustering algorithm to detect some clusters automatically depending on the earthquake data distribution and create clusters of the earthquake data for the prediction. The semantic interpretation is presented in the system to provide easier information from the seismicity prediction analysis. The system consists of four main computational functions: (1) Data acquisition and pre-processing, (2) Automatic clustering of earthquake data distribution, (3) Seismicity prediction of earthquake time period occurrence based on cluster with confidence levels of seismic event using the Guttenberg-Richter law, and (4) Region-based seismicity prediction analysis and semantic interpretation of the prediction for each cluster. For experiments, we use earthquake data series provided by the Advanced National Seismic System (ANSS) in the year 1963-2015 with the location of Indonesia. We made a series of experiments for earthquakes in Nias (2005), Yogyakarta (2006), and Padang (2009), with respectively 6.3, 7.6 and 8.7 Richter magnitude level. Our system presented the seismicity prediction analysis from each earthquake cluster and provided an easy interpretation of the prediction probability.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7269
DO  - 10.18517/ijaseit.9.2.7269

RefWorks

RT Journal Article
ID 7269
A1 Barakbah, Ali Ridho
A1 Harsono, Tri
A1 Sudarsono, Amang
T1 Automatic Cluster-oriented Seismicity Prediction Analysis of Earthquake Data Distribution in Indonesia
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 2
YR 2019
SP 587
OP 593
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 seismicity prediction analysis; earthquake prediction; automatic clustering; semantic interpretation.
AB Many researchers have analyzed the earthquakes to predict the earthquake period occurrences. However, they commonly faced the difficulty to project the prediction into the region adjusted to the earthquake data distribution and to provide an interpretation of the prediction for the region. This paper presents a new system for cluster-oriented seismicity prediction analysis, and semantic interpretation of the prediction result projected to the region. The system applies our automatic clustering algorithm to detect some clusters automatically depending on the earthquake data distribution and create clusters of the earthquake data for the prediction. The semantic interpretation is presented in the system to provide easier information from the seismicity prediction analysis. The system consists of four main computational functions: (1) Data acquisition and pre-processing, (2) Automatic clustering of earthquake data distribution, (3) Seismicity prediction of earthquake time period occurrence based on cluster with confidence levels of seismic event using the Guttenberg-Richter law, and (4) Region-based seismicity prediction analysis and semantic interpretation of the prediction for each cluster. For experiments, we use earthquake data series provided by the Advanced National Seismic System (ANSS) in the year 1963-2015 with the location of Indonesia. We made a series of experiments for earthquakes in Nias (2005), Yogyakarta (2006), and Padang (2009), with respectively 6.3, 7.6 and 8.7 Richter magnitude level. Our system presented the seismicity prediction analysis from each earthquake cluster and provided an easy interpretation of the prediction probability.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7269
DO  - 10.18517/ijaseit.9.2.7269