Discovering Bengkulu Province Earthquake Clusters with CLARANS Methods
Bengkulu is one of the provinces that lies in the ring of fire which has dynamic number of earthquakes each year. There were 2,989 earthquakes recorded in the Bengkulu from 2000 to 2021 with spatially different and unique attributes. The occurrences over Bengkulu were also included on the top twenty...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Pouyan Press
2024-07-01
|
| Series: | Journal of Soft Computing in Civil Engineering |
| Subjects: | |
| Online Access: | https://www.jsoftcivil.com/article_196432_c0ea8a47053a0d85f93bd9dcc9eae900.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849718197098381312 |
|---|---|
| author | Arie Vatresia Ferzha Utama Intan Hati Lindung Mase |
| author_facet | Arie Vatresia Ferzha Utama Intan Hati Lindung Mase |
| author_sort | Arie Vatresia |
| collection | DOAJ |
| description | Bengkulu is one of the provinces that lies in the ring of fire which has dynamic number of earthquakes each year. There were 2,989 earthquakes recorded in the Bengkulu from 2000 to 2021 with spatially different and unique attributes. The occurrences over Bengkulu were also included on the top twenty of significant event of earthquake that caused a lot of damage and death with the highest level of magnitude (8.4 SR). Due to its novel occurrences, how the points of earthquake clustered is still an interesting research question to be answered to empowered the location to be ready for the disaster. With the accelerating movement of artificial intelligence, this research used three methods to process earthquake data (Elbow, CLARANS, and Silhouette Coefficient) to find a better understanding of the cluster’s discovery. Matuschka method was occupied to uncover the earthquakes intensity and level of damage caused by earthquakes in the areas based on the MMI scale. This research succeeded to generate the six best cluster types based on the time of occurrence and level of damage during with the information. Furthermore, this research also mapped the severity of risk over the district to see the distribution of clusters produced. This research found that the area of Rejang Lebong is the most vulnerable to the earthquake with the class of heavy damage based on MMI value. Another class that involved into moderate risk is Muko-Muko area. The Validation performance showed a value of 0.55 which involved on feasible rate. |
| format | Article |
| id | doaj-art-079faffd7e464f9781071e1aca727d8a |
| institution | DOAJ |
| issn | 2588-2872 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Pouyan Press |
| record_format | Article |
| series | Journal of Soft Computing in Civil Engineering |
| spelling | doaj-art-079faffd7e464f9781071e1aca727d8a2025-08-20T03:12:26ZengPouyan PressJournal of Soft Computing in Civil Engineering2588-28722024-07-0183718610.22115/scce.2023.381204.1589196432Discovering Bengkulu Province Earthquake Clusters with CLARANS MethodsArie Vatresia0Ferzha Utama1Intan Hati2Lindung Mase3Associate Professor, Department of Informatics, Faculty of Engineering, University of Bengkulu, Bengkulu, IndonesiaLecturer, Department of Information System, Faculty of Engineering, University of Bengkulu, Bengkulu, IndonesiaGraduate Student, Department of Informatics, Faculty of Engineering, University of Bengkulu, Bengkulu, IndonesiaAssociate Professor, Department of Civil Engineering, Faculty of Engineering, University of Bengkulu, Bengkulu, IndonesiaBengkulu is one of the provinces that lies in the ring of fire which has dynamic number of earthquakes each year. There were 2,989 earthquakes recorded in the Bengkulu from 2000 to 2021 with spatially different and unique attributes. The occurrences over Bengkulu were also included on the top twenty of significant event of earthquake that caused a lot of damage and death with the highest level of magnitude (8.4 SR). Due to its novel occurrences, how the points of earthquake clustered is still an interesting research question to be answered to empowered the location to be ready for the disaster. With the accelerating movement of artificial intelligence, this research used three methods to process earthquake data (Elbow, CLARANS, and Silhouette Coefficient) to find a better understanding of the cluster’s discovery. Matuschka method was occupied to uncover the earthquakes intensity and level of damage caused by earthquakes in the areas based on the MMI scale. This research succeeded to generate the six best cluster types based on the time of occurrence and level of damage during with the information. Furthermore, this research also mapped the severity of risk over the district to see the distribution of clusters produced. This research found that the area of Rejang Lebong is the most vulnerable to the earthquake with the class of heavy damage based on MMI value. Another class that involved into moderate risk is Muko-Muko area. The Validation performance showed a value of 0.55 which involved on feasible rate.https://www.jsoftcivil.com/article_196432_c0ea8a47053a0d85f93bd9dcc9eae900.pdfearthquakebengkuluclusteringelbowclarans |
| spellingShingle | Arie Vatresia Ferzha Utama Intan Hati Lindung Mase Discovering Bengkulu Province Earthquake Clusters with CLARANS Methods Journal of Soft Computing in Civil Engineering earthquake bengkulu clustering elbow clarans |
| title | Discovering Bengkulu Province Earthquake Clusters with CLARANS Methods |
| title_full | Discovering Bengkulu Province Earthquake Clusters with CLARANS Methods |
| title_fullStr | Discovering Bengkulu Province Earthquake Clusters with CLARANS Methods |
| title_full_unstemmed | Discovering Bengkulu Province Earthquake Clusters with CLARANS Methods |
| title_short | Discovering Bengkulu Province Earthquake Clusters with CLARANS Methods |
| title_sort | discovering bengkulu province earthquake clusters with clarans methods |
| topic | earthquake bengkulu clustering elbow clarans |
| url | https://www.jsoftcivil.com/article_196432_c0ea8a47053a0d85f93bd9dcc9eae900.pdf |
| work_keys_str_mv | AT arievatresia discoveringbengkuluprovinceearthquakeclusterswithclaransmethods AT ferzhautama discoveringbengkuluprovinceearthquakeclusterswithclaransmethods AT intanhati discoveringbengkuluprovinceearthquakeclusterswithclaransmethods AT lindungmase discoveringbengkuluprovinceearthquakeclusterswithclaransmethods |