| Autor: |
Arie Vatresia, Ferzha Utama, Intan Hati, Lindung Mase |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Journal of Soft Computing in Civil Engineering, Vol 8, Iss 3, Pp 71-86 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2588-2872 |
| DOI: |
10.22115/scce.2023.381204.1589 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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