- Tytuł:
- Development of Data-mining Technique for Seismic Vulnerability Assessment
- Autorzy:
-
Wojcik, Waldemar
Karmenova, Markhaba
Smailova, Saule
Tlebaldinova, Aizhan
Belbeubaev, Alisher - Powiązania:
- https://bibliotekanauki.pl/articles/1844631.pdf
- Data publikacji:
- 2021
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
data analysis
seismic assessment
clustering
h-means
k-means
random forest - Opis:
- Assessment of seismic vulnerability of urban infrastructure is an actual problem, since the damage caused by earthquakes is quite significant. Despite the complexity of such tasks, today’s machine learning methods allow the use of “fast” methods for assessing seismic vulnerability. The article proposes a methodology for assessing the characteristics of typical urban objects that affect their seismic resistance; using classification and clustering methods. For the analysis, we use kmeans and hkmeans clustering methods, where the Euclidean distance is used as a measure of proximity. The optimal number of clusters is determined using the Elbow method. A decision-making model on the seismic resistance of an urban object is presented, also the most important variables that have the greatest impact on the seismic resistance of an urban object are identified. The study shows that the results of clustering coincide with expert estimates, and the characteristic of typical urban objects can be determined as a result of data modeling using clustering algorithms.
- Źródło:
-
International Journal of Electronics and Telecommunications; 2021, 67, 2; 261-266
2300-1933 - Pojawia się w:
- International Journal of Electronics and Telecommunications
- Dostawca treści:
- Biblioteka Nauki
Artykuł