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Predictive Analysis of the Building Damage From the 2011 Great East Japan Tsunami Using Decision Tree Classification Related Algorithms
- Source :
- IEEE Access, Vol 9, Pp 31065-31077 (2021)
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- When considering a tsunami disaster, many researchers have considered the tsunami's flow depth and velocity as the primary contributors to the building damage. Additionally, the majority of these studies have used the maximum value as the measure of each of these two factors. However, building damage may not occur when the maximum flow depth and the maximum flow velocity of the tsunami are reached. This study addressed two objectives based on the 2011 Great East Japan Earthquake and Tsunami. Firstly, to find out whether the maximum values of the flow depth and flow velocity are the same as their critical values and, secondly, to verify which combination of the parameters is the best predictor of the building damage level. The data from 18,000 buildings in Ishinomaki City, Japan, with the cooperation of the Japanese joint survey team, were analyzed using the decision tree related algorithms. The critical variables were the simulated data at the time when the buildings collapsed. The analysis showed the accuracy of the prediction based on the group of variables. Finally, the findings showed that the combination of the critical flow depth and maximum flow velocity provided the highest accuracy for classifying the level of building damage.
- Subjects :
- 2011 Great East Japan earthquake and tsunami
021110 strategic, defence & security studies
010504 meteorology & atmospheric sciences
General Computer Science
Decision tree learning
Maximum flow problem
0211 other engineering and technologies
General Engineering
Decision tree
Building damages
data mining
02 engineering and technology
01 natural sciences
decision tree algorithm
Statistical classification
Flow velocity
Simulated data
General Materials Science
Joint (building)
lcsh:Electrical engineering. Electronics. Nuclear engineering
lcsh:TK1-9971
Algorithm
Flow depth
Geology
0105 earth and related environmental sciences
Subjects
Details
- ISSN :
- 21693536
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....3093929951fc9cf03a6395f7c668691b