Duration prediction of Chilean strong motion data using machine learning

Chanda, Sarit and Raghucharan, M.C. and Karthik Reddy, K.S.K. and Chaudhari, Vasudeo and Somala, S N (2021) Duration prediction of Chilean strong motion data using machine learning. Journal of South American Earth Sciences, 109. p. 103253. ISSN 08959811

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Chile is rocked by inslab, interface as well as crustal events. Duration estimates based on Chilean strong motion flatfile is used to predict total duration as well as significant-duration. We use six different machine learning algorithms k-nearest neighbours, support vector machine, Random forest, Neural network, AdaBoost, decision tree and estimate the accuracies of prediction for each component (EW, NS, Z) of ground motion for different tectonic environments. The estimates of duration using machine learning are found to be quite accurate and the best performing machine learning algorithm in prediction of the total duration and the significant-duration are highlighted.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Article
Uncontrolled Keywords: Classifiers, Duration, Inslab, Machine learning algorithms, Significant-duration
Subjects: Civil Engineering > Geosystems
Divisions: Department of Civil Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 24 Mar 2021 06:43
Last Modified: 24 Mar 2021 06:43
URI: http://raiith.iith.ac.in/id/eprint/7686
Publisher URL: http://doi.org/10.1016/j.jsames.2021.103253
OA policy: https://v2.sherpa.ac.uk/id/publication/14115
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