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Publication Type : Journal Article
Source : Journal of South American Earth Sciences 109, 2021
Url : https://doi.org/10.1016/j.jsames.2021.103253
Keywords : Duration, Significant-duration, InslabClassifiers, Machine learning algorithms
Campus : Coimbatore
School : School of Engineering
Department : Civil
Year : 2021
Abstract : 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.
Cite this Research Publication : Chanda, Sarit, M. C. Raghucharan, K. S. K Karthik Reddy, Vasudeo Chaudhari, and Surendra Nadh Somala. "Duration prediction of Chilean strong motion data using machine learning." Journal of South American Earth Sciences 109, 2021