Duration prediction of Chilean strong motion data using machine learning

被引：13

作者：

Chanda, Sarit ^{[1
]}

Raghucharan, M. C. ^{[1
]}

Reddy, K. S. K. Karthik ^{[1
]}

Chaudhari, Vasudeo ^{[1
]}

Somala, Surendra Nadh ^{[1
]}

机构：

[1] Indian Inst Technol Hyderabad, Hyderabad, India

来源：

JOURNAL OF SOUTH AMERICAN EARTH SCIENCES | 2021年 / 109卷

关键词：

Duration; Significant-duration; Inslab; Classifiers; Machine learning algorithms; STRONG GROUND MOTION;

D O I：

10.1016/j.jsames.2021.103253

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

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.

引用

页数：8

共 13 条

[1] Physically Parameterized Prediction Equations for Significant Duration in Active Crustal Regions [J].