Duration prediction of Chilean strong motion data using machine learning

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.