Tsunami potential identification based on seismic features using KNN algorithm
Novianty A.a, Machbub C.a, Widiyantoro S.a, Meilano I.a, Irawan H.a
a School of Electrical Engineering and Informatics, ITB, Indonesia
Abstract
© 2019 IEEE.Tsunami is one of the destructive natural disasters in the world. Tectonic earthquake events trigger most of tsunamis around the world. Machine learning has been used widely in various problems in the world, including natural disasters problems. However, the use of machine learning method in specific problems related to tsunami still requires a lot of study and improvement in many aspects. This paper presents the tsunami potential identification based on seismic parameters by utilizing a classification algorithm, namely k-nearest neighbors (k-NN). The algorithm is applied to classify earthquake events in the dataset into two classes, i.e., tsunami potential and no tsunami potential events. The study uses three schemes of features for the experiment to obtain the optimal accuracy. Results indicate that the accuracy of the identification is a promising result for initial research in this area.
Author keywords
Classification algorithm,Earthquake events,K-nearest neighbors,Machine learning methods,Natural disasters,Seismic parameters,Specific problems,Tectonic earthquakes
Indexed keywords
accuracy,earthquake,k-NN,machine learning,tsunami
Funding details
ACKNOWLEDGMENT This study is supported by the Ministry of Research, Technology, and Higher Education of the Republic of Indonesia through the national strategic research grant and also BMKG – the Agency of Meteorology, Climatology, and Geophysics of Indonesia as the partner institutions of the research.