Neural Network for Earthquake Prediction Based on Automatic Clustering in Indonesia

Mohammad Nur Shodiq (State Polytechnic of Banyuwangi, East Java, Indonesia)
Dedy Hidayat Kusuma (State Polytechnic of Banyuwangi, East Java, Indonesia)
Mirza Ghulam Rifqi (State Polytechnic of Banyuwangi, East Java, Indonesia)
Ali Ridho Barakbah (Electronic Engineering Polytechnic Institute of Surabaya, East Java, Indonesia)
Tri Harsono (Electronic Engineering Polytechnic Institute of Surabaya, East Java, Indonesia)

JOIV : International Journal on Informatics Visualization
Vol 2, No 1 (2018)

A model of artificial neural networks (ANNs) is presented in this paper to predict aftershock during the next five days after an earthquake occurrence in selected cluster of Indonesia with magnitude equal or larger than given threshold. The data were obtained from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) and United States Geological Survey’s (USGS). Six clusters was an optimal number of cluster base-on cluster analysis implementing Valley Tracing and Hill Climbing algorithm, while Hierarchical K-means was applied for datasets clustering. A quality evaluation was then conducted to measure the proposed model performance for two different thresholds. The experimental result shows that the model gave better performance for predicting an aftershock occurrence that equal or larger than 6 Richter’s scale magnitude.