Spatial analisys of magnitude distribution for earthquake prediction using neural network based on automatic clustering in Indonesia

A spatial analysis of magnitude distribution is presented in this paper to identify the optimal number of clusters based on seismic data of all region in Indonesia. The data were obtained from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) and United States Geological Sur...

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Bibliographic Details
Published in2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) pp. 246 - 251
Main Authors Shodiq, Mohammad Nur, Kusuma, Dedy Hidayat, Rifqi, Mirza Ghulam, Barakbah, Ali Ridho, Harsono, Tri
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2017
Subjects
artificial neural network
Artificial neural networks
Clustering algorithms
earthquake prediction
Earthquakes
Geology
Predictive models
seismic clustering
Training
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Summary:A spatial analysis of magnitude distribution is presented in this paper to identify the optimal number of clusters based on seismic data of all region in Indonesia. The data were obtained from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) and United States Geological Survey's (USGS). Clustering process consist of two steps: finding the global optimum number of clusters using Valley Tracing and clustering the dataset based on Hierarchical K-means. The optimal number of cluster obtained is 6 cluster. A model of Artificial Neural Networks (ANNs) is implemented for selected cluster to conduct an earthquake prediction. The architecture of the neural network model is composed of seven inputs, two hidden layers with thirty-two nodes each and one output. Back propagation training method and sigmoid activation function are applied. The input values are related to the b-value, the Bath's law, and the Omori-Utsu's law. The ANNs prototype predicts earthquake which is equal or larger than the given threshold magnitude during the next five days after an earthquake occurrence. Statistical tests are provided using two threshold values (5.5 and 6). The ANNs result showed that the proposed model gave better performance to predict earthquake that equal or larger than 6 Richter's scale magnitude. Finally, the result were compared to other ANNs model showing quantitatively and qualitatively better results.
DOI:10.1109/KCIC.2017.8228594