Adaptive Neural Fuzzy Inference System and Automatic Clustering for Earthquake Prediction in Indonesia

Abstract

Earthquake is a type of natural disaster. The Indonesian archipelago located in the world's three mega plates; they are Australian plate, Eurasian plate, and Pacific plate. Therefore, it is possible for applied of earthquake risk of mitigation. One of them is to provide information about earthquake occurrences. This information used for spatiotemporal analysis of earthquakes. This paper presented Spatial Analysis of Magnitude Distribution for Earthquake Prediction using adaptive neural fuzzy inference system (ANFIS) based on automatic clustering in Indonesia. This system has three main sections: (1) Data preprocessing, (2) Automatic Clustering, (3) Adaptive Neural Fuzzy Inference System. For experimental study, earthquake data obtained Indonesian Agency for Meteorological, Climatological, and Geophysics (BMKG) and the United States Geological Survey’s (USGS), the year 2010-2017 in the location of Indonesia. Automatic clustering process produces The optimal number of cluster, that is 7 clusters. Each cluster will be analyzed based on earthquake distribution. Its calculate the b value of earthquake to get the seven seismicity indicators. Then, implementation for ANFIS uses 100 training epochs, Number of membership function (MFs) is 2, MFs type input is gaussian membership function (gaussmf). The ANFIS result showed that the system can predict the non-occurrence of aftershocks with the average performance of 70%.

Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges

“…For the study area of Indonesia, the magnitude of the earthquake was predicted by HKMC-ANN-based model [71], [72] with low performance and with ANFIS [55] it was predicted with high relative performance. ANFIS [54] predicted the occurrence and non-occurrence with low relative performance. GFCV-based method [48] showed high performance for bvalue prediction for the Indonesian region.…”

“…Shodiq et al [54] came up with the idea of using a combination of automatic clustering and ANFIS for earthquake prediction. The proposed model involves pre-processing, automatic clustering, and ANFIS.…”

“…Shodiq et al [54], used ANFIS for spatial analysis of magnitude distribution of aftershocks and found that the non-occurrence was able to be predicted by the model with 70% accuracy. But the prediction of the occurrence of the earthquake was 60% accurate.…”

“…These methods are considered as high performing methods according to the criteria described previously. The ANFIS-based method [54] achieved relative accuracy of 0% and the ANFIS-based method [52] had relative RMSE of 0%. These two models are reported as low performing methods.…”

Incremental Associative Mining based Risk-Mapping System for Earthquake Analysis in Indonesia

“…Barakbah et al [10,27] analyzed cluster-oriented seismicity predictions of earthquakes in Indonesia and semantic interpretation of predicted results projected into each region. Shodiq et al [11][12][13][14] performed earthquake predictions using the Automatic Clustering and spatial analysis of magnitude distribution to cluster the earthquake data based on the location of the source point and Neural Networks methods.…”