Analysis of Seismic Hazard Prediction Using Non Parametric Conic Multivariate Adaptive Regression Splines (C-Mars) Methods

Abstract

The data mining process requires a data set that can be used in determining a number of specific patterns to gain new knowledge. Large data sets (Big data) require special methods to get effective results. Included in this study are using big data related to the earthquake in Lombok. Earthquake research, especially in Lombok, is needed because Lombok is on three active plates in Indonesia, so that the danger of earthquake damage can be minimized. Earthquake data obtained from the Geophysical Station (BMKG) of Mataram has different characteristics and is complex, an appropriate method is needed, namely by detecting a non-parametric method with the Multivariate Adaptive Regression Spline (MARS). The use of the backward stepwise algorithm with the Conical Quadratic Programming (CQP) framework of MARS, referred to as CMARS (Conic Multivariate Adaptive Regression Splines), is used for optimizing the results. The conclusions of this study are 1. A mathematical model with a total of 12 basis functions (BF) has contributed to the prediction analysis of the PGA dependent variable. 2. Contributions of the influence of independent variables on the PGA value are the epicenter distance (R e pi) of 100% and the Magnitude (Mw) of 31.08608%, while the temperature of the incident location (SUHU) of 5.48525% and depth (Depth) of 3,52988%. 3. Acquired areas that have earthquake hazard levels in the order of the most vulnerable are Malaka, Genggelang, Tegal Maja, Senggigi and Mangsit.