Seismic Ground Parameter Estimation in Cianjur Using Artificial Neural Network Method with PSO

Indonesia, strategically positioned along the Pacific Ring of Fire, is exceptionally susceptible to seismic activities. Accurate estimation of ground motion parameters is crucial for mitigating the impact of earthquakes. Recent advancements in machine learning have expanded its application across various fields, including the analysis of seismic data. This research proposes the use of Artificial Neural Networks (ANNs) to enhance the accuracy and reliability of estimating ground motion parameters. Our methodology involves the collection and processing of data from seismic stations across Indonesia, provided by BMKG (Meteorological, Climatological, and Geophysical Agency of Indonesia). The dataset encompasses records from eight seismological stations, capturing three channels. This study primarily focuses on the Cianjur region, a zone highly prone to seismic disturbances, and intends to develop a robust model capable of accurately estimating ground motion parameters, particularly in regions with incomplete or limited seismic data. Therefore, this research is expected to contribute to ongoing efforts to improve the estimation of earthquake-related seismic parameters by estimating earthquake-related seismic parameters using the Artificial Neural Network (ANN) method and model optimization using the Particle Swarm Optimization (PSO) algorithm. The results were evaluated using PSO and without PSO. It is shown that the model using PSO shows better performance than without using PSO. In the ANN HNE + PSO Set 4 scenario, the model achieved an MSE of 0.185, MAE of 0.209, and a regression score of 0.904. The other scenarios generated in this study show good performance in general, but with variations in performance depending on the data set used.