Prediction of Peak Ground Acceleration (PGA) in Java Using Artificial Neural Network Method

Dublin Core

Title

Prediction of Peak Ground Acceleration (PGA) in Java Using Artificial Neural Network Method

Subject

Earthquake, Prediction, Peak Ground Acceleration (PGA), Artificial Neural Network

Description

Java is one of the islands in Indonesia that frequently experiences earthquakes. Earthquakes can cause significant ground motion that can damage buildings and threaten human life. Peak Ground Acceleration (PGA) is a measure of the maximum ground acceleration that occurs during an earthquake and is an important factor that must be considered at every construction site to assess the potential damage that can be caused by an earthquake. The parameters considered in determining PGA predictions are earthquake parameters, such as magnitude and hypocenter distance. In addition, the PGA value is also influenced by local site conditions. With advances in information technology and artificial intelligence, especially in the development of Artificial Neural Networks (ANN), research on PGA prediction needs to be conducted as one of the efforts to reducethe risk of earthquakes. The purpose of this research is to obtain the best network architecture forpredicting PGA values. The criteria for selecting the best network architecture is done by comparing the error value of each possible architecture formed. The best prediction results are obtained in the model with 3-15-1 architecture with a correlation value of 0.67.

Creator

Sofyan Hadi Rahmawan1, Cahyo Crysdian2, Sri Harini3

Source

https://ijicom.respati.ac.id/index.php/ijicom/article/view/90/58

Date

December 2024

Contributor

Fajar bagus W

Format

PDF

Language

English

Type

Text

Files

Collection

Citation

Sofyan Hadi Rahmawan1, Cahyo Crysdian2, Sri Harini3, “Prediction of Peak Ground Acceleration (PGA) in Java Using Artificial Neural Network Method,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8399.