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Melki Adi Kurniawan
Universitas Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Education Materials Science & Nanotechnology Physics

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Convolutional Neural Network for Earthquake Ground Motion Prediction Model in Earthquake Early Warning System in West Java Melki Adi Kurniawan; Sastra Kusuma Wijaya; Nuraini Rahma Hanifa
Jurnal Penelitian Pendidikan IPA Vol. 9 No. 11 (2023): November
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i11.3514

Abstract

As urbanization continues, more people and infrastructure are concentrated in areas that are at risk from earthquakes. This can increase the potential damage and loss of life when earthquakes occur. Indonesia is a region that is near the boundary of three major tectonic plates which has a very high frequency of earthquake occurrences. Over the past two decades, a new approach to earthquake disaster risk mitigation has emerged. It is based on the advent of digital seismology and advances in data transmission and automatic processing that make it possible to send warnings before the largest ground motion that called the Earthquake Early Warning System (EEW). On-site EEW is a type of EEW that consists of limited seismic stations located at a specific destination/infrastructure (for early detection systems). On-site EEW estimates ground motion parameters directly from the characteristics of seismograms recorded by the system. An artificial intelligence approach to EEW is necessary to increase the speed and accuracy of information, which increases processing time, especially in areas very close to the epicenter
Convolutional Neural Network for Earthquake Ground Motion Prediction Model in Earthquake Early Warning System in West Java Melki Adi Kurniawan; Sastra Kusuma Wijaya; Nuraini Rahma Hanifa
Jurnal Penelitian Pendidikan IPA Vol 9 No 11 (2023): November
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i11.3514

Abstract

As urbanization continues, more people and infrastructure are concentrated in areas that are at risk from earthquakes. This can increase the potential damage and loss of life when earthquakes occur. Indonesia is a region that is near the boundary of three major tectonic plates which has a very high frequency of earthquake occurrences. Over the past two decades, a new approach to earthquake disaster risk mitigation has emerged. It is based on the advent of digital seismology and advances in data transmission and automatic processing that make it possible to send warnings before the largest ground motion that called the Earthquake Early Warning System (EEW). On-site EEW is a type of EEW that consists of limited seismic stations located at a specific destination/infrastructure (for early detection systems). On-site EEW estimates ground motion parameters directly from the characteristics of seismograms recorded by the system. An artificial intelligence approach to EEW is necessary to increase the speed and accuracy of information, which increases processing time, especially in areas very close to the epicenter
Co-Authors Nuraini Rahma Hanifa Sastra Kusuma Wijaya