Seismic Ground Parameter Estimation in Cianjur Using Artificial Neural Network Method with PSO - Dalam bentuk buku karya ilmiah
DYKA KHAIRULLAH ZAMHARI

Informasi Umum

Kode

24.05.625

Klasifikasi

006.32 - Neural networks, perceptrons, connectionism, neural computers

Jenis

Karya Ilmiah - Thesis (S2) - Reference

Subjek

Deep Learning

Dilihat

196 kali

Informasi Lainnya

Abstraksi

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. This can be seen from the evaluation results in the ANN HNE + PSO Set 1 scenario obtained an MSE score of 0.185, MAE 0.209, as well as the highest regression score value obtained in the scenario of 0.904. In the acceleration on displacement dataset experiment there is an ANN HNN + PSO Set 7 model scenario with an MSE score of 0.035, MAE 0.096, as well as the highest regression score value obtained in that scenario of 0.982. In the acceleration on velocity experiment there is ANN HNE + PSO Set 2 with an MSE score of 0.000, MAE 0.001, as well as the highest regression score value obtained in the scenario of 0.999. In acceleration + displacement there is an ANN HNN + PSO Set 7 scenario with an MSE score of 0.035, MAE 0.084, as well as the highest regression score value obtained in the scenario of 0.982. In acceleration + velocity + displacement there is an ANN HNE + PSO Set 2 scenario with an MSE score of 3.723, MAE 0.001, as well as the highest regression score value obtained in the scenario of 0.999. In acceleration + velocity there is an ANN HNE + PSO Set 2 scenario with an MSE score of 5.018, MAE 0.004, as well as the highest regression score value obtained in the scenario of 0.999. The other scenarios generated in this study show good performance in general, but with variations in performance depending on the data set used.

Keyword: ANN, Cianjur, Earthquake, Estimation, PSO

Koleksi & Sirkulasi

Tersedia 1 dari total 1 Koleksi

Pengarang

Nama	DYKA KHAIRULLAH ZAMHARI
Jenis	Perorangan
Penyunting	Muhammad Ary Murti, Hilman Fauzi Tresna Sania Putra
Penerjemah

Penerbit

Nama	Universitas Telkom, S2 Teknik Elektro
Kota	Bandung
Tahun	2024

Sirkulasi

Harga sewa	IDR 0,00
Denda harian	IDR 0,00
Jenis	Non-Sirkulasi

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