Facta universitatis - series: Electronics and Energetics 2022 Volume 35, Issue 4, Pages: 603-617
https://doi.org/10.2298/FUEE2204603D
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Chaotic seismic signal modeling based on noise and earthquake anomaly detection
Dehbozorgi Leila (Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran)
Akbari-Hasanjani Reza (Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran)
Sabbaghi-Nadooshan Reza (Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran), r_sabbaghi@iauctb.ac.ir
Since ancient times, people have tried to predict earthquakes using simple
perceptions such as animal behavior. The prediction of the time and strength
of an earthquake is of primary concern. In this study chaotic signal
modeling is used based on noise and detecting anomalies before an earthquake
using artificial neural networks (ANNs). Artificial neural networks are
efficient tools for solving complex problems such as prediction and
identification. In this study, the effective features of chaotic signal
model is obtained considering noise and detection of anomalies five minutes
before an earthquake occurrence. Neuro-fuzzy classifier and MLP neural
network approaches showed acceptable accuracy of 84.6491% and 82.8947%,
respectively. Results demonstrate that the proposed method is an effective
seismic signal model based on noise and anomaly detection before an
earthquake.
Keywords: Artificial neural networks, chaos, earthquake, entropy, prediction, seismic signal processing, wavelet transforms
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