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Author Lin, J.W. ♦ Chiou, J.S. ♦ Chao, C.T.
Source Directory of Open Access Journals (DOAJ)
Content type Text
Publisher Copernicus Publications
File Format PDF
Date Created 2018-08-22
Copyright Year ©2018
Language English
Subject Domain (in LCC) QC801-809
Subject Keyword Science ♦ Physics ♦ Cosmic physics ♦ Geophysics
Abstract A new modified elementary Levenberg–Marquardt Algorithm (M-LMA) was used to minimise backpropagation errors in training a backpropagation neural network (BPNN) to predict the records related to the Chi-Chi earthquake from four seismic stations: Station-TAP003, Station-TAP005, Station-TCU084, and Station-TCU078 belonging to the Free Field Strong Earthquake Observation Network, with the learning rates of 0.3, 0.05, 0.2, and 0.28, respectively. For these four recording stations, the M-LMA has been shown to produce smaller predicted errors compared to the Levenberg–Marquardt Algorithm (LMA). A sudden predicted error could be an indicator for Early Earthquake Warning (EEW), which indicated the initiation of strong motion due to large earthquakes. A trade-Off decision-making process with BPNN (TDPB), using two alarms, adjusted the threshold of the magnitude of predicted error without a mistaken alarm. With this approach, it is unnecessary to consider the problems of characterising the wave phases and pre-processing, and does not require complex hardware; an existing seismic monitoring network-covered research area was already sufficient for these purposes.
ISSN 21930856
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2018-08-01
e-ISSN 21930856
Journal Geoscientific Instrumentation, Methods and Data Systems
Volume Number 7
Page Count 9
Starting Page 235
Ending Page 243


Source: Directory of Open Access Journals (DOAJ)