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| Titel |
A comparison of classical and intelligent methods to detect potential thermal anomalies before the 11 August 2012 Varzeghan, Iran, earthquake (Mw = 6.4) |
| VerfasserIn |
M. Akhoondzadeh |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 13, no. 4 ; Nr. 13, no. 4 (2013-04-23), S.1077-1083 |
| Datensatznummer |
250018416
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| Publikation (Nr.) |
 copernicus.org/nhess-13-1077-2013.pdf |
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| Zusammenfassung |
| In this paper, a number of classical and intelligent methods, including
interquartile, autoregressive integrated moving average (ARIMA), artificial
neural network (ANN) and support vector machine (SVM), have been proposed to
quantify potential thermal anomalies around the time of the 11 August 2012
Varzeghan, Iran, earthquake (Mw = 6.4). The duration of the data
set,
which is comprised of Aqua-MODIS land surface temperature (LST) night-time
snapshot images, is 62 days. In order to quantify variations of LST data
obtained from satellite images, the air temperature (AT) data derived from
the meteorological station close to the earthquake epicenter has been taken
into account. For the models examined here, results indicate the following:
(i) ARIMA models, which are the most widely used in the time series community
for short-term forecasting, are quickly and easily implemented, and can
efficiently act through linear solutions. (ii) A multilayer perceptron (MLP)
feed-forward neural network can be a suitable non-parametric method to
detect the anomalous changes of a non-linear time series such as variations of
LST. (iii) Since SVMs are often used due to their many advantages for
classification and regression tasks, it can be shown that, if the difference
between the predicted value using the SVM method and the observed value
exceeds the pre-defined threshold value, then the observed value could be
regarded as an anomaly. (iv) ANN and SVM methods could be powerful tools in modeling
complex phenomena such as earthquake precursor time series where we may not
know what the underlying data generating process is. There is good agreement
in the results obtained from the different methods for quantifying potential
anomalies in a given LST time series. This paper indicates that the
detection of the potential thermal anomalies derive credibility from
the overall efficiencies and potentialities of the four integrated
methods. |
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