Detecting time series anomalies using hybrid methods applied to Radon signals recorded in caves for possible correlation with earthquakes
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985891%3A_____%2F20%3A00532709" target="_blank" >RIV/67985891:_____/20:00532709 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/68407700:21340/20:00345799
Výsledek na webu
<a href="https://link.springer.com/article/10.1007/s40328-020-00298-1" target="_blank" >https://link.springer.com/article/10.1007/s40328-020-00298-1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s40328-020-00298-1" target="_blank" >10.1007/s40328-020-00298-1</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Detecting time series anomalies using hybrid methods applied to Radon signals recorded in caves for possible correlation with earthquakes
Popis výsledku v původním jazyce
Anomalies in the Radon activity concentration time series recorded in five European caves (Czech Republic, Slovakia, Slovenia) are detected using three hybrid methods: (1) multiple linear regression and autoregressive integrated moving average statistical methods, (2) Empirical Mode Decomposition with Support Vector Regression techniques and (3) the Singular Spectrum Analysis composed with a predicting methodology. Results coming from the three methods are compared and the best hybrid method is selected based on statistical evaluation criteria of the uncertainty. Radon anomalies occur +/- 30 days from earthquake occurrence, selected according to the Dobrovolsky's earthquake preparation zone formula and to seismic events (with magnitude >= 4) occurred in the neighboring European Countries to the monitoring caves. The anomalies detection furnishes results consistent across the used methodologies, as proven by the calculation of a statistical parameter that search the presence of anomalies coming from the hybrid methods within +/- 30 days from earthquake event.
Název v anglickém jazyce
Detecting time series anomalies using hybrid methods applied to Radon signals recorded in caves for possible correlation with earthquakes
Popis výsledku anglicky
Anomalies in the Radon activity concentration time series recorded in five European caves (Czech Republic, Slovakia, Slovenia) are detected using three hybrid methods: (1) multiple linear regression and autoregressive integrated moving average statistical methods, (2) Empirical Mode Decomposition with Support Vector Regression techniques and (3) the Singular Spectrum Analysis composed with a predicting methodology. Results coming from the three methods are compared and the best hybrid method is selected based on statistical evaluation criteria of the uncertainty. Radon anomalies occur +/- 30 days from earthquake occurrence, selected according to the Dobrovolsky's earthquake preparation zone formula and to seismic events (with magnitude >= 4) occurred in the neighboring European Countries to the monitoring caves. The anomalies detection furnishes results consistent across the used methodologies, as proven by the calculation of a statistical parameter that search the presence of anomalies coming from the hybrid methods within +/- 30 days from earthquake event.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10402 - Inorganic and nuclear chemistry
Návaznosti výsledku
Projekt
Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2020
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Acta Geodaetica et Geophysica
ISSN
2213-5812
e-ISSN
—
Svazek periodika
55
Číslo periodika v rámci svazku
3
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
16
Strana od-do
405-420
Kód UT WoS článku
000559588400001
EID výsledku v databázi Scopus
2-s2.0-85085731743