Automated anomalous behaviour detection in soil radon gas prior to earthquakes using computational intelligence techniques.
J Environ Radioact
; 203: 48-54, 2019 Jul.
Article
in En
| MEDLINE
| ID: mdl-30861489
ABSTRACT
In this article, three computational intelligence (CI) models were developed to automatically detect anomalous behaviour in soil radon gas (222Rn) time series data. Data were obtained at a fault line and analysed using three machine learning techniques with the aim at identifying anomalies in temporal radon data prompted by seismic events. Radon concentrations were modelled with corresponding meteorological and statistical parameters. This leads to the estimation of soil radon gas without and with meteorological parameters. The comparison between computed radon concentration and actual radon concentrations was used in finding radon anomaly based upon automated system. The anomaly in radon time series data could be considered due to noise or seismic activity. Findings of study show that under meticulously characterized environments, on exclusion of noise contribution, seismic activity is responsible for anomalous behaviour seen in radon time series data.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Soil Pollutants, Radioactive
/
Artificial Intelligence
/
Radiation Monitoring
/
Radon
Type of study:
Diagnostic_studies
/
Prognostic_studies
Language:
En
Journal:
J Environ Radioact
Journal subject:
SAUDE AMBIENTAL
Year:
2019
Document type:
Article
Affiliation country:
Pakistan