Delegated Regressor, A Robust Approach for Automated Anomaly Detection in the Soil Radon Time Series Data.

We propose a new method based on the idea of delegating regressors for predicting the soil radon gas concentration (SRGC) and anomalies in radon or any other time series data. The proposed method is compared to different traditional boosting e.g.

Descriptive analysis and earthquake prediction using boxplot interpretation of soil radon time series data.

Correlation of radon anomalies with meteorological parameters and earthquake occurrence has been reported in many studies. This paper reports descriptive statistical analysis and boxplot contingent earthquake prediction based upon soil radon time series data. Data has been collected over a fault line, passing beneath the Muzaffarabad, for the period of one year.

Automated anomalous behaviour detection in soil radon gas prior to earthquakes using computational intelligence techniques.

In this article, three computational intelligence (CI) models were developed to automatically detect anomalous behaviour in soil radon gas (Rn) 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.