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., Extreme Gradient Boosting (EGB) and simple regression methods e.g., support vector regressors with linear kernel and radial kernel in terms of accurate predictions. R language has been used for the statistical analysis of radon time series (RTS) data. The results obtained show that the proposed methodology predicts SRGC more accurately when compared to different traditional boosting and regression methods. The best correlation is found between the actual and predicted radon concentration for window size of 2 i.e., two days before and after the start of seismic activities. RTS data was collected from 05 February 2017 to 16 February 2018, including 7 seismic events recorded during the study period. Findings of study show that the proposed methodology predicts the SRGC with more precision, for all the window sizes, by overlapping predicted with the actual radon time series concentrations.

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. Soil radon gas (SRG) was measured using RTM 1688-2 radiometric instrument (made by SARAD GmbH). The range of radon in soil was found to be 14349 Bqm-3, whereas the ranges of temperature, pressure and relative humidity were found as 38.50 C0, 29 mbar and 67% respectively. SRG data shows that time series follows normal distribution. Values of coefficient of variation (CV) indicate the consistency of the recorded values of radon in soil and metrological parameters. Variance inflation factor (VIF) and Durbin Watson test (d) indicate a moderate multicollinearity and autocorrelation between variables. The analysis of radon time series using boxplots and meteorological parameters show specific patterns in radon concentrations (outliers, variant IQRs, first quartile values, and median values) due to pre-earthquake underground seismic activities. On the basis of these patterns earthquake may be more early predicted without using complicated predictive systems. Boxplots also predicted that there is no significant pattern found in dispersion of meteorological factors measured in this study. To the best of our knowledge this is first ever attempt to predict earthquake using boxplot explanation.

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 (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.