Modeling Techniques for Estimating Soil Radon Concentrations

• Published in: Journal of physics. Conference series Vol. 2919; no. 1; pp. 12036 - 12044
• Main Authors: Borgohain, Devashree, Jaishi, Hari Prasad, Singh, Sanjay, Tiwari, Raghavendra Prasad, Tiwari, Ramesh Chandra
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.12.2024
• Subjects: Autocorrelation functions; Autoregressive models; Geophysics; Natural radioactivity; Prediction models; Radon; Radon levels; Soil analysis; Statistical analysis; Time series
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2919/1/012036

This paper describes the comprehensive analysis of soil radon concentrations through the application of the Auto Regressive Integrated Moving Average (ARIMA) Model. The study utilizes information on radon gathered from March 2013 to May 2016, employing LR-115 type II detectors. Radon and thoron, prominent constituents of natural radioactivity in the Earth’s crust, are pivotal subjects in geophysics and indoor measurements. The investigation focuses on deriving the predicted values of radon concentrations, employing various ARIMA models. Specifically, four distinct models: ARIMA (3, 0, 7), ARIMA (4, 0, 7), ARIMA (3, 0, 6), and ARIMA (4, 0, 6) are constructed. Evaluation of the auto-correlation function (ACF) and partial auto correlation function (PACF) plots suggests that ARIMA (3, 0, 7) and ARIMA (4, 0, 7) stand out as promising candidates for effectively capturing the data dynamics. To validate the efficacy of each model and ascertain the most suitable one, a comparative analysis between the estimated and real values is conducted. This involves the implementation of a linear fit test and an F-test. The statistical results decisively indicate that ARIMA (3, 0, 7) outperforms ARIMA (4, 0, 7), establishing its superiority as a better model to forecast radon in soil concentrations in the given time series data. This comprehensive approach demonstrates the significance of precise predictive models in spotting deviations in time series datasets and advances the understanding of soil radon variation.