Statistical approaches to forecast gamma dose rates by using measurements from the atmosphere

In this paper, the results obtained by inter-comparing several statistical techniques for estimating gamma dose rates, such as an exponential moving average model, a seasonal exponential smoothing model and an artificial neural networks model, are reported. Seven years of gamma dose rates data measured in Daejeon City, Korea, were divided into two parts to develop the models and validate the effectiveness of the generated predictions by the techniques mentioned above. Artificial neural networks model shows the best forecasting capability among the three statistical models. The reason why the artificial neural networks model provides a superior prediction to the other models would be its ability for a non-linear approximation. To replace the gamma dose rates when missing data for an environmental monitoring system occurs, the moving average model and the seasonal exponential smoothing model can be better because they are faster and easier for applicability than the artificial neural networks model. These kinds of statistical approaches will be helpful for a real-time control of radio emissions or for an environmental quality assessment.