Prediction of weather induced background radiation fluctuation with recurrent neural networks

Background radiation estimation plays an important role in the anomalous radiation detection. Accurately estimating temporal and spatial fluctuations of background radiation helps to reduce the false alarm rate and improve the estimation accuracy of anomalous source location. It has been long observed that background radiation is positively correlated with precipitation due to the scavenging effect of rain and snow. This paper presents the usage of recurrent neural networks to predict the background radiation level based on past weather and radiation data. Two datasets are prepared with different noise levels. Experiment results show that recurrent neural networks outperform the traditional moving average algorithm on the high noise dataset; recurrent neural networks perform as well as the moving average algorithm on the low noise dataset.