Neuro-SVM Anticipatory System for Online Monitoring of Radiation and Abrupt Change Detection

Abstract

Fast processing of consecutive measurements and accurate detection of abrupt changes is of primary importance for online monitoring of radiation. In this paper, a hybrid anticipatory system is discussed together with its application to real time radiation monitoring. The system utilizes the synergism of an artificial neural network (ANN) with support vector machines (SVM) to tackle the problem of anticipating the measurements for an ahead-in-time horizon. The system employs an ensemble of support vector regressors (SVRs) whose predictions constitute the input values to a neural network. Therefore, it is the neural network that provides the final predictions over the designated time horizon. The neuro-SVM predictions are divided by the respective observed values and compared with a predetermined threshold where an abrupt change is identified if the ratio exceeds the threshold value. The proposed anticipatory system is compared with the naïve prediction approach as well as the independent support vector regressors. Results clearly demonstrate that the neuro-SVM outperforms the naïve predictor and the individual SVRs in the majority of the cases regarding prediction accuracy and rate of abrupt change detection.