Modeling of Safety Parameters for Low Level Radioactive Waste Repository Using Machine Learning Approaches

Abstract

The paper is aimed at the safety assessment of intermediate and low level radioactive waste (LLRW) disposal facility. Now a days extensive research is going on to develop safety assessment methodologies for radioactive waste disposal facilities. For disposal of low level radioactive waste, near surface disposal facility is assumed the preferred option. Safety assessment is helpful to get public confidence. The main objective of disposal of radioactive waste is to protect the human health and the environment from its worse effects. Therefore, it is necessary to manage the radioactive waste safely. In this work, machine learning (ML) approaches of support vector regression (SVR), generalized regression neural network (GRNN), artificial neural network (ANN) and multiple linear regressions (MLR) have been applied for the modeling of different safety parameters of LLRW disposal facility. Simulations have been performed to model the distribution coefficients (Kd), leaching rates (𝜆𝑙 ), and retardation factors (Rf) of radionuclide present in the RW. Experimentations are conducted in Matlab environment. Percentage absolute difference is used to evaluate the performance of the proposed models. The best results have been achieved by SVR and GRNN models with correlation coefficients R=0.99812 and 0.94773 for Kd, respectively. The performance of ML models is compared with conventional linear regression (LR) methods. Experiments highlights that the proposed ML models provide better results compared to conventional LR methods. This study is useful for the development and safety assessments of our national future assessment of low level radioactive waste disposal facility.