Artificial Neural Networks for Radiation Dose Prediction in Nuclear Emergencies-Preliminary Investigations

DOI

10.2991/msota-16.2016.98How to use a DOI?

Keywords

artificial neural networks, radiation dose prediction, nuclear power plant, atmospheric dispersion

Abstract

This work investigates the use of Artificial Neural Networks (ANN) for radiation dose prediction due to a nuclear power plant (NPP) accident with radioactive material release. The main objective is to avoid necessity of using complex time-consuming simulators during the emergency. Training, test and production data sets have been generated by realistic simulations on the precise atmospheric dispersion system used in CNAAA Brazilian NPP. Considering a hypothetical Lost of Coolant Accident (LOCA), several ANN architectures have been trained with a wide range of atmospheric conditions in order to predict spatial effective doses. As a result, a Backpropagation Multilayer Perceptron (MLP) with 5 layers demonstrated to achieve the best generalization, reaching a correlation factor of 0.990 for the validation dataset. On the other hand the GRNN reached a correlation factor slightly worse (0.986) but very faster.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Cláudio Márcio do Nascimento Abreu Pereira
AU  - Roberto Schirru
AU  - Kelcio Gomes
AU  - José Luiz Cunha
PY  - 2016/12
DA  - 2016/12
TI  - Artificial Neural Networks for Radiation Dose Prediction in Nuclear Emergencies-Preliminary Investigations
BT  - Proceedings of 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016)
PB  - Atlantis Press
SP  - 441
EP  - 444
SN  - 2352-538X
UR  - https://doi.org/10.2991/msota-16.2016.98
DO  - 10.2991/msota-16.2016.98
ID  - Pereira2016/12
ER  -