Deep Learning for Prediction of Population of Acetes in Avoiding Biological Hazards for Nuclear Power Plants

Abstract

There have been frequent incidents of water intake blockage due to marine organisms, which pose a serious threat to the normal operation of nuclear power plants across the world. In order to avoid biological hazards for Nuclear Power Plants, we investigated the disaster-caused marine organism. In this work, we focus on the acetes, which is the main cause of the accident. By investigating the biological characteristics of acetes, we have established a mathematical model of the population dynamics of acetes. We have also utilized two deep learning methods, LSTM and Transformer, to predict the population density of acetes. Finally, we have also compared the two methods. As a result, we find that LSTM performs better and it can be used for data-based dynamical modeling in future work.