Temporal Prediction of Paralytic Shellfish Toxins in the Mussel Mytilus galloprovincialis Using a LSTM Neural Network Model from Environmental Data

Abstract

Paralytic shellfish toxins (PSTs) are produced mainly by Alexandrium catenella (formerly A. tamarense). Since 2000, the National Institute of Fisheries Science (NIFS) has been providing information on PST outbreaks in Korean coastal waters at one- or two-week intervals. However, a daily forecast is essential for immediate responses to PST outbreaks. This study aimed to predict the outbreak timing of PSTs in the mussel Mytilus galloprovincialis in Jinhae Bay and along the Geoje coast in the southern coast of the Korea Peninsula. We used a long-short-term memory (LSTM) neural network model for temporal prediction of PST outbreaks from environmental data, such as water temperature (WT), tidal height, and salinity, measured at the Geojedo, Gadeokdo, and Masan tidal stations from 2006 to 2020. We found that PST outbreaks is gradually accelerated during the three years from 2018 to 2020. Because the in-situ environmental measurements had many missing data throughout the time span, we applied LSTM for gap-filling of the environmental measurements. We trained and tested the LSTM models with different combinations of environmental factors and the ground truth timing data of PST outbreaks for 5479 days as input and output. The LSTM model trained from only WT had the highest accuracy (0.9) and lowest false-alarm rate. The LSTM-based temporal prediction model may be useful as a monitoring system of PSP outbreaks in the coastal waters of southern Korean.