Temporal Prediction of Paralytic Shellfish Toxins in the Mussel Mytilus galloprovincialis Using a LSTM Neural Network Model from Environmental Data
Abstract
:1. Introduction
2. Results
2.1. Periodic Tendency of Paralytic Shellfish Toxins Outbreak
2.2. Gap-Filling of Environmental Factors
2.3. Temporal Prediction of PSTs Outbreak
3. Discussion
3.1. Performance of LSTM Models
3.2. Environmental Factors
4. Conclusions
5. Materials and Methods
5.1. Study Area
5.2. Data
5.3. LSTM Neural Network Model
5.4. Performance Assessment
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variable | Tidal Station | 1 Station | 2 Stations | ||
---|---|---|---|---|---|
Geojedo | Gadeokdo | Masan | |||
WT (°C) | Geojedo | - | 0.84 | 1.22 | 0.89 |
Gadeokdo | 1.08 | - | 1.33 | 0.92 | |
Masan | 2.22 | 1.82 | - | 1.91 | |
Tidal height (cm) | Geojedo | - | 35.76 | 19.15 | 18.45 |
Gadeokdo | 30.77 | - | 12.17 | 10.86 | |
Masan | 33.40 | 21.09 | - | 15.53 | |
Salinity (PSU) | Geojedo | - | 1.32 | 1.83 | 1.33 |
Gadeokdo | 2.08 | - | 2.77 | 2.22 | |
Masan | 3.44 | 3.69 | - | 3.45 |
Models | Factors | (1) | (2) | (3) | (4) | Accuracy |
---|---|---|---|---|---|---|
LSTM-1 | WT | 871 | 64 | 41 | 120 | 0.90 |
LSTM-2 | WT + Tidal | 822 | 33 | 90 | 151 | 0.89 |
LSTM-3 | WT + Salinity | 811 | 21 | 101 | 163 | 0.89 |
LSTM-4 | WT + Tidal + Salinity | 766 | 8 | 146 | 176 | 0.86 |
Station Name | Latitude (°N) | Longitude (°E) | Availability |
---|---|---|---|
Geoje (Ge) | 34.80 | 128.70 | 1 January 2006–Present |
Gadeokdo (Ga) | 35.02 | 128.81 | 1 January 1977–Present |
Masan (Ma) | 35.20 | 128.58 | 1 December 2002–Present |
Ground Truth Data | |||
---|---|---|---|
False (npst) | Ture (pst) | ||
The Predicted Result | False (nPST) | (1) True negative | (2) False negative |
True (PST) | (3) False positive | (4) True positive |
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Shin, J.; Kim, S.M. Temporal Prediction of Paralytic Shellfish Toxins in the Mussel Mytilus galloprovincialis Using a LSTM Neural Network Model from Environmental Data. Toxins 2022, 14, 51. https://doi.org/10.3390/toxins14010051
Shin J, Kim SM. Temporal Prediction of Paralytic Shellfish Toxins in the Mussel Mytilus galloprovincialis Using a LSTM Neural Network Model from Environmental Data. Toxins. 2022; 14(1):51. https://doi.org/10.3390/toxins14010051
Chicago/Turabian StyleShin, Jisun, and Soo Mee Kim. 2022. "Temporal Prediction of Paralytic Shellfish Toxins in the Mussel Mytilus galloprovincialis Using a LSTM Neural Network Model from Environmental Data" Toxins 14, no. 1: 51. https://doi.org/10.3390/toxins14010051