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| Prediction Model of Water Temperature Combination for Prawn Cluture Based on WTD-LSTM |
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| DOI：10.16768/j.issn.1004-874X.2021.02.020 |
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| Abstract: |
| 【Objective】The study was conducted to improve the prediction accuracy of water temperature in prawn
culture and grasp the change rules of aquaculture timely【Method】An prediction model of aquaculture water temperature
based on Wavelet Threshold Denoising（WTD） and Long Short-term Memory（LSTM）neural network was proposed. The WTD method was used to eliminate the correlation between the original variables, reduce noise interference and enhance
the smoothness of signal data. Furtherly, the LSTM with strong predictive power was used to predict the signals.【Result】
The mean absolute error（MAPE）, root mean square error（RMSE）, and absolute error（MAE）of WTD-LSTM were 0.0104,
0.0382 and 0.0288, respectively. Compared with standard BP neural network, standard ELM and standard LSTM, the evaluation
indicators of MAPE, RMSE and MAE decreased by 64.85%, 59.62%, 64.62%; 63.64%, 61.18%, 60.12%; and 47.48%, 37.07%,
46.27%, respectively. According to the visual analysis, compared with the other three models, the prediction result of WTDLSTM was close to the true curve value, which could well fit for the nonlinear time series trend of aquaculture water temperature.
【Conclusion】The model has good prediction performance and generalization ability, which can meet the actual demand for
accurate prediction of water temperature in prawn culture and provide decision-making for water quality prediction and early
warning of prawn culture. |
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