| 李祥铜 1,2，曹 亮 1,2，李湘丽 2,3，刘双印 1,2,4，徐龙琴 1,2，呼 增 1,2，黄运茂 2，尹 航 1,2.基于 WTD-LSTM 的对虾养殖水温组合预测模型[J].广东农业科学,2021,48(2):153-160 |
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基于 WTD-LSTM 的对虾养殖水温组合预测模型 |
| 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 |
| 中文关键词: 对虾 水温 预测 小波阈值降噪 长短时记忆神经网络 |
| 英文关键词: prawn water temperature prediction wavelet threshold denoising（WTD） long short-term memory （LSTM）neural network |
| 基金项目:国家自然科学基金（61871475）；广东省科技计划项目（2017B0101260016）；广州市创新平台建设计划项目（201905010006）；广东省农业技术研发项目（2018LM2168） |
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| 中文摘要: |
| 【目的】提高对虾养殖水温预测精度，及时掌握水产养殖水温变化规律。【方法】提出基于小波
阈值降噪（Wavelet threshold denoising，WTD）和长短时记忆神经网络（Long short-term memory，LSTM）的水产
养殖水温预测模型，利用 WTD 方法消除原变量间的相关性，减少数据噪声干扰并增强信号数据平滑性，进一步
利用预测能力极强的 LSTM 进行预测。【结果】WTD-LSTM 模型评价指标平均绝对误差（MAPE）、均方根误差（RMSE）
及平均绝对误差（MAE）分别为 0.0104、0.0382 和 0.0288，与标准 BP 神经网络、标准 ELM、标准 LSTM 等 3 种
模型进行对比，评价指标 MAPE、RMSE、MAE 分别降低了 64.85%、59.62%、64.62%， 63.64%、61.18%、60.12%，
47.48%、37.07%、46.27%；从可视化分析来看，WTD-LSTM 预测模型预测结果贴近真实值曲线，相比其他 3 种
模型，能很好地拟合养殖水温非线性时间序列变化趋势。【结论】WTD-LSTM 模型具有良好的预测性能和泛化
能力，可以满足对虾养殖水温精确预测的实际需求，能为对虾养殖水质预测预警提供决策。 |
| 英文摘要: |
| 【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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