基于WTD-LSTM的对虾养殖水温组合预测模型

李祥铜; 曹亮; 李湘丽; 刘双印; 徐龙琴; 呼增; 黄运茂; 尹航

doi:10.16768/j.issn.1004-874X.2021.02.020

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

摘要

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（M_APE）、均方根误差（R_MSE）及平均绝对误差（M_AE）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M_APE、R_MSE、M_AE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。

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(M_APE), root mean square error(R_MSE), and absolute error(M_AE)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 M_APE, R_MSE and M_AE 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 WTD-LSTM 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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