期刊文献+

基于WTD-LSTM的对虾养殖水温组合预测模型 被引量:3

Prediction Model of Water Temperature Combination for Prawn Cluture Based on WTD-LSTM
下载PDF
导出
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。 【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 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.
作者 李祥铜 曹亮 李湘丽 刘双印 徐龙琴 呼增 黄运茂 尹航 LI Xiangtong;CAO Liang;LI Xiangli;LIU Shuangyin;XU Longqin;HU Zeng;HUANG Yunmao;YIN Hang(College of Information Science and Technology,Zhongkai University of Agriculture and Engineering,Guangzhou 510225,China;Intelligent Agriculture Engineering Research Center of Guangdong Higher Education Institutes/Guangzhou Key Laboratory of Agricultural Products Quality&Safety Traceability Information Technology,Guangzhou 510225,China;Library,Zhongkai University of Agriculture and Engineering,Guangzhou 510225,China;College of Mechanical and Electric Engineering,Shihezi University,Shihezi 832000,China)
出处 《广东农业科学》 CAS 2021年第2期153-160,共8页 Guangdong Agricultural Sciences
基金 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)。
关键词 对虾 水温 预测 小波阈值降噪 长短时记忆神经网络 prawn water temperature prediction wavelet threshold denoising(WTD) long short-term memory(LSTM)neural network
  • 相关文献

参考文献15

二级参考文献186

共引文献706

同被引文献69

引证文献3

二级引证文献3

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部