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Smart Agriculture ?? 2021, Vol. 3 ?? Issue (2): 115-125.doi: 10.12133/j.smartag.2021.3.2.202106-SA008

? 信息处理与决策 ? 上一篇    下一篇

对虾养殖溶解氧浓度组合预测模型EMD-RF-LSTM

尹航1,3,4,6(), 李祥铜1,3,4,6, 徐龙琴1,3,4,6, 李景彬2, 刘双印1,2,3,4,5,6, 曹亮1,3,4, 冯大春1,3,4,6, 郭建军1,3,4,6, 李利桥2()   

  1. 1.仲恺农业工程学院 信息科学与技术学院,广东 广州 510225
    2.石河子大学 机械电气工程学院,新疆 石河子,832000
    3.仲恺农业工程学院 智慧农业创新研究院,广东 广州 510225
    4.仲恺农业工程学院 广东省高校智慧农业工程技术研究中心,广东 广州 510225
    5.仲恺农业工程学院 广东省水禽健康养殖重点实验室,广东 广州 510225
    6.仲恺农业工程学院 广东省农产品安全大数据工程技术研究中心,广东 广州 510225
  • 收稿日期:2021-06-11 修回日期:2021-06-28 出版日期:2021-06-30 发布日期:2021-08-25
  • 基金资助:
    国家自然科学基金项目(61871475);广东省自然科学基金项目(2021A1515011605);现代农业机械兵团重点实验室开放课题资助(BTNJ2021002);广东省科技厅重点领域研发计划项目(2020B0202080002);北京市自然科学基金项目(4182023);广东省科技计划项目(2019B020215003);广州市重点研发计划项目(20210300003);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)
  • 作者简介:尹 航(1978-),男,博士,副教授,研究方向为人工智能和重大装备健康管理。E-mail:736028008@qq.com
  • 通讯作者: 李利桥 E-mail:736028008@qq.com;liliqiao1108@ 163.com

EMD-RF-LSTM: Combination Prediction Model of Dissolved Oxygen Concentration in Prawn Culture

Hang YIN1,3,4,6(), Xiangtong LI1,3,4,6, Longqin XU1,3,4,6, Jingbin LI2, Shuangyin LIU1,2,3,4,5,6, Liang CAO1,3,4, Dachun FENG1,3,4,6, Jianjun GUO1,3,4,6, Liqiao LI2()   

  1. 1.Zhongkai University of Agriculture and Engineering, College of Information Science and Technology, Guangzhou, 510225, China
    2.Shihezi University, College of Mechanical and Electric Engineerings, Shihezi, 832000, China
    3.Zhongkai University of Agriculture and Engineering, Academy of Smart Agricultural Engineering Innovations, Guangzhou 510225, China
    4.Zhongkai University of Agriculture and Engineering, Smart Agriculture Engineering Research Center of Guangdong Higher Education Institutes, Guangzhou 510225, China
    5.Zhongkai University of Agriculture and Engineering, Guangdong Key Laboratory of Waterflow Health Breeding, Guangzhou 510225, China
  • Received:2021-06-11 Revised:2021-06-28 Online:2021-06-30 Published:2021-08-25
  • corresponding?author: Liqiao LI E-mail:736028008@qq.com;liliqiao1108@ 163.com

摘要:

溶解氧(DO)浓度是对虾养殖水质检测的核心指标。为提高对虾养殖溶解氧浓度的预测精度,本研究提出了一种基于经验模态分解、随机森林和长短时记忆神经网络(EMD-RF-LSTM)的对虾养殖溶解氧浓度组合预测模型。首先采用经验模态分解(EMD)对养殖水质溶解氧浓度时序数据进行多尺度特征提取,得到不同尺度下的固有模态分量(IMF);然后分别采用长短时记忆神经网络(LSTM)和随机森林(RF)对高、低频不同尺度IMF进行建模;最后结合各分量预测结果构建叠加模型,实现对溶解氧浓度时序数据的综合预测。本研究模型在广东省湛江市南三岛对虾养殖基地展开了试验及应用,在基于真实数据集的性能测试中,经验模态分解后EMD-ELM模型与极限学习机(ELM)模型对比,平均绝对误差(MAPE)、均方根误差(RMSE)和平均绝对误差(MAE)分别降低了30.11%、29.60%和32.95%。在经验模态分解基础上用RF和LSTM对不同特征尺度的本征模态分量分别预测后叠加求和,EMD-RF-LSTM模型预测的精度指标MAPERMSEMAE分别为0.0129、0.1156和0.0844,其中关键指标MAPE较EMD-ELM、EMD-RF和EMD-LSTM分别降低了84.07%、57.57%和49.81%,预测精度显著提高。结果表明,本研究针对经验模态分解后高、低频分量分别预测的策略可有效提升综合性能,表明本研究模型具有较高的预测精度,能够较准确地实现对虾养殖水体中溶解氧浓度预测。

关键词: 对虾养殖, 溶解氧浓度预测, 经验模态分解, 随机森林, 长短时记忆神经网络

Abstract:

Dissolved oxygen is an important environmental factor for prawn breeding. In order to improve the prediction accuracy of dissolved oxygen concentration in prawn pond, and solve the problem of low prediction accuracy of different frequency domain modal classification after empirical modal decomposition of nonlinear time series data when there are few training samples, an combination prediction model based on empirical mode decomposition (EMD), random forest (RF) and long short term memory neural network (LSTM) was proposed in this research. Firstly, the time series data of prawn breeding dissolved oxygen concentration were decomposed at multiple scales by EMD to obtain a set of stationary intrinsic mode function (IMF). Secondly, with fewer training samples, poor predicts effects on the low-frequency were verified component by LSTM. Then, IMF1-IMF4 were divided into high-frequency components through test results and used for LSTM model. IMF5-IMF7, Rn were divided for RF model, the EMD-RF-LSTM combination model was constructed to improve the prediction accuracy. Modeled low-frequency and high-frequency components IMF using RF and LSTM, then predictions of each component were accumulated and the prediction value of dissolved oxygen of sequence data were got. Finally, the performance of the model was compared with the limit learning machine (ELM), RF, standard LSTM, EMD-ELM and EMD-RF, EMD-LSTM, etc. In the test based on real dataset, the EMD-ELM model contrasted with ELM model, reduced the mean absolute error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) by 30.11%, 29.60% and 32.95%, respectively. The MAPE, RMSE, MAE for the proposed models were 0.0129,0.1156,0.0844, respectively. MAPE decreased by 84.07%, 57.57%, and 49.81% compared with EMD-ELM, EMD-RF and EMD-LSTM, respectively, the prediction accuracy was significantly improved. The results show that the proposed model EMD-RF-LSTM has good prediction performance and generalization ability, which is meets the actual demand of accurate prediction of dissolved oxygen concentration in prawn culture, and can provide reference for the prediction and early warning of prawn pond water quality.

Key words: prawn pond, dissolved oxygen prediction, empirical mode decomposition, random forest, short and long-term memory neural network

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