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

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 decompositi...

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Autores principales:	YIN Hang, LI Xiangtong, XU Longqin, LI Jingbin, LIU Shuangyin, CAO Liang, FENG Dachun, GUO Jianjun, LI Liqiao
Formato:	article
Lenguaje:	EN ZH
Publicado:	Editorial Office of Smart Agriculture 2021
Materias:	prawn pond dissolved oxygen prediction empirical mode decomposition random forest short and long-term memory neural network Agriculture (General) S1-972 Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/35a84d9b883c4d0eb40405964e4ce369
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spelling	oai:doaj.org-article:35a84d9b883c4d0eb40405964e4ce3692021-11-17T07:52:11ZEMD-RF-LSTM: Combination Prediction Model of Dissolved Oxygen Concentration in Prawn Culture2096-809410.12133/j.smartag.2021.3.2.202106-SA008https://doaj.org/article/35a84d9b883c4d0eb40405964e4ce3692021-06-01T00:00:00Zhttp://www.smartag.net.cn/article/2021/2096-8094/2096-8094-2021-3-2-115.shtmlhttps://doaj.org/toc/2096-8094Dissolved 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.YIN HangLI XiangtongXU LongqinLI JingbinLIU ShuangyinCAO LiangFENG DachunGUO JianjunLI LiqiaoEditorial Office of Smart Agriculturearticleprawn ponddissolved oxygen predictionempirical mode decompositionrandom forestshort and long-term memory neural networkAgriculture (General)S1-972Technology (General)T1-995ENZH智慧农业, Vol 3, Iss 2, Pp 115-125 (2021)
institution	DOAJ
collection	DOAJ
language	EN ZH
topic	prawn pond dissolved oxygen prediction empirical mode decomposition random forest short and long-term memory neural network Agriculture (General) S1-972 Technology (General) T1-995
spellingShingle	prawn pond dissolved oxygen prediction empirical mode decomposition random forest short and long-term memory neural network Agriculture (General) S1-972 Technology (General) T1-995 YIN Hang LI Xiangtong XU Longqin LI Jingbin LIU Shuangyin CAO Liang FENG Dachun GUO Jianjun LI Liqiao EMD-RF-LSTM: Combination Prediction Model of Dissolved Oxygen Concentration in Prawn Culture
description	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.
format	article
author	YIN Hang LI Xiangtong XU Longqin LI Jingbin LIU Shuangyin CAO Liang FENG Dachun GUO Jianjun LI Liqiao
author_facet	YIN Hang LI Xiangtong XU Longqin LI Jingbin LIU Shuangyin CAO Liang FENG Dachun GUO Jianjun LI Liqiao
author_sort	YIN Hang
title	EMD-RF-LSTM: Combination Prediction Model of Dissolved Oxygen Concentration in Prawn Culture
title_short	EMD-RF-LSTM: Combination Prediction Model of Dissolved Oxygen Concentration in Prawn Culture
title_full	EMD-RF-LSTM: Combination Prediction Model of Dissolved Oxygen Concentration in Prawn Culture
title_fullStr	EMD-RF-LSTM: Combination Prediction Model of Dissolved Oxygen Concentration in Prawn Culture
title_full_unstemmed	EMD-RF-LSTM: Combination Prediction Model of Dissolved Oxygen Concentration in Prawn Culture
title_sort	emd-rf-lstm: combination prediction model of dissolved oxygen concentration in prawn culture
publisher	Editorial Office of Smart Agriculture
publishDate	2021
url	https://doaj.org/article/35a84d9b883c4d0eb40405964e4ce369
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EMD-RF-LSTM: Combination Prediction Model of Dissolved Oxygen Concentration in Prawn Culture

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