A prediction model of aquaculture water quality based on multiscale decomposition

In the field of intensive aquaculture, the deterioration of water quality is one of the main factors restricting the normal growth of aquatic products. Predicting water quality in real time constitutes the theoretical basis for the evaluation, planning and intelligent regulation of the aquaculture e...

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Autores principales:	Huanhai Yang, Shue Liu
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	complete ensemble empirical mode decomposition with adaptive noise long short-term memory sample entropy water quality prediction aquaculture Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/3c2d74c9cd1649e48597b40d286a9658
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id	oai:doaj.org-article:3c2d74c9cd1649e48597b40d286a9658
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spelling	oai:doaj.org-article:3c2d74c9cd1649e48597b40d286a96582021-11-23T02:22:37ZA prediction model of aquaculture water quality based on multiscale decomposition10.3934/mbe.20213741551-0018https://doaj.org/article/3c2d74c9cd1649e48597b40d286a96582021-09-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021374?viewType=HTMLhttps://doaj.org/toc/1551-0018In the field of intensive aquaculture, the deterioration of water quality is one of the main factors restricting the normal growth of aquatic products. Predicting water quality in real time constitutes the theoretical basis for the evaluation, planning and intelligent regulation of the aquaculture environment. Based on the design principles of decomposition, recombination and integration, this paper constructs a multiscale aquaculture water quality prediction model. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is used to decompose the different water quality variables at different time scales step by step to generate a series of intrinsic mode function (IMF) components with the same characteristic scale. Then, the sample entropy of each IMF component is calculated, the components with similar sample entropies are combined, and the original data are recombined into several subsequences through the above operations. In this paper, a prediction model based on a long short-term memory (LSTM) neural network is constructed to predict each recombination subsequence, and the Adam optimization algorithm is used to continuously update the weight of neural network to train and optimize the prediction performance. Finally, the predicted value of each subsequence is superimposed to predict the original water quality data. The dissolved oxygen and pH data of an aquaculture base were collected for prediction experiments, the results of which show that the proposed model has a high prediction accuracy and strong generalization performance.Huanhai YangShue Liu AIMS Pressarticlecomplete ensemble empirical mode decomposition with adaptive noiselong short-term memorysample entropywater quality predictionaquacultureBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 7561-7579 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	complete ensemble empirical mode decomposition with adaptive noise long short-term memory sample entropy water quality prediction aquaculture Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	complete ensemble empirical mode decomposition with adaptive noise long short-term memory sample entropy water quality prediction aquaculture Biotechnology TP248.13-248.65 Mathematics QA1-939 Huanhai Yang Shue Liu A prediction model of aquaculture water quality based on multiscale decomposition
description	In the field of intensive aquaculture, the deterioration of water quality is one of the main factors restricting the normal growth of aquatic products. Predicting water quality in real time constitutes the theoretical basis for the evaluation, planning and intelligent regulation of the aquaculture environment. Based on the design principles of decomposition, recombination and integration, this paper constructs a multiscale aquaculture water quality prediction model. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is used to decompose the different water quality variables at different time scales step by step to generate a series of intrinsic mode function (IMF) components with the same characteristic scale. Then, the sample entropy of each IMF component is calculated, the components with similar sample entropies are combined, and the original data are recombined into several subsequences through the above operations. In this paper, a prediction model based on a long short-term memory (LSTM) neural network is constructed to predict each recombination subsequence, and the Adam optimization algorithm is used to continuously update the weight of neural network to train and optimize the prediction performance. Finally, the predicted value of each subsequence is superimposed to predict the original water quality data. The dissolved oxygen and pH data of an aquaculture base were collected for prediction experiments, the results of which show that the proposed model has a high prediction accuracy and strong generalization performance.
format	article
author	Huanhai Yang Shue Liu
author_facet	Huanhai Yang Shue Liu
author_sort	Huanhai Yang
title	A prediction model of aquaculture water quality based on multiscale decomposition
title_short	A prediction model of aquaculture water quality based on multiscale decomposition
title_full	A prediction model of aquaculture water quality based on multiscale decomposition
title_fullStr	A prediction model of aquaculture water quality based on multiscale decomposition
title_full_unstemmed	A prediction model of aquaculture water quality based on multiscale decomposition
title_sort	prediction model of aquaculture water quality based on multiscale decomposition
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/3c2d74c9cd1649e48597b40d286a9658
work_keys_str_mv	AT huanhaiyang apredictionmodelofaquaculturewaterqualitybasedonmultiscaledecomposition AT shueliu apredictionmodelofaquaculturewaterqualitybasedonmultiscaledecomposition AT huanhaiyang predictionmodelofaquaculturewaterqualitybasedonmultiscaledecomposition AT shueliu predictionmodelofaquaculturewaterqualitybasedonmultiscaledecomposition
_version_	1718417369469026304

A prediction model of aquaculture water quality based on multiscale decomposition

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