Cargando…
Water quality prediction in sea cucumber farming based on a GRU neural network optimized by an improved whale optimization algorithm
Sea cucumber farming is an important part of China’s aquaculture industry, and sea cucumbers have higher requirements for aquaculture water quality. This article proposes a sea cucumber aquaculture water quality prediction model that uses an improved whale optimization algorithm to optimize the gate...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9202628/ https://www.ncbi.nlm.nih.gov/pubmed/35721411 http://dx.doi.org/10.7717/peerj-cs.1000 |
_version_ | 1784728570866696192 |
---|---|
author | Yang, Huanhai Liu, Shue |
author_facet | Yang, Huanhai Liu, Shue |
author_sort | Yang, Huanhai |
collection | PubMed |
description | Sea cucumber farming is an important part of China’s aquaculture industry, and sea cucumbers have higher requirements for aquaculture water quality. This article proposes a sea cucumber aquaculture water quality prediction model that uses an improved whale optimization algorithm to optimize the gated recurrent unit neural network(IWOA-GRU), which provides a reference for the water quality control in the sea cucumber growth environment. This model first applies variational mode decomposition (VMD) and the wavelet threshold joint denoising method to remove mixed noise in water quality time series. Then, by optimizing the convergence factor, the convergence speed and global optimization ability of the whale optimization algorithm are strengthened. Finally, the improved whale optimization algorithm is used to construct a GRU prediction model based on optimal network weights and thresholds to predict sea cucumber farming water quality. The model was trained and tested using three water quality indices (dissolved oxygen, temperature and salinity) of sea cucumber culture waters in Shandong Peninsula, China, and compared with prediction models such as support vector regression (SVR), random forest (RF), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory neural network (LSTM). Experimental results show that the prediction accuracy and generalization performance of this model are better than those of the other compared models. |
format | Online Article Text |
id | pubmed-9202628 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92026282022-06-17 Water quality prediction in sea cucumber farming based on a GRU neural network optimized by an improved whale optimization algorithm Yang, Huanhai Liu, Shue PeerJ Comput Sci Computational Biology Sea cucumber farming is an important part of China’s aquaculture industry, and sea cucumbers have higher requirements for aquaculture water quality. This article proposes a sea cucumber aquaculture water quality prediction model that uses an improved whale optimization algorithm to optimize the gated recurrent unit neural network(IWOA-GRU), which provides a reference for the water quality control in the sea cucumber growth environment. This model first applies variational mode decomposition (VMD) and the wavelet threshold joint denoising method to remove mixed noise in water quality time series. Then, by optimizing the convergence factor, the convergence speed and global optimization ability of the whale optimization algorithm are strengthened. Finally, the improved whale optimization algorithm is used to construct a GRU prediction model based on optimal network weights and thresholds to predict sea cucumber farming water quality. The model was trained and tested using three water quality indices (dissolved oxygen, temperature and salinity) of sea cucumber culture waters in Shandong Peninsula, China, and compared with prediction models such as support vector regression (SVR), random forest (RF), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory neural network (LSTM). Experimental results show that the prediction accuracy and generalization performance of this model are better than those of the other compared models. PeerJ Inc. 2022-05-31 /pmc/articles/PMC9202628/ /pubmed/35721411 http://dx.doi.org/10.7717/peerj-cs.1000 Text en ©2022 Yang and Liu https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Computational Biology Yang, Huanhai Liu, Shue Water quality prediction in sea cucumber farming based on a GRU neural network optimized by an improved whale optimization algorithm |
title | Water quality prediction in sea cucumber farming based on a GRU neural network optimized by an improved whale optimization algorithm |
title_full | Water quality prediction in sea cucumber farming based on a GRU neural network optimized by an improved whale optimization algorithm |
title_fullStr | Water quality prediction in sea cucumber farming based on a GRU neural network optimized by an improved whale optimization algorithm |
title_full_unstemmed | Water quality prediction in sea cucumber farming based on a GRU neural network optimized by an improved whale optimization algorithm |
title_short | Water quality prediction in sea cucumber farming based on a GRU neural network optimized by an improved whale optimization algorithm |
title_sort | water quality prediction in sea cucumber farming based on a gru neural network optimized by an improved whale optimization algorithm |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9202628/ https://www.ncbi.nlm.nih.gov/pubmed/35721411 http://dx.doi.org/10.7717/peerj-cs.1000 |
work_keys_str_mv | AT yanghuanhai waterqualitypredictioninseacucumberfarmingbasedonagruneuralnetworkoptimizedbyanimprovedwhaleoptimizationalgorithm AT liushue waterqualitypredictioninseacucumberfarmingbasedonagruneuralnetworkoptimizedbyanimprovedwhaleoptimizationalgorithm |