Yang H and Liu S (2022), Water quality prediction in sea cucumber farmin...

ECB-ART-50276

PeerJ Comput Sci 2022 May 31;8:e1000. doi: 10.7717/peerj-cs.1000.

Show Gene links Show Anatomy links

Water quality prediction in sea cucumber farming based on a GRU neural network optimized by an improved whale optimization algorithm.

Yang H , Liu S .

???displayArticle.abstract???
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.

???displayArticle.pubmedLink??? 35721411
???displayArticle.pmcLink??? PMC9202628
???displayArticle.link??? PeerJ Comput Sci

???attribute.lit??? ???displayArticles.show???

References [+] :

Aldhyani, Water Quality Prediction Using Artificial Intelligence Algorithms. 2020, Pubmed

	Figure 1. GRU neuron structure diagram.
	Figure 2. The IWOA-GRU prediction model construction flowchart.
	Figure 3. VMD decomposition effect of water quality data.
	Figure 4. Water quality data denoising effect.
	Figure 5. Parameter optimization curve.
	Figure 6. The prediction effect of the SVR model on dissolved oxygen.
	Figure 7. The prediction effect of the CNN model on dissolved oxygen.
	Figure 8. The prediction effect of the RF model on dissolved oxygen.
	Figure 9. The prediction effect of the RNN model on dissolved oxygen.
	Figure 10. The prediction effect of the LSTM model on dissolved oxygen.
	Figure 11. The prediction effect of the GRU model on dissolved oxygen.
	Figure 12. The prediction effect of the IWOA-GRU model on dissolved oxygen.
	Figure 13. Predictive effect of each comparative model on water temperature.
	Figure 14. Predictive effect of each comparative model on salinity.
	Figure 15. Water quality prediction effect of multiple marine ranches.