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

Yang, Hongjuan; Li, Shue

doi:10.7717/peerj-cs.1000

Cited by 19 publications

(7 citation statements)

References 44 publications

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“…GRU computations share similarities with the hidden states in standard RNNs. In a GRU, the previous hidden state h t−1 has the same dimensions as the reset gate (Yang and Liu, 2022). After computing the candidate hidden state, the prior hidden state information is transferred to the current hidden state through the update gate.…”

Section: Data Pre-processingmentioning

confidence: 99%

Research on Water Quality Prediction of Mine Surroundings Based on Improved Machine Learning Algorithm

Liu,

Gao

2024

Preprint

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Acid Mine Drainage (AMD) poses a significant environmental challenge, frequently occurring at mining, smelting, and closed mine locations. This phenomenon arises when sulfide ores come into contact with oxygen and water, forming sulfuric acid. This acid subsequently dissolves in mine water, increasing its acidity. Sulfate (SO4) serves as a crucial indicator of acid mine water quality. Precise prediction of SO4 concentrations post-treatment is essential for achieving compliant and stable wastewater discharge, thereby mitigating environmental risks. In this paper, we introduce IPSO-GRU, a novel artificial intelligence algorithm designed to predict water quality accurately. Our IPSO-GRU model employs particle swarm optimization to enhance support vector regression for SO4 prediction. The performance indices of the model show a Root Mean Square Error (RMSE) of 0.104, a Mean Absolute Error (MAE) of 0.061, and a Coefficient of Determination (R²) of 0.79. Comparative evaluations with IPSO-RNN and IPSO-LSTM models reveal that IPSO-GRU outperforms these alternatives across RMSE, MAE, and R² metrics, confirming its efficacy as the most suitable model for predicting SO4 concentrations in mine wastewater.

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Section: Data Pre-processingmentioning

confidence: 99%

Research on Water Quality Prediction of Mine Surroundings Based on Improved Machine Learning Algorithm

Liu,

Gao

2024

Preprint

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“…Supplementing average value [16,17], linear interpolation [17,18], KNN [19,20] Dimensionality reduction PCA [8], pooling layer [21], t-SNE [22], SPCA [23] Removing outliers Pauta criterion [18], EWMA [24] Feature selection PSO [1], LASSO [8,25], ASO [26], GA [27], MI [28], GRA [29], PCC [30], CCA [31] Decomposition EMD [32], EEMD [33], CEEMDAN [19,27,[34][35][36][37][38], ICEEMDAN [39], SSA [40,41], VMD [42], SVMD [43] Normalization [6,17,20,27,30,31,34,42,[44][45][46][47][48][49]…”

Section: Missing Valuesmentioning

confidence: 99%

“…Sen et al [57] used a grid search to find the number of nodes in each layer and the number of layers in CNN and LSTM networks when designing the network architecture. Yang and Liu [35] used an improved whale optimization algorithm to optimize the number of layers and nodes in the GRU algorithm for water quality prediction. The results showed that the proposed model could outperform the original GRU model.…”

Section: Network Architecture Selectionmentioning

confidence: 99%

A Study of Optimization in Deep Neural Networks for Regression

et al. 2023

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Due to rapid development in information technology in both hardware and software, deep neural networks for regression have become widely used in many fields. The optimization of deep neural networks for regression (DNNR), including selections of data preprocessing, network architectures, optimizers, and hyperparameters, greatly influence the performance of regression tasks. Thus, this study aimed to collect and analyze the recent literature surrounding DNNR from the aspect of optimization. In addition, various platforms used for conducting DNNR models were investigated. This study has a number of contributions. First, it provides sections for the optimization of DNNR models. Then, elements of the optimization of each section are listed and analyzed. Furthermore, this study delivers insights and critical issues related to DNNR optimization. Optimizing elements of sections simultaneously instead of individually or sequentially could improve the performance of DNNR models. Finally, possible and potential directions for future study are provided.

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“…GRU may be presented as a spinoff of LSTM [26], a type of RNN. Although GRU is lucid and more compact than LSTM, not only the competency in mastering context is not omitted, but on the contrary, reducing the training time [27] [28]. Alluded to research conducted by [6] as well as [8] [9] [10] on predicting COVID-19 mRNA vaccine degradation rate, it is deduced that GRU is indeed an applicable algorithm for this bioinformatics-related artificial intelligence-based research.…”

Section: Gated Recurrent Unit (Gru)mentioning

confidence: 99%

Exploration of hybrid deep learning algorithms for covid-19 mrna vaccine degradation prediction system

Ing

Abdullah²,

Mashor³

et al. 2022

Int. J. Adv. Intell. Informatics

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Coronavirus causes a global pandemic that has adversely affected public health, the economy, including every life aspect. To manage the spread, innumerable measurements are gathered. Administering vaccines is considered to be among the precautionary steps under the blueprint. Among all vaccines, the messenger ribonucleic acid (mRNA) vaccines provide notable effectiveness with minimal side effects. However, it is easily degraded and limits its application. Therefore, considering the cruciality of predicting the degradation rate of the mRNA vaccine, this prediction study is proposed. In addition, this study compared the hybridizing sequence of the hybrid model to identify its influence on prediction performance. Five models are created for exploration and prediction on the COVID-19 mRNA vaccine dataset provided by Stanford University and made accessible on the Kaggle community platform employing the two deep learning algorithms, Long Short-Term Memory (LSTM) as well as Gated Recurrent Unit (GRU). The Mean Columnwise Root Mean Square Error (MCRMSE) performance metric was utilized to assess each model’s performance. Results demonstrated that both GRU and LSTM are befitting for predicting the degradation rate of COVID-19 mRNA vaccines. Moreover, performance improvement could be achieved by performing the hybridization approach. Among Hybrid_1, Hybrid_2, and Hybrid_3, when trained with Set_1 augmented data, Hybrid_3 with the lowest training error (0.1257) and validation error (0.1324) surpassed the other two models; the same for model training with Set_2 augmented data, scoring 0.0164 and 0.0175 MCRMSE for training error and validation error, respectively. The variance in results obtained by hybrid models from experimenting claimed hybridizing sequence of algorithms in hybrid modeling should be concerned.

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

Cited by 19 publications

References 44 publications

Research on Water Quality Prediction of Mine Surroundings Based on Improved Machine Learning Algorithm

Research on Water Quality Prediction of Mine Surroundings Based on Improved Machine Learning Algorithm

A Study of Optimization in Deep Neural Networks for Regression

Exploration of hybrid deep learning algorithms for covid-19 mrna vaccine degradation prediction system

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