DA-Bi-SRU for water quality prediction in smart mariculture

Chen, Zijie; Hu, Zhuhua; Xu, Lewei; Zhao, Yaochi; Zhou, Xiaoyi

doi:10.1016/j.compag.2022.107219

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…This model uses parallel computing to speed up model training, makes each time step independent of the one before it, employs skip connections to solve the gradient disappearance problem, and improves information capture about the time series' characteristics with respect to its positive and negative bi-directional structure. BiSRU is an improved version of BiGRU that retains the modeling capabilities while using less computation (and hyperparameters) [30].…”

Section: G Bidirectional Simple Recurrent Unit (Bisru)mentioning

confidence: 99%

A Novel Hybrid Model to Predict Dissolved Oxygen for Efficient Water Quality in Intensive Aquaculture

Liu

Hassan

et al. 2023

IEEE Access

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Dissolved oxygen content is a key indicator of water quality in aquaculture environment. Because of its nonlinearity, dynamics, and complexity, which makes traditional methods face challenges in the accuracy and speed of dissolved oxygen content prediction. As a solution to these issues, this study introduces a hybrid model consisting of the Light Gradient Boosting Machine (LightGBM) and the Bidirectional Simple Recurrent Unit (BiSRU). Firstly, Linear interpolation and smoothing were used to identify significant parameters. LightGBM algorithm then determines the significance of dissolved oxygen by eliminating irrelevant variables and predicting dissolved oxygen in intensive aquaculture. Finally, the attention method was implemented to map the weighting and learning parameter matrices, so enabling the BiSRU's hidden states to be assigned different weights. The findings shown that the presented prediction model can accurately anticipate the fluctuating trend of dissolved oxygen over a 10-day period in just 122 seconds, and the accuracy rate reached 96.28%. Comparing the model effects of LightGBM -BiSRU, LightGBM -GRU, LightGBM-LSTM, and BiSRU -Attention takes the least time. Its higher prediction accuracy can provide an essential reference for intensive aquaculture water quality regulation.

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Section: G Bidirectional Simple Recurrent Unit (Bisru)mentioning

confidence: 99%

A Novel Hybrid Model to Predict Dissolved Oxygen for Efficient Water Quality in Intensive Aquaculture

Liu

Hassan

et al. 2023

IEEE Access

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“…Additionally, it has better long-period acquisition and nonlinear processing capability than TCNs. The SRU network has recently been applied to complex nonlinear classification and regression problems, achieving commendable predictive results in water quality prediction [32], humidity in waterfowl breeding environments [33], remaining useful life prediction of bearings [34], and spatiotemporal traffic speed prediction in urban road networks [35].…”

Section: Introductionmentioning

confidence: 99%

Ultra-Short-Term Photovoltaic Power Generation Prediction Based on Hunter–Prey Optimized K-Nearest Neighbors and Simple Recurrent Unit

Tang,

Zhang,

Huang

et al. 2024

Applied Sciences

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In view of the current problems of complex models and insufficient data processing in ultra-short-term prediction of photovoltaic power generation, this paper proposes a photovoltaic power ultra-short-term prediction model named HPO-KNN-SRU, based on a Simple Recurrent Unit (SRU), K-Nearest Neighbors (KNN), and Hunter–Prey Optimization (HPO). Firstly, the sliding time window is determined by using the autocorrelation function (ACF), partial correlation function (PACF), and model training. The Pearson correlation coefficient method is used to filter the principal meteorological factors that affect photovoltaic power. Then, the K-Nearest Neighbors (KNN) algorithm is utilized for effective outlier detection and processing to ensure the quality of input data for the prediction model, and the Hunter–Prey Optimization (HPO) algorithm is applied to optimize the parameters of the KNN algorithm. Finally, the efficient Simple Recurrent Unit (SRU) model is used for training and prediction, with the Hunter–Prey Optimization (HPO) algorithm applied to optimize the parameters of the SRU model. Simulation experiments and extensive ablation studies using photovoltaic data from the Desert Knowledge Australia Solar Centre (DKASC) in Alice Springs, Australia, validate the effectiveness of the integrated model, the KNN outlier handling, and the HPO algorithm. Compared to the Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Simple Recurrent Unit (SRU) models, this model exhibits an average reduction of 19.63% in Mean Square Error (RMSE), 27.54% in Mean Absolute Error (MAE), and an average increase of 1.96% in coefficient of determination (R2) values.

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“…Due to the excellent performance of the deep learning framework in time series forecasting, especially in long-term forecasting, most scholars have studied the application of deep learning methods in water quality forecasting. Examples include convolutional neural network (Ta and Wei (2018)), long short-term memory (Barzegar et al (2020)), gated recurrent unit neural network (Cao et al (2020)), bidirectional simple recurrent units (Chen et al (2022)) and temporal convolutional network (Li et al (2022b)).…”

Section: Introductionmentioning

confidence: 99%

A hybrid intelligence model for predicting dissolved oxygen in aquaculture water

Yang

Sun

2023

Front. Mar. Sci.

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Dissolved oxygen is an important water quality indicator that affects the health of aquatic products in aquaculture, and its monitoring and prediction are of great significance. To improve the prediction accuracy of dissolved oxygen water quality series, a hybrid prediction model based on variational mode decomposition (VMD) and a deep belief network (DBN) optimized by an improved slime mould algorithm (SMA) is proposed in this paper. First, VMD is used to decompose the nonlinear dissolved oxygen time series into several relatively stable intrinsic mode function (IMF) subsequences with different frequency scales. Then, the SMA is improved by applying elite opposition-based learning and nonlinear convergence factors to increase its population diversity and enhance its local search and global convergence capabilities. Finally, the improved SMA is used to optimize the hyperparameters of the DBN, and the aquaculture water quality prediction VMD-ISMA-DBN model is constructed. The model is used to predict each IMF subsequence, and the ISMA optimization algorithm is used to adaptively select the optimal hyperparameters of the DBN model, and the prediction results of each IMF are accumulated to obtain the final prediction result of the dissolved oxygen time series. The dissolved oxygen data of aquaculture water from 8 marine ranches in Shandong Province, China were used to verify the prediction performance of the model. Compared with the stand-alone DBN model, the prediction performance of the model has been significantly improved, MAE and MSE have been reduced by 43.28% and 40.43% respectively, and (R2) has been increased by 8.37%. The results show that the model has higher prediction accuracy than other commonly used intelligent models (ARIMA, RF, TCN, ELM, GRU and LSTM); hence, it can provide a reference for the accurate prediction and intelligent regulation of aquaculture water quality.

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DA-Bi-SRU for water quality prediction in smart mariculture

Cited by 7 publications

References 32 publications

A Novel Hybrid Model to Predict Dissolved Oxygen for Efficient Water Quality in Intensive Aquaculture

A Novel Hybrid Model to Predict Dissolved Oxygen for Efficient Water Quality in Intensive Aquaculture

Ultra-Short-Term Photovoltaic Power Generation Prediction Based on Hunter–Prey Optimized K-Nearest Neighbors and Simple Recurrent Unit

A hybrid intelligence model for predicting dissolved oxygen in aquaculture water

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