A three-dimensional prediction method of dissolved oxygen in pond culture based on Attention-GRU-GBRT

Cao, Xinkai; Ren, Ni; Tian, Ganglu; Fan, Yuxing; Duan, Qingling

doi:10.1016/j.compag.2020.105955

Cited by 26 publications

(11 citation statements)

References 19 publications

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“…The higher the value of the coefficient of determination R 2 , the better the model fits the data. The specific formulae are shown in equations ( 2)- (5).…”

Section: Model Performance Metricsmentioning

confidence: 99%

“…Reference source not found.. 2 Dissolved oxygen (DO) is a key indication of water quality since it is essential to the survival of aquatic animals and is used by their metabolism [3]. Excessive or insufficient DO can affect the healthy growth of farmed fish, shrimp, and other organisms, easily resulting in disease outbreaks and even mass mortality, which would result in significant economic losses for business [4][5]. For this reason, predicting dissolved oxygen concentrations and their trends in advance, regulating dissolved oxygen concentrations in a timely manner and ensuring healthy growth of aquatic products in a comfortable environment are important for preventing water quality deterioration, reducing the risk of aquaculture and the healthy and sustainable development of intensive aquaculture [6].…”

Section: Introductionmentioning

confidence: 99%

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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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“…The higher the value of the coefficient of determination R 2 , the better the model fits the data. The specific formulae are shown in equations ( 2)- (5).…”

Section: Model Performance Metricsmentioning

confidence: 99%

Section: Introductionmentioning

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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“…The attention mechanism extracted features from diverse environmental data, yielding superior results compared to LSTM without attention. Cao et al. (2021) applied attention-LSTM to simulate DO concentrations in ponds, achieving an RMSE of 0.380 mg/L.…”

Section: Lstm With Attention Mechanismmentioning

confidence: 99%

Long short-term memory models of water quality in inland water environments

Pyo,

Pachepsky,

Kim

et al. 2023

Water Research X

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“…Among these, depth neural networks are one of the most representative machine learning methods. Flood prediction methods of deep neural networks mainly include RNN-based prediction methods [6], LSTM-based prediction methods [7], and GRU-based prediction methods [8]. The above intelligent methods mainly start from the perspective of Euclidean data regression, ignoring the important physical structure of the basin itself and the spatiotemporal characteristics of data features.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven and Knowledge-Guided Heterogeneous Graphs and Temporal Convolution Networks for Flood Forecasting

Shao

Jun

et al. 2023

Applied Sciences

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Data-driven models have been successfully applied to flood prediction. However, the nonlinearity and uncertainty of the prediction process and the possible noise or outliers in the data set will lead to incorrect results. In addition, data-driven models are only trained from available datasets and do not involve scientific principles or laws during the model training process, which may lead to predictions that do not conform to physical laws. To this end, we propose a flood prediction method based on data-driven and knowledge-guided heterogeneous graphs and temporal convolutional networks (DK-HTAN). In the data preprocessing stage, a low-rank approximate decomposition algorithm based on a time tensor was designed to interpolate the input data. Adding an attention mechanism to the heterogeneous graph module is beneficial for introducing prior knowledge. A self-attention mechanism with temporal convolutional network was introduced to dynamically calculate spatiotemporal correlation characteristics of flood data. Finally, we propose physical mechanism constraints for flood processes, adjusted and optimized data-driven models, corrected predictions that did not conform to physical mechanisms, and quantified the uncertainty of predictions. The experimental results on the Qijiang River Basin dataset show that the model has good predictive performance in terms of interval prediction index (PI), RMSE, and MAPE.

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A three-dimensional prediction method of dissolved oxygen in pond culture based on Attention-GRU-GBRT

Cited by 26 publications

References 19 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

Long short-term memory models of water quality in inland water environments

Data-Driven and Knowledge-Guided Heterogeneous Graphs and Temporal Convolution Networks for Flood Forecasting

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