Prediction of dissolved oxygen in pond culture water based on K-means clustering and gated recurrent unit neural network

Cao, Xinkai; Liu, Yiran; Wang, Jianping; Liu, Chunhong; Duan, Qingling

doi:10.1016/j.aquaeng.2020.102122

Cited by 68 publications

(27 citation statements)

References 23 publications

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“…These parameters include water quality parameters and meteorological parameters. Many studies have been conducted at present [14][15][16][17][18][19][20][21][22][23][24].…”

Section: B Multi-parameter Predictionmentioning

confidence: 99%

“…Shi et al [22] adopted K-medoids to group the dataset into different clusters according to its characteristics in CSELM dissolved oxygen prediction model, but there exists redundant input of CSELM. Cao et al [23] presented a prediction of dissolved oxygen in pond culture based on K-means clustering and Gated Recurrent Unit (GRU) neural network. In [24], discrete wavelet transforms (DWT) with different wavelet functions are compared in denoising diel, daytime and nighttime dynamics of DO.…”

Section: B Multi-parameter Predictionmentioning

confidence: 99%

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An Enhanced Extreme Learning Machine for Dissolved Oxygen Prediction in Wireless Sensor Networks

Kuang¹,

Shi

Hua³

et al. 2020

IEEE Access

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Water quality monitoring using Wireless Sensor Networks (WSNs) is essential in aquaculture water quality management. In the field of water quality monitoring, dissolved oxygen (DO) is a key parameter, and its prediction can provide decision support for aquaculture production, thereby reducing farming risk. However, it is difficult to build a precise prediction model, and existing methods of DO prediction neglect the importance of analyzing DO content. To address this problem, this study proposes a hybrid DO prediction model, named KIG-ELM, which is composed of K-means, improved genetic algorithm (IGA), and extreme learning machine (ELM). This model is based on edge computing architecture, in which data acquisition, processing and dissolved oxygen prediction are distributed in sensing nodes, routing nodes and server respectively. Sensing technique and clustering operation are applied in the process of data acquisition and processing. Meanwhile, an optimized extreme learning machine is implemented for DO prediction. We evaluate the efficiency and accuracy of proposed prediction approach in a practical aquaculture on massive water quality data. Experimental results show that the hybrid model achieves significant prediction results and can meet the needs of practical production and management.

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“…These parameters include water quality parameters and meteorological parameters. Many studies have been conducted at present [14][15][16][17][18][19][20][21][22][23][24].…”

Section: B Multi-parameter Predictionmentioning

confidence: 99%

Section: B Multi-parameter Predictionmentioning

confidence: 99%

An Enhanced Extreme Learning Machine for Dissolved Oxygen Prediction in Wireless Sensor Networks

Kuang¹,

Shi

Hua³

et al. 2020

IEEE Access

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show abstract

“…It has fewer parameters and faster convergence speed under the condition of the same prediction accuracy as LSTM [16]. In recent years, GRU has achieved good application results in fields of time series data prediction such as meteorology [17], wind power [18], and pond aquaculture water [19]. To efficiently integrate relevant information in the context of time series data, Liu Juntao [20] and other scholars have successfully applied the bidirectional stacked simple recursive unit (Bi-S-SRU) to water quality prediction of marine aquaculture, demonstrating the feasibility of bidirectional neural network to predict water quality parameters.…”

Section: Introductionmentioning

confidence: 99%

Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model

Yan

Liu

et al. 2021

Water

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The current global water environment has been seriously damaged. The prediction of water quality parameters can provide effective reference materials for future water conditions and water quality improvement. In order to further improve the accuracy of water quality prediction and the stability and generalization ability of the model, we propose a new comprehensive deep learning water quality prediction algorithm. Firstly, the water quality data are cleaned and pretreated by isolation forest, the Lagrange interpolation method, sliding window average, and principal component analysis (PCA). Then, one-dimensional residual convolutional neural networks (1-DRCNN) and bi-directional gated recurrent units (BiGRU) are used to extract the potential local features among water quality parameters and integrate information before and after time series. Finally, a full connection layer is used to obtain the final prediction results of total nitrogen (TN), total phosphorus (TP), and potassium permanganate index (COD-Mn). Our prediction experiment was carried out according to the actual water quality data of Daheiting Reservoir, Luanxian Bridge, and Jianggezhuang at the three control sections of the Luan River in Tangshan City, Hebei Province, from 5 July 2018 to 26 March 2019. The minimum mean absolute percentage error (MAPE) of this method was 2.4866, and the coefficient of determination (R2) was able to reach 0.9431. The experimental results showed that the model proposed in this paper has higher prediction accuracy and generalization than the existing LSTM, GRU, and BiGRU models.

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“…Supervised learning is commonly used for classification and regression, where using data as a sample after trained by machine learning model which have the same target values [21]. From the theory of machine learning as well as its advantages, there are several implements in aquaculture recently such as biomass fish detection [22], size estimates [23][24][25], weight estimates [26][27][28], count [29][30][31][32], fish recognition [33][34][35][36][37][38], age detection [39,40], sex identification [34,[41][42][43], fish species classification [44][45][46][47][48][49][50], feeding behavior [51,52], group behavior [53], abnormal behavior [54,55], univariate prediction [38,[56][57][58][59], multivariate prediction [60][61][62], with the high accuracy rate.…”

Section: Introductionmentioning

confidence: 99%

Overview of Smart Aquaculture System: Focusing on Applications of Machine Learning and Computer Vision

Kim

2021

Electronics

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Smart aquaculture is nowadays one of the sustainable development trends for the aquaculture industry in intelligence and automation. Modern intelligent technologies have brought huge benefits to many fields including aquaculture to reduce labor, enhance aquaculture production, and be friendly to the environment. Machine learning is a subdivision of artificial intelligence (AI) by using trained algorithm models to recognize and learn traits from the data it watches. To date, there are several studies about applications of machine learning for smart aquaculture including measuring size, weight, grading, disease detection, and species classification. This review provides and overview of the development of smart aquaculture and intelligent technology. We summarized and collected 100 articles about machine learning in smart aquaculture from nearly 10 years about the methodology, results as well as the recent technology that should be used for development of smart aquaculture. We hope that this review will give readers interested in this field useful information.

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Prediction of dissolved oxygen in pond culture water based on K-means clustering and gated recurrent unit neural network

Cited by 68 publications

References 23 publications

An Enhanced Extreme Learning Machine for Dissolved Oxygen Prediction in Wireless Sensor Networks

An Enhanced Extreme Learning Machine for Dissolved Oxygen Prediction in Wireless Sensor Networks

Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model

Overview of Smart Aquaculture System: Focusing on Applications of Machine Learning and Computer Vision

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