Prediction of dissolved oxygen content in aquaculture using Clustering-based Softplus Extreme Learning Machine

Shi, Pei; Li, Guanghui; Yongming, Yuan; Huang, Guangyan; Kuang, Liang

doi:10.1016/j.compag.2019.01.004

Cited by 61 publications

(35 citation statements)

References 23 publications

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“…This experiment evaluated the ID-SVDD algorithm on a real water quality dataset. All data were collected from the internet of things (IOT) monitoring system running in the Nanquan breeding base located in Wuxi city, Jiangsu province [46]. This system uses various types of sensors to collect water quality data (e.g., DO, pH, and dissolved oxygen relative saturation).…”

Section: Methodsmentioning

confidence: 99%

Outlier Detection Using Improved Support Vector Data Description in Wireless Sensor Networks

Shi

Yongming

et al. 2019

Sensors

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Wireless sensor networks (WSNs) are susceptible to faults in sensor data. Outlier detection is crucial for ensuring the quality of data analysis in WSNs. This paper proposes a novel improved support vector data description method (ID-SVDD) to effectively detect outliers of sensor data. ID-SVDD utilizes the density distribution of data to compensate SVDD. The Parzen-window algorithm is applied to calculate the relative density for each data point in a data set. Meanwhile, we use Mahalanobis distance (MD) to improve the Gaussian function in Parzen-window density estimation. Through combining new relative density weight with SVDD, this approach can efficiently map the data points from sparse space to high-density space. In order to assess the outlier detection performance, the ID-SVDD algorithm was implemented on several datasets. The experimental results demonstrated that ID-SVDD achieved high performance, and could be applied in real water quality monitoring.

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Section: Methodsmentioning

confidence: 99%

Outlier Detection Using Improved Support Vector Data Description in Wireless Sensor Networks

Shi

Yongming

et al. 2019

Sensors

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“…important factors affecting the performance of DO in a River (pH, Temp) as indicated in the studies by [10], [16], [17], [27]. Generally, the average performance of the LSTM was recorded as unsatisfactory in both calibration and verification phases.…”

Section: Figure 6 Two-dimensional Taylor Diagram For Lstm Elm Grnnmentioning

confidence: 99%

Hybrid Machine Learning Ensemble Techniques for Modeling Dissolved Oxygen Concentration

et al. 2020

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The reliable prediction of dissolved oxygen concentration (DO) is significantly crucial for protecting the health of the aquatic ecosystem. The current research employed four different single AI-based models, namely long short-term memory neural network (LSTM), extreme learning machine (ELM), Hammerstein-Weiner (HW) and general regression neural network (GRNN) for modeling the DO concentration of Kinta River, Malaysia using available water quality (WQ) parameters. Afterwards, the first scenario used four different ensemble techniques (ET). Two linear, i.e. simple averaging ensemble (SAE) and weighted averaging ensemble (WAE) and two nonlinear namely; backpropagation neural network ensemble (BPNN-E) and HW ensemble (HW-E). The second scenario employed a hybrid random forest (RF) ensemble in order to enhance the prediction accuracy of the single models. The WQ parameters were subjected to a different pre-analysis test to ascertain their stability. The four-model combinations are generated using the nonlinear sensitivity input selection approach. The modeling performance was assessed using the statistical measures of Nash-Sutcliffe coefficient efficiency (NSE), Willmott's index of agreement (WI), root mean square error (RMSE), mean absolute error (MAE) and mean square error (MSE) and correlation coefficient (CC). The results of the single AI-based models demonstrated that HW (M3) served as the best model for predicting DO concentration. For ensemble results, BPNN-E (WI=0.9764) was superior to the other three ET with average decreased of more than 2% with regards to MAE. Investigation on the hybrid RF ensemble demonstrated the reliable accuracy for all the hybrid models with better predictive skill shown by the HW-RF (CC=0.981) ensemble. The overall results verified the promising impact of HW-M3, ET and hybrid RF ensemble for the prediction of the DO concentration in the Kinta River, Malaysia.

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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%

“…If the added white noise and iteration times are not appropriate, the false component will appear after decomposition. 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.…”

Section: B Multi-parameter Predictionmentioning

confidence: 99%

See 1 more Smart Citation

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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Prediction of dissolved oxygen content in aquaculture using Clustering-based Softplus Extreme Learning Machine

Cited by 61 publications

References 23 publications

Outlier Detection Using Improved Support Vector Data Description in Wireless Sensor Networks

Outlier Detection Using Improved Support Vector Data Description in Wireless Sensor Networks

Hybrid Machine Learning Ensemble Techniques for Modeling Dissolved Oxygen Concentration

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

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