A hybrid intelligent method for three-dimensional short-term prediction of dissolved oxygen content in aquaculture

Chen, Yingyi; Yu, Huihui; Cheng, Yanjun; Cheng, Qianqian; Li, Bingbing

doi:10.1371/journal.pone.0192456

Cited by 21 publications

(7 citation statements)

References 35 publications

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“…However, a hybrid three-dimensional (3D) dissolved oxygen content estimation model based on a radial basis function neural network, K-means, and subtractive clustering effectively demonstrated the three-dimensional distribution in predicting changes in dissolved oxygen content on crap pools [42]. Many crayfish cannot be sold at the end of the sales period due to errors made in production planning, growth estimation, transportation, grading processes, and the application of the appropriate stock density.…”

Section: Discussionmentioning

confidence: 99%

Modeling and predicting meat yield and growth performance using morphological features of narrow-clawed crayfish with machine learning techniques

GÜLTEPE

Berber

Gültepe

2023

Preprint

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In this study, predictions of length-weight relationships and meat productivity were generated by machine learning models using measurement data of male and female crayfish in the narrow-clawed crayfish population living in Apolyont Lake. The data set was created using the growth performance and morphometric characters obtained from 1416 crayfish in different years to determine the length-weight relationship and length-meat yield. Statistical methods, artificial intelligence, and machine learning are used due to the difficulty of constructing mathematical models in multi-parameter and multivariate problems. In recent studies, artificial intelligence and machine learning methods give higher accuracy than other prediction methods in large data sets with complex structures. No previous studies have been conducted on such population parameters. The analysis results show that most of the models designed as an alternative to traditional estimation methods in future planning studies in sustainable fisheries, aquaculture, and natural sources management are valid for machine learning and artificial intelligence. Seven different machine learning algorithms were applied to the data set and the length-weight relationships and length-meat yields were evaluated for both male and female individuals. Support Vector Regression (SVR) has achieved the best prediction performance with 0.996 and 0.992 values for the length-weight of males and females, with 0.996 and 0.995 values for the length-meat yield of males and females. The results showed that the SVR outperforms the others for all scenarios regarding the accuracy, sensitivity, and specificity metrics.

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Section: Discussionmentioning

confidence: 99%

Modeling and predicting meat yield and growth performance using morphological features of narrow-clawed crayfish with machine learning techniques

GÜLTEPE

Berber

Gültepe

2023

Preprint

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“…Yueting, W et al [10] developed the Self-cleaning aquacultural water quality monitoring system that produced better effects on the sensors of mass spectrometry, but the sensors other than this sensor have the possibility to damage the sensor and reduce the life time of the sensor. Chen, Yet al [8] modelled the Hybrid three-dimensional (3D) dissolved oxygen content prediction model that identified the variation in trends and offers guidance for the aquaculture, but the lack of parameters for the improvement of accuracy is the major drawback of this method. Chandanapalli, S.B et al [15] designed the Functional tangent decision tree algorithm that offered increased accuracy in classification to obtain the quality of water, but failed to use the optimization algorithms for the improvement of accuracy in prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization Driven Distributed Deep Learning for Aqua Status Prediction in IoT

Rajeshwar¹

2023

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The continuous screening of the quality and characteristics of water in IoT (Internet of Things) is essential due to the increasing requirement on aquaculture in order to maximize the yields. There are various physicochemical parameters used in water quality monitoring, but the analysis of these parameters are needed to obtain the final decision with experts. This paper proposes an aqua status prediction model in IoT using the Fractional Gravitational Search Algorithm based distributed Deep Convolutional Neural network (FGSA-based distributed Deep CNN). Initially, the aqua parameters are analysed using the distributed IoT nodes in the aqua environment. The loss and delay in the transmission of the data related to the aqua status is controlled with the selection of cluster head optimally using the FGSA. The prediction of the aqua status is done with the distributed DeepCNN in the final step. The performance of the proposed method is analyzed with the evaluation metrics, namely accuracy, energy, and throughput. The accuracy, energy, and throughput of the proposed FGSA-based distributed Deep CNN classifier is obtained as 95.4758, 99.4293, and 99.9571, respectively, which is high as compared to the existing methods. This shows the effectiveness of the proposed method in the prediction of the aqua status.

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“…To verify the predictive performance of the dissolved oxygen forecasting model based on the ECA-Adam-RBFNN algorithm proposed in this paper, the traditional RBFNN prediction method was selected as well as the LSTM, ARIMA, SVR [48], BPNN [49], [50] , K-MLPNN [51], [52] and SC-K-means-RBF [53] methods to compare their performance in predicting the time series data of the dissolved oxygen in the fishery water ( Fig. 9), (Fig.…”

Section: Ch(k)mentioning

confidence: 99%

Radial Basis Function Neural Network Model for Dissolved Oxygen Concentration Prediction Based on an Enhanced Clustering Algorithm and Adam

Sun

Feng

2021

IEEE Access

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In fishery aquaculture, water quality directly determines the economic benefits of aquatic products, and dissolved oxygen is an important factor affecting water quality. To accurately grasp the trends of variation in dissolved oxygen, a dissolved oxygen concentration forecasting model based on an enhanced clustering algorithm and Adam with a radial basis function neural network (ECA-Adam-RBFNN) is proposed. An enhanced clustering algorithm (ECA) combining K-means with ant colony optimization is introduced in place of random selection to determine the center positions of the neural network hidden layer units. If the number of center points is too high, the neural network will be overfit, whereas if it is too low, sudden changes will appear in the results. Once the hidden layer centers have been determined, the radial basis function (RBF) width is calculated from the maximum center distance and the number of center points to avoid the two extreme cases of RBF that are too peaked or flat. The recursive least squares (RLS) algorithm is introduced to obtain the connection weights from the hidden layer to the output layer. The Adam algorithm is introduced to iteratively differentiate the objective function to adjust the center values, weights and width while adaptively varying the learning rates for these three types of parameters. Finally, the improved forecasting algorithm is applied for the prediction of the dissolved oxygen concentration in fishery aquaculture. The experimental results show that under identical conditions, compared with a long short-term memory (LSTM) network, a backpropagation neural network (BPNN), a traditional RBF neural network, a support vector regression (SVR) model, an autoregressive integrated moving average (ARIMA), K-MLPNN (K-means muhilayer perceptron neural networks), and SC-K-means-RBF model, the improved algorithm achieves significant reductions in the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) as model evaluation indicators.

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A hybrid intelligent method for three-dimensional short-term prediction of dissolved oxygen content in aquaculture

Cited by 21 publications

References 35 publications

Modeling and predicting meat yield and growth performance using morphological features of narrow-clawed crayfish with machine learning techniques

Modeling and predicting meat yield and growth performance using morphological features of narrow-clawed crayfish with machine learning techniques

Optimization Driven Distributed Deep Learning for Aqua Status Prediction in IoT

Radial Basis Function Neural Network Model for Dissolved Oxygen Concentration Prediction Based on an Enhanced Clustering Algorithm and Adam

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