Abstract
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.
Highlights
In fish farming, water quality directly determines the economic benefits of aquatic products, and dissolved oxygen is an important factor affecting water quality
To verify the predictive performance of the dissolved oxygen forecasting model based on the enhanced clustering algorithm (ECA)-Adam-radial basis function neural network (RBFNN) algorithm proposed in this paper, the traditional RBFNN prediction method was selected as well as the long short-term memory (LSTM), autoregressive integrated moving average (ARIMA), support vector regression (SVR) [47], backpropagation neural network (BPNN) [48], [49], K-MLPNN [50], [51] and SC-K-means-radial basis function (RBF) [52] methods to compare their performance in predicting the time series data of the dissolved oxygen in the fishery water (Fig. 9), (Fig. 10),and the prediction results were evaluated based on the mean absolute percentage error (MAPE), mean absolute error (MAE) and root mean square error (RMSE) [53], [54]
The selected cluster centers are more in line with the characteristics of the sample set distribution, and the hidden layer center parameters of the RBFNN can be more reasonably selected based on these cluster centers
Summary
Water quality directly determines the economic benefits of aquatic products, and dissolved oxygen is an important factor affecting water quality. To improve the prediction accuracy of RBFNN, 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. Since the central parameters of the hidden layer of a traditional RBFNN are randomly selected, such a network is susceptible to problems such as overfitting and sudden changes in the prediction results To mitigate these shortcomings, we propose a novel enhanced clustering method. The ECA-Adam-RBFNN prediction model proposed in this paper effectively solves the problem of network overfitting and sudden changes in the prediction results caused by the random selection of the number of hidden layer centers of the RBFNN. B. ANT COLONY ALGORITHM The center position of the hidden layer of the radial basis function neural network must be determined using a clustering algorithm or clustering model to solve the minimization problem (seeking the global optimal solution). When the ant colony algorithm is used for clustering, it is important to choose a reasonable value of ρ [31]
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