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

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.