Abstract:
Aiming at the problem of low prediction accuracy caused by the nonlinearity and instability of dissolved oxygen concentration series in rivers, a prediction model based on the fusion of two-level decomposition technology and improved neural network is proposed in this paper. Firstly, the complete ensemble empirical mode decomposition of adaptive noise is introduced to decompose the dissolved oxygen time series data. The permutation entropy of each Intrinsic Mode Function (IMF) after decomposition is calculated to quantify the complexity of the sequence, and the variational mode decomposition is used to decompose the IMF with higher entropy. The nonlinearity and instability of the sequence are further weakened to ensure the prediction accuracy. Then, sparrow search algorithm is used to optimize the weights and thresholds of the neural network, and each component is predicted. Finally, the final prediction result is obtained by reconstructing the prediction results of each component. The experimental results show that the average absolute error of the proposed model is 0.091, The root mean square error is 0.14, The average absolute percentage error was 0.96%, The coefficient of determination is 0.948, which is better than other prediction models.