A Hybrid ARIMA-GABP Model for Predicting Sea Surface Temperature

Abstract

Sea surface temperature (SST) is one of the most important parameters in air–sea interaction, and its accurate prediction is of great significance in the study of global climate change. However, SST is affected by heat flux, ocean dynamic processes, cloud coverage, and other factors, which means it contains linear and nonlinear components. Existing prediction models, especially single prediction models, cannot effectively handle these linear and nonlinear components in the meantime, degrading their accuracy concerning the prediction of SST. To remedy this weakness, this paper proposes a novel prediction model by the Lagrange multiplier method to combine the auto-regressive integrated moving average (ARIMA) model and the back propagation (BP) neural network model, where these two models have superior prediction performance for linear and nonlinear components, respectively. Moreover, the genetic algorithm is exploited to construct the genetic algorithm BP (GABP) neural network to further improve the performance of the proposed model. To verify the effectiveness of the proposed model, experiments predicting the SST based on historic time-series data are performed. The experiment results indicate that the mean absolute error (MAE) of the ARIMA-GABP model is only 0.3033 °C and the root mean square error (RMSE) is 0.3970 °C, which is better than the ARIMA model, BP neural network model, long short-term memory (LSTM) model, GABP neural network model, and ensemble empirical model decomposition BP model among various datasets. Therefore, the proposed model has superior and robust performance concerning predicting SST.

An activated variable parameter gradient‐based neural network for time‐variant constrained quadratic programming and its applications

“…Compared with the NI algorithm, gradient-based algorithms, for example, the original gradient neural network (OGNN) model, avoid the inverse of a matrix, which significantly reduces its computing complexity [16]. Because of that, gradient-based algorithms are more popular in large-scale problems [17][18][19]. For instance, the stochastic gradient descent method and its varieties are the most commonly employed to solve large-scale optimisation compared with the stochastic Newton-type algorithms.…”