A hybrid WA–CPSO-LSSVR model for dissolved oxygen content prediction in crab culture

Abstract

To increase prediction accuracy, reduce aquaculture risks and optimize water quality management in intensive aquaculture ponds, this paper proposes a hybrid dissolved oxygen content forecasting model based on wavelet analysis (WA) and least squares support vector regression (LSSVR) with an optimal improved Cauchy particle swarm optimization (CPSO) algorithm. In the modeling process, the original dissolved oxygen sequences were de-noised and decomposed into several resolution frequency signal subsets using the wavelet analysis method. Independent prediction models were developed using decomposed signals with wavelet analysis and least squares support vector regression. The independent prediction values were reconstructed to obtain the ultimate prediction results. In addition, because the kernel parameter δ and the regularization parameter γ in the LSSVR training procedure significantly influence forecasting accuracy, the Cauchy particle swarm optimization (CPSO) algorithm was used to select optimum parameter combinations for LSSVR. The proposed hybrid model was applied to predict dissolved oxygen in river crab culture ponds. Compared with traditional models, the test results of the hybrid WA–CPSO-LSSVR model demonstrate that de-noising and capturing non-stationary characteristics of dissolved oxygen signals after WA comprise a very powerful and reliable method for predicting dissolved oxygen content in intensive aquaculture accurately and quickly.