Dissolved oxygen content prediction in crab culture using a hybrid intelligent method.
A precise predictive model is needed to obtain a clear understanding of the changing dissolved oxygen content in outdoor crab ponds, to assess how to reduce risk and to optimize water quality management. The uncertainties in the data from multiple sensors are a significant factor when building a dissolved oxygen content prediction model. To increase prediction accuracy, a new hybrid dissolved oxygen content forecasting model based on the radial basis function neural networks (RBFNN) data fusion method and a least squares support vector machine (LSSVM) with an optimal improved particle swarm optimization(IPSO) is developed. In the modelling process, the RBFNN data fusion method is used to improve information accuracy and provide more trustworthy training samples for the IPSO-LSSVM prediction model. The LSSVM is a powerful tool for achieving nonlinear dissolved oxygen content forecasting. In addition, an improved particle swarm optimization algorithm is developed to determine the optimal parameters for the LSSVM with high accuracy and generalizability. In this study, the comparison of the prediction results of different traditional models validates the effectiveness and accuracy of the proposed hybrid RBFNN-IPSO-LSSVM model for dissolved oxygen content prediction in outdoor crab ponds.
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.|Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083, P.R. China.|Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, 100083, P.R. China.
Huihui
H
Yu
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.|Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083, P.R. China.|Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, 100083, P.R. China.
Yingyi
Y
Chen
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.|Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083, P.R. China.|Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, 100083, P.R. China.
ShahbazGul
S
Hassan
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.|Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083, P.R. China.|Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, 100083, P.R. China.
Daoliang
D
Li