A hybrid model for dissolved oxygen prediction in aquaculture based on multi-scale features

作     者：Chen Li Zhenbo Li Jing Wu Ling Zhu Jun Yue 

作者机构：College of Information and Electrical EngineeringChina Agricultural UniversityBeijing 100083China College of Information and Electrical EngineeringLudong UniversityYantaiShandong 264025China 

出 版 物：《Information Processing in Agriculture》 (农业信息处理（英文）)

年 卷 期：2018年第5卷第1期

页      面：11-20页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：This research is financially supported by National Natural Science Foundation Project of China(61471133)—"The research on combination model for water quality prediction in aquaculture based on dynamic multi-scale analysis" 

主　　题：DO prediction Aquaculture Hybrid model 

摘      要：To increase prediction accuracy of dissolved oxygen(DO)in aquaculture,a hybrid model based on multi-scale features using ensemble empirical mode decomposition(EEMD)is ***,original DO datasets are decomposed by EEMD and we get several ***,these components are used to reconstruct four terms including high frequency term,intermediate frequency term,low frequency term and trend ***,according to the characteristics of high and intermediate frequency terms,which fluctuate violently,the least squares support vector machine(LSSVR)is used to predict the two *** fluctuation of low frequency term is gentle and periodic,so it can be modeled by BP neural network with an optimal mind evolutionary computation(MEC-BP).Then,the trend term is predicted using grey model(GM)because it is nearly ***,the prediction values of DO datasets are calculated by the sum of the forecasting values of all *** experimental results demonstrate that our hybrid model outperforms EEMD-ELM(extreme learning machine based on EEMD),EEMD-BP and MEC-BP models based on the mean absolute error(MAE),mean absolute percentage error(MAPE),mean square error(MSE)and root mean square error(RMSE).Our hybrid model is proven to be an effective approach to predict aquaculture DO.