Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (GRU)

作     者：Wuyan Li Hao Wu Nanyang Zhu Yongnian Jiang Jinglu Tan Ya Guo 

作者机构：Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education)School of Internet of Things EngineeringJiangnan UniversityWuxi 214122China Jiangsu Internet Agricultural Development CenterNanjing 210017China Jiangsu Zhongnong IoT Technology Co.LTDYixing 214200China University of MissouriColumbiaMO 65211USA 

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

年 卷 期：2021年第8卷第1期

页      面：185-193页

核心收录：

学科分类：08[工学] 0828[工学-农业工程] 082801[工学-农业机械化工程] 

基　　金：This project is partially supported by National Natural Science Foundation of China(No:31771680,No:51961125102,No:21706096) Fundamental Research Funds for the Central Universities of China(No:JUSRP51730A) the Modern Agriculture Funds of Jiangsu Province(No:BE2018334) the 111 Project(B12018)and the Research Funds for New Faculty of Jiangnan University 

主　　题：Dissolved oxygen RNN model LSTM model GRU model 

摘      要：Dissolved oxygen(DO),an important water quality indicator in aquaculture,affects the survival rate of aquatic creatures and the yield of aquatic ***,it is important to predict DO in fishery ponds for applying artificial aeration with low energy and ***,deep learning models,such as recurrent neural network(RNN),long short-term memory(LSTM),and gated recurrent unit(GRU),are often used to predict the trend of time series,but it is unclear which one of them is more suitable for prediction of DO in fishery *** this work,the RNN model,LSTM model,and GRU model were used to build three DO predicting *** performance of the three models were compared by mean absolute error(MAE),mean square error(MSE),mean absolute percentage error(MAPE),and the coefficient of determination(R2).The performance of RNN is worse result than LSTM and *** four evaluation indicators of GRU are 0.450 mg/L,0.411,0.054,and 0.994,and the four indicators of LSTM are 0.407 mg/L,0.294,0.059,and 0.970,which shows that the performance of GRU is similar to LSTM,but the time cost and number of parameters used for GRU is much lower than *** is concluded that the GRU has overall better performance and can be applied to practical applications.