Artificial Intelligence in Internet of Things System for Predicting Water Quality in Aquaculture Fishponds

作     者：Po-Yuan Yang Yu-Cheng Liao Fu-I Chou 

作者机构：Department of Intelligent RoboticsNational Pingtung UniversityPingtung900TaiwanChina Department of Electrical EngineeringNational Kaohsiung University of Science and TechnologyKaohsiung807TaiwanChina 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第46卷第9期

页      面：2861-2880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：Publication costs are funded by the Ministry of Science and Technology Taiwan under Grant Numbers MOST 110-2221-E-153-010 

主　　题：Fishery gated recurrent unit hyperparameter optimization long short-term memory Taguchi method water quality prediction 

摘      要：Aquaculture has long been a critical economic sector in *** a key factor in aquaculture production efficiency is water quality,an effective means of monitoring the dissolved oxygen content(DOC)of aquaculture water is *** study developed an internet of things system for monitoring DOC by collecting essential data related to water *** intelligence technology was used to construct a water quality prediction model for use in a complete system for managing water *** aquaculture water quality depends on a continuous interaction among multiple factors,and the current state is correlated with the previous state,a model with time series is ***,this study used recurrent neural networks(RNNs)with sequential *** used RNNs such as long short-term memory model and gated recurrent unit(GRU)model have a memory function that appropriately retains previous results for use in processing current *** construct a suitable RNN model,this study used Taguchi method to optimize hyperparameters(including hidden layer neuron count,iteration count,batch size,learning rate,and dropout ratio).Additionally,optimization performance was also compared between 5-layer and 7-layer network *** experimental results revealed that the 7-layer GRU was more suitable for the application considered in this *** values obtained in tests of prediction performance were mean absolute percentage error of 3.7134%,root mean square error of 0.0638,and R-value of ***,thewater qualitymanagement system developed in this study can quickly provide practitioners with highly accurate data,which is essential for a timely response to water quality *** study was performed in collaboration with the Taiwan Industrial Technology Research Institute and a local fishery *** application of the system by the fishery company confirmed that the monitoring system is effective in improving the surviv