文献类型：会议论文

英文题名：Prediction of Dissolved Oxygen Content in Aquaculture of Hyriopsis Cumingii Using Elman Neural Network

作者：Liu, Shuangyin[1,2];Yan, Mingxia[1];Tai, Haijiang[1];Xu, Longqin[2];Li, Daoliang[1]

机构：[1]China Agr Univ, Coll Elect & Informat Engn, Beijing 100083, Peoples R China;[2]Guangdong Ocean Univ, Coll Informat, Zhanjiang 524025, Guangdong, Peoples R China

会议论文集：5th International Conference on Computer and Computing Technologies in Agriculture (CCTA)

会议日期：OCT 29-31, 2011

会议地点：China Agr Univ, Beijing, PEOPLES R CHINA

主办单位：China Agr Univ

语种：英文

外文关键词：prediction; dissolved oxygen; Elman neural network; Hyriopsis Cumingii

外文摘要：Hyriopsis Cumingii is Chinese major fresh water pearl mussel, widely distributed in the southern provinces of China's large and medium-sized freshwater lakes. In the management of Hyriopsis Cumingii ponds, dissolved oxygen (DO) is the key point to measure, predict and control. In this study, we analyzes the important factors for predicting dissolved oxygen of Hyriopsis Cumingii ponds, and finally chooses solar radiation(SR), water temperature(WT), wind speed(WS), PH and oxygen(DO) as six input parameters. In this paper, Elman neural networks were used to predict and forecast quantitative characteristics of water. As the dissolved oxygen in the outdoor pond is low controllability and scalability, this paper proposes a predicting model for dissolved oxygen. The true power and advantage of this method lie in its ability to (1) represent both linear and non-linear relationships and (2) learn these relationships directly from the data being modeled. The study focuses on Singapore coastal waters. The Elman NN model is built for quick assessment and forecasting of selected water quality variables at any location in the domain of interest. Experimental results show that: Elman neural network predicting model with good fitting ability, generalization ability, and high prediction accuracy, can better predict the changes of dissolved oxygen.