Prediction of Dissolved Oxygen Content in Aquaculture of Hyriopsis Cumingii Using Elman Neural Network - Computer and Computing Technologies in Agriculture V - Part III Access content directly
Conference Papers Year : 2012

Prediction of Dissolved Oxygen Content in Aquaculture of Hyriopsis Cumingii Using Elman Neural Network

Abstract

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.
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Dates and versions

hal-01361178 , version 1 (06-09-2016)

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Attribution - CC BY 4.0

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Shuangyin Liu, Mingxia Yan, Haijiang Tai, Longqin Xu, Daoliang Li. Prediction of Dissolved Oxygen Content in Aquaculture of Hyriopsis Cumingii Using Elman Neural Network. 5th Computer and Computing Technologies in Agriculture (CCTA), Oct 2011, Beijing, China. pp.508-518, ⟨10.1007/978-3-642-27275-2_57⟩. ⟨hal-01361178⟩
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