Prediction model of dissolved oxygen in ponds based on ELM neural network

Dissolved oxygen in ponds is affected by many factors, and its distribution is unbalanced. In this paper, in order to improve the imbalance of dissolved oxygen distribution more effectively, the dissolved oxygen prediction model of Extreme Learning Machine (ELM) intelligent algorithm is established,...

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Bibliographic Details
Published in:IOP conference series. Earth and environmental science 2018-02, Vol.121 (2), p.22003
Main Authors: Li, Xinfei, Ai, Jiaoyan, Lin, Chunhuan, Guan, Haibin
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Correlation coefficient
Correlation coefficients
Dissolved oxygen
Electric potential
Learning algorithms
Machine learning
Neural networks
Oxygen
Ponds
Prediction models
Voltage
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Summary:Dissolved oxygen in ponds is affected by many factors, and its distribution is unbalanced. In this paper, in order to improve the imbalance of dissolved oxygen distribution more effectively, the dissolved oxygen prediction model of Extreme Learning Machine (ELM) intelligent algorithm is established, based on the method of improving dissolved oxygen distribution by artificial push flow. Select the Lake Jing of Guangxi University as the experimental area. Using the model to predict the dissolved oxygen concentration of different voltage pumps, the results show that the ELM prediction accuracy is higher than the BP algorithm, and its mean square error is MSEELM=0.0394, the correlation coefficient RELM=0.9823. The prediction results of the 24V voltage pump push flow show that the discrete prediction curve can approximate the measured values well. The model can provide the basis for the artificial improvement of the dissolved oxygen distribution decision.
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/121/2/022003