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

International Conference on Energy Engineering and Environmental Protection 2017

能源学;生态环境科学

Li, Xinfei^1 ; Ai, Jiaoyan^1 ; Lin, Chunhuan^1 ; Guan, Haibin^1

College of Electrical Engineering, Guangxi University, Nanning

530004, China^1

关键词: Correlation coefficient;    Different voltages;    Dissolved oxygen concentrations;    Extreme learning machine;    Intelligent Algorithms;    Oxygen distribution;    Prediction accuracy;    Prediction model;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/121/2/022003/pdf
DOI  :  10.1088/1755-1315/121/2/022003

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

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.