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, 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 R-ELM=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.