Application of data fusion based on deep belief network in air quality monitoring

The successive development of social industrialization and modernization has caused a great many of environmental problems. Due to the imbalance between the environment and rapid development, the problem of urban air quality has become more and more prominent, so air quality monitoring has become particularly important. In air quality monitoring, unexpected situations such as damage to the monitoring equipment and the relocation of the station building will occur, resulting in the lack of monitoring data. For this lack of monitoring data, the missing values of the monitoring data are supplemented by the data fusion method using deep belief network (DBN). It can provide relevant researchers with reference data for further analysis and research on air quality. Compared with BP neural network for data fusion, DBN method is closer to the actual monitoring value. (C) 2021 The Authors. Published by Elsevier B.V.

Automated summary

The successive development of social industrialization and modernization has led to an increase in environmental problems, with urban air quality issues becoming more prominent, making air quality monitoring crucial. The deep belief network (DBN) method for data fusion can supplement missing monitoring data, providing reference data for further analysis and research on air quality. Compared to the BP neural network method, DBN is closer to actual monitoring values.