Application of data fusion based on deep belief network in air quality monitoring
Yuanjing Ma, Junshuang Li, Ruifeng Guo
Abstract
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.