Environmental Monitoring Prediction Based on Graph Convolution Neural Network
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Abstract: Environmental monitoring plays an important role in identifying environmental characteristics. Abnormality is related to negative effects and seriously affects human life. Many sensors can be placed in specific areas and are responsible for monitoring the environmental characteristics of specific phenomena. Sensors report their measurement results to a central system, which can carry out invitational reasoning. Therefore, the system responds to any event related to the observed phenomenon through decision-making. This paper proposes an event recognition mechanism based on sensor measurement, and gives the corresponding decision, so as to realize the real-time event recognition. The system adopts the statistical learning method of data fusion and prediction (time series regression) to effectively collect the measurement data of sensors. Fuzzy logic is used to deal with the uncertainty of derivative alarm decision. This paper introduces the application of GIS, geostationary, meta database and cart in environmental monitoring by querying survey data in meta-data database connected with GIS. The results show that the I | o value of mechanical ventilation is 36% with indoor heat source and 18% without indoor heat source.
Keyword: Convolution Neural Network; Environmental Monitoring; Prediction, Monitoring