Air quality monitoring and analysis with dynamic training using deep learning

Abstract

Time series prediction is a challenging predictive modeling case. It is essential to have a prediction model that can adapt to dynamic data. Air quality data show a significant changing degree of spatial and temporal data. Therefore, the updated deep learning model is suitable for this case. In this paper, monitoring and analysis of air quality with dynamic training using recurrent neural network (RNN) are proposed to provide the model remains up-to-date as new data comes. In the experiments, by adjusting the model, the accuracy is enhanced. The scheduling retrained model is provided based on the expected mean absolute percentage error (MAPE) value. First, the machine learning architecture environment is being prepared. Secondly, the RNN parameters were optimized for excellent level predictive precision. Third, set and test the scheduling and MAPE value based on the MAPE’s expected value for the automatic retraining model. Finally, on the interactive map, the output is presented using R and Shiny to visualize the RNN training results.