A ConvGRU-Based Visual Analysis System for Air Pollution Prediction

Forecasting the concentration of fine particulate pollutants is one of the main ways to formulate measures of anti-pollution and emission reduction. However, traditional large-scale prediction simulations must be carried on supercomputer for hours or even days. The high cost and low efficiency even affect its timeliness. In order to resolve these issues, in this paper, we proposed a convolutional gated recurrent unit (ConvGRU for short) model based fine particle air pollution prediction method, the main idea is to design a loss function for fine particle prediction, named Comprehensive Loss Function (C-Loss Function for short), both the absolute error and relative error between the prediction results and the actual values have been evaluated by our C-Loss Function; by comparing with the commonly used Mean Square Loss Function, it is proved that C-Loss can make the prediction model more suitable for fine particles; furthermore, according to the requirements from domain scientists, an interactive visual analytics system has also been designed, in this system, domain scientists can efficiently obtain a series of prediction results, so as to interactively explore the correlation between the formation process of air pollution and meteorological factors, this can offer some scientific support for further making better anti-pollution measures; finally, the effectiveness of our proposed system has been demonstrated through analysis of a series of application examples.