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 meas-ures of anti-pollution and emission reduction.However,traditional large-scale prediction simulations must be car-ried 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 sci-entists can efficiently obtain a series of prediction results,so as to interactively explore the correlation be-tween the formation process of air pollution and meteorological factors,this can offer some scientific sup-port 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.
air pollutionvisual analysisconvolutional gated recurrent unit(ConvGRU)
NETL
NSTL
万方数据
