A STUDY ON THE APPLICATION OF DEEP LEARNING TO AIR POLLUTION PREDICT AT INTERSECTIONS WITH CONPLEX STRUCTURE

Abstract

Though the situation of air pollution in Japan is improving in recent years, the points or limited aera with high polluted density still exist due to heavy source or enclosed space. As a result, the complaint about air pollution occurs often. In order to improve the situation of the high density points, alerts are sent out on the website or on the local electronic bulletin board to urge the drivers to refrain their car-use. However, the alert is only based on current observed values, there is no method to predict the alert in advance now.

In this study, Street Canyon by deep neural networks is used to the predict of air pollution, and validity of the predictions are examined, then the necessary input items are discussed. As a result, it was found that each input item (temperature, wind speed and direction, and periodicity of traffic volume) was associated with NO2 and the fact that broad-based input items are required for SPM predictions. It is also shown that deep learning is useful in studying air pollution predictions under Street Canyon.