A roadside air pollutant concentration prediction method based on reconstructed deep learning

Abstract

The invention relates to a roadside air pollutant concentration prediction method based on reconstruction deep learning. On the basis of a reconstruction deep learning method, a depth reconstruction Elman model is provided according to the time-space distribution characteristics of roadside air pollutants; according to the characteristics of a restricted Boltzmann machine, initialization of the depth reconstruction Elman model is completed by utilization of part input data of a roadside air pollutant concentration data set; the depth reconstruction Elman model is trained by adoption of a gradient descent algorithm; and, due to the characteristic mapping function of the model, a real-time roadside air pollutant concentration prediction method based on the factors, such as road network information, weather information and traffic information, can be obtained.

Description

technical field [0001] The invention relates to issues related to roadside air pollutant concentration in the field of environmental detection, and in particular to a roadside air pollutant concentration prediction method based on reconstruction deep learning. Background technique [0002] Urban pollutants are mainly produced by traffic emissions, the main pollutants are carbon monoxide CO, carbon dioxide CO2, nitrogen oxides NOx and so on. CO is a stable substance that will not react chemically with other pollutants or substances in the air, and NO can react with ozone O 3 reflect NO 2 , while NO 2 Can also be converted to NO. CO is not only poisonous, but also a greenhouse gas together with CO2, and the resulting greenhouse effect is harmful to the global environment. NO 2 Therefore, real-time prediction of roadside air pollutant concentration is of great significance for environmental management and traffic planning. [0003] Restricted by economic level and scientif...

AI Technical Summary

Problems solved by technology

[0003] Restricted by economic level and scientific research ability, my country's air quality monitoring work started relatively late. After more than 40 years of development since the 1970s, many provinces and cities in my country have established air quality monitoring systems. There is still a lot of room for improvement in the detection of roadside air pollutant concentrations

The main reasons are as follows: 1. At present, the equipment used to detect the concentration of air pollutants on the roadside is mainly an air monitoring station, which is expensive and can only be equipped with a limited number of stations in the city. and the surrounding environment are complex, the feasibility of real-time prediction of roadside air pollutant concentrations in various areas of the city through detection equipment is very low

2. Based on the low feasibility of comprehensive detection of equipment, scholars from various countries try to solve this problem through prediction methods. At present, in the research on the concentration of roadside air pollutants at home and abroad, the methods adopted are mainly divided into two categories: 1. Gaussian model and Subsequent series of line source models based on Gaussian model, this type of method needs to use different models for roads in different states, and the model accuracy for complex roads is not high; 2. Concentration of roadside pollutants based on neural network Detection, this type of method can identify the simple nonlinear relationship between the input and output data, but it has great limitations in learning the more essential feature mapping between the input and output data. Each neural network can only represent one The relationship between a pollutant and the input has great defects in real-time and mobility

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