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Particle diameter constrained PM2.5 deep learning remote sensing estimation method

A particle diameter, deep learning technology, applied in neural learning methods, chemical machine learning, scientific instruments, etc., can solve problems such as high value underestimation, low value overestimate, etc., to reduce outliers, improve robustness, and alleviate high Effects of undervaluation and undervaluation

Pending Publication Date: 2022-04-12
SUN YAT SEN UNIV
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Problems solved by technology

[0004] Although the deep learning model has achieved high estimation accuracy, it is prone to underestimation of high values ​​and overestimation of low values, and even leads to the estimated PM 2.5 concentration higher than PM 10 or less than PM 1 problems that are inconsistent with prior knowledge

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  • Particle diameter constrained PM2.5 deep learning remote sensing estimation method
  • Particle diameter constrained PM2.5 deep learning remote sensing estimation method
  • Particle diameter constrained PM2.5 deep learning remote sensing estimation method

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Embodiment Construction

[0054] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0055] In order to make the content and technical solutions of this application clearer, the relevant terms and meanings are explained:

[0056] PM 1 : A general term for solid particles or liquid droplets with an aerodynamic equivalent diameter less than or equal to 1 micron in the ambient air, also known as particulate matter that can enter the lungs.

[0057] PM 2.5 : Generally refers to fine particles, which means particles with an aerodynamic equivalent diameter less than or equal to 2.5 microns in the ambient air.

[0058] PM 10 : Usually refers to the particulat...

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Abstract

The invention discloses a particle diameter constrained PM2.5 deep learning remote sensing estimation method, which comprises the following steps of: constructing a deep neural network model based on satellite remote sensing aerosol optical thickness (AOD), reanalysis data, ground station observation data and the like, and establishing constraint conditions by utilizing a priori relationship (namely PM2.5 concentration is more than or equal to PM1 concentration and less than or equal to PM10 concentration) of particle diameters of PM1, PM2.5 and PM10; and on the basis of the constructed constraint term of the deep neural network loss function, the PM2.5 deep learning remote sensing estimation method with the particle diameter constraint is provided. According to the method, the phenomena of high-value underestimation and low-value overestimation in deep learning PM2.5 estimation can be relieved, the precision of PM2.5 remote sensing estimation is improved, and the method can be widely applied to the technical field of PM2.5 concentration remote sensing estimation.

Description

technical field [0001] The present invention relates to PM 2.5 Concentration remote sensing estimation technology field, especially a particle diameter constrained PM 2.5 Deep Learning Remote Sensing Estimation Method. Background technique [0002] Fine particulate matter (PM 2.5 ) refers to particulate matter with a dynamic diameter of 2.5 microns or less in the ambient air. Live in high concentrations of PM for a long time 2.5 In such an environment, people's respiratory system, cardiovascular system, and nervous system will be greatly harmed. Therefore, carry out PM 2.5 Dynamic monitoring research is of great significance. At present, my country's ambient air automatic monitoring stations are sparsely distributed, unable to accurately and effectively monitor PM in a large area. 2.5 concentration. Therefore, using satellite remote sensing data to estimate large-scale PM 2.5 The concentration has important academic significance and application value. [0003] PM b...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16C20/20G16C20/70G06N3/04G06N3/08G01N15/06
Inventor 李同文阴顺超程晓吴金橄王天星
Owner SUN YAT SEN UNIV
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