CN114414446B - Near-ground particulate matter concentration estimation method, device and equipment based on satellite remote sensing - Google Patents
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Abstract
The invention relates to a near-ground particulate matter concentration estimation method based on satellite remote sensing, which comprises the steps of obtaining satellite remote sensing data of a target monitoring area, and carrying out inversion calculation on the satellite remote sensing data to obtain aerosol optical thickness of the target monitoring area; acquiring environmental impact factor data at a target monitoring area; and taking the target aerosol optical thickness and the environmental impact factor data as inputs of a trained particulate matter concentration model to obtain a near-ground particulate matter concentration estimation result of a target monitoring area, wherein the particulate matter concentration model is built based on a mixed model LSTM-GPR of a long-short-term memory neural network and Gaussian process regression. The invention can provide regional distribution of near-ground particles in a wide range, and has wide application in aspects of pollutant monitoring, pollution event determination, pollution source analysis and regional pollutant conveying. The invention also relates to a near-ground particulate matter concentration estimation method device and equipment based on satellite remote sensing.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to a near-ground particulate matter concentration estimation method, device and equipment based on satellite remote sensing.
Background
Along with the rapid development of national economy, the urban scale is continuously enlarged, industrialization leads the social economy to develop at a high speed, and also brings about massive consumption of resources and serious damage to the atmospheric environment. Especially in recent years, regional atmospheric environmental problems are increasingly prominent. At present, a ground environment monitoring station is mainly established to monitor the concentration of particulate matters, polluted gas and the like, but the stations are often sparse and concentrated in cities, so that the spatial distribution of aerosol particles is difficult to comprehensively reflect, and the macroscopic monitoring of the concentration of the particulate matters cannot be performed.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a near-ground particulate matter concentration estimation method, device and equipment based on satellite remote sensing.
The technical scheme for solving the technical problems is as follows:
a near-ground particulate matter concentration estimation method based on satellite remote sensing, the method comprising:
acquiring satellite remote sensing data of a target monitoring area, and performing inversion calculation on the satellite remote sensing data to obtain aerosol optical thickness of the target monitoring area;
acquiring environmental impact factor data at the target monitoring area;
and taking the target aerosol optical thickness and the environmental impact factor data as inputs of a trained particle concentration model to obtain a near-ground particle concentration estimation result of the target monitoring area, wherein the particle concentration model is built based on a mixed model LSTM-GPR of a long-term memory neural network and Gaussian process regression.
On the basis of the technical scheme, the invention can be improved as follows.
Further, the step of obtaining the near-ground particulate matter concentration estimation result of the target monitoring area by using the target aerosol optical thickness and the environmental impact factor data as inputs of a trained particulate matter concentration model specifically includes:
normalizing the environmental impact factor data;
taking the environment influence factor data after normalization processing as an input variable of the long-short-period memory neural network, and taking a near-ground particulate matter concentration predicted value as an output variable;
and taking the near-ground particulate matter concentration predicted value obtained by the long-short-term memory neural network as an input variable of a GPR (general purpose computing) of a Gaussian process regression model, and taking the near-ground particulate matter concentration estimated result of the target monitoring area as an output variable of the GPR.
Further, the normalizing the environmental impact factor data specifically includes:
and normalizing the environmental impact factor data through a Min-Max function.
Further, the acquiring the environmental impact factor data at the target monitoring area specifically includes:
acquiring environmental impact factor data of the target monitoring area, wherein the environmental impact factor data comprises pollution emission impact factor data, meteorological impact factor data and geographic impact factor data;
wherein the pollution emission impact factor data includes a pollution industrial quantity, a highway distribution profile, and a population density distribution profile of the target monitored area;
the geographic impact factors include a terrain grade and a surface coverage type of the target monitored area.
Further, the particulate matter concentration model is specifically built by the following method:
establishing a mixed model LSTM-GPR based on long-short-term memory neural network and Gaussian process regression;
training LSTM by using a training data set consisting of normalized historical environmental impact factor data and actual near-ground particulate matter concentration values;
inputting the normalized historical environmental impact factor data in the training data set into the trained LSTM to obtain a near-ground particulate matter concentration predicted value of the training data set;
and taking the near-surface particulate matter concentration predicted value as input of a GPR model, taking an actual near-surface particulate matter concentration value opposite to the near-surface particulate matter concentration predicted value as output of the GPR model, and training GPR to obtain the particulate matter concentration model.
It should be appreciated that this embodiment uses Long-term memory LSTM (Long-short term memory) neural networks as the basic prediction method. Because input data of different dimensions has different characteristics, the timing characteristics in multiple data dimensions cannot be fully extracted if a single network structure is used. The method ensures that different types of data are not affected each other in the bottom network, and ensures the fusion of multidimensional data in the high-level network.
The method has the beneficial effects that: the near-ground particulate matter concentration estimation method based on satellite remote sensing comprises the steps of obtaining satellite remote sensing data of a target monitoring area, and carrying out inversion calculation on the satellite remote sensing data to obtain aerosol optical thickness of the target monitoring area; acquiring environmental impact factor data at the target monitoring area; and taking the target aerosol optical thickness and the environmental impact factor data as inputs of a trained particle concentration model to obtain a near-ground particle concentration estimation result of the target monitoring area, wherein the particle concentration model is built based on a mixed model LSTM-GPR of a long-term memory neural network and Gaussian process regression. The satellite remote sensing system can provide regional distribution of near-ground particles in a wide range, and has wide application in aspects of pollutant monitoring, pollution event determination, pollutant source analysis and regional pollutant conveying.
The invention also solves the technical problems as follows:
a near-ground particulate concentration estimation device based on satellite remote sensing, the device comprising:
the first acquisition module is used for acquiring satellite remote sensing data of a target monitoring area, and carrying out inversion calculation on the satellite remote sensing data to obtain the aerosol optical thickness of the target monitoring area;
the second acquisition module is used for acquiring environmental impact factor data at the target monitoring area;
and the estimation module is used for taking the target aerosol optical thickness and the environmental impact factor data as inputs of a trained particle concentration model to obtain a near-ground particle concentration estimation result of the target monitoring area, wherein the particle concentration model is built based on a mixed model LSTM-GPR of a long-term memory neural network and Gaussian process regression.
Further, the estimation module is specifically configured to normalize the environmental impact factor data;
taking the environment influence factor data after normalization processing as an input variable of the long-short-period memory neural network, and taking a near-ground particulate matter concentration predicted value as an output variable;
and taking the near-ground particulate matter concentration predicted value obtained by the long-short-term memory neural network as an input variable of a GPR (general purpose computing) of a Gaussian process regression model, and taking the near-ground particulate matter concentration estimated result of the target monitoring area as an output variable of the GPR.
Further, the second acquisition module is specifically configured to normalize the environmental impact factor data through a Min-Max function.
Furthermore, the present invention provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the satellite remote sensing-based near-surface particulate matter concentration estimation method according to any one of the above-mentioned aspects.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of the near-ground particulate matter concentration estimation method based on satellite remote sensing according to any one of the technical schemes when executing the program.
Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly explain the embodiments of the present invention or the drawings used in the description of the prior art, and it is obvious that the drawings described below are only some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for estimating near-ground particulate matter concentration based on satellite remote sensing according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of a near-ground particulate matter concentration estimation device based on satellite remote sensing according to another embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
As shown in fig. 1, the method for estimating the concentration of near-ground particulate matters based on satellite remote sensing according to the embodiment of the invention comprises the following steps:
110. satellite remote sensing data of a target monitoring area are obtained, inversion calculation is carried out on the satellite remote sensing data, and the aerosol optical thickness of the target monitoring area is obtained.
120. And acquiring environmental impact factor data at the target monitoring area.
130. And taking the target aerosol optical thickness and the environmental impact factor data as inputs of a trained particle concentration model to obtain a near-ground particle concentration estimation result of the target monitoring area, wherein the particle concentration model is built based on a mixed model LSTM-GPR of a long-term memory neural network and Gaussian process regression.
Further, step 130 specifically includes:
131. and normalizing the environmental impact factor data.
132. And taking the environment influence factor data after normalization processing as an input variable of the long-short-period memory neural network, and taking a near-ground particulate matter concentration predicted value as an output variable.
133. And taking the near-ground particulate matter concentration predicted value obtained by the long-short-term memory neural network as an input variable of a GPR (general purpose computing) of a Gaussian process regression model, and taking the near-ground particulate matter concentration estimated result of the target monitoring area as an output variable of the GPR.
Further, step 131 specifically includes:
and normalizing the environmental impact factor data through a Min-Max function.
Further, step 120 specifically includes:
and acquiring environmental impact factor data of the target monitoring area, wherein the environmental impact factor data comprises pollution emission impact factor data, meteorological impact factor data and geographic impact factor data.
Wherein the pollution emission impact factor data includes a pollution industrial quantity, a highway distribution profile, and a population density distribution profile of the target monitored area.
The geographic impact factors include a terrain grade and a surface coverage type of the target monitored area.
Further, the particulate matter concentration model is specifically built by the following method:
and establishing a mixed model LSTM-GPR based on long-term and short-term memory neural network and Gaussian process regression.
LSTM is trained using a training data set consisting of normalized historical environmental impact factor data and actual near-surface particulate matter concentration values.
And inputting the normalized historical environmental impact factor data in the training data set into the trained LSTM to obtain a near-ground particulate matter concentration predicted value of the training data set.
And taking the near-surface particulate matter concentration predicted value as input of a GPR model, taking an actual near-surface particulate matter concentration value opposite to the near-surface particulate matter concentration predicted value as output of the GPR model, and training GPR to obtain the particulate matter concentration model.
It should be understood that aerosol remote sensing data, especially aerosol optical thickness (Aerosol Optical Depth, AOD) reflects attenuation degree of aerosol and the like in the atmosphere to incident solar electromagnetic radiation, is widely applied to atmospheric pollution monitoring, realizes monitoring of ground particulate matter concentration from point and surface, can greatly make up for the defects of ground monitoring stations, and improves monitoring precision.
The near-ground particulate matter concentration estimation method based on satellite remote sensing provided by the embodiment comprises the steps of obtaining satellite remote sensing data of a target monitoring area, and carrying out inversion calculation on the satellite remote sensing data to obtain aerosol optical thickness of the target monitoring area; acquiring environmental impact factor data at the target monitoring area; and taking the target aerosol optical thickness and the environmental impact factor data as inputs of a trained particle concentration model to obtain a near-ground particle concentration estimation result of the target monitoring area, wherein the particle concentration model is built based on a mixed model LSTM-GPR of a long-term memory neural network and Gaussian process regression. The satellite remote sensing system can provide regional distribution of near-ground particles in a wide range, and has wide application in aspects of pollutant monitoring, pollution event determination, pollutant source analysis and regional pollutant conveying.
As shown in fig. 2, a near-ground particulate matter concentration estimation device based on satellite remote sensing includes:
the first acquisition module is used for acquiring satellite remote sensing data of a target monitoring area, and carrying out inversion calculation on the satellite remote sensing data to obtain the aerosol optical thickness of the target monitoring area.
The second acquisition module is used for acquiring environmental impact factor data at the target monitoring area;
and the estimation module is used for taking the target aerosol optical thickness and the environmental impact factor data as inputs of a trained particle concentration model to obtain a near-ground particle concentration estimation result of the target monitoring area, wherein the particle concentration model is built based on a mixed model LSTM-GPR of a long-term memory neural network and Gaussian process regression.
Further, the estimation module is specifically configured to normalize the environmental impact factor data.
And taking the environment influence factor data after normalization processing as an input variable of the long-short-period memory neural network, and taking a near-ground particulate matter concentration predicted value as an output variable.
And taking the near-ground particulate matter concentration predicted value obtained by the long-short-term memory neural network as an input variable of a GPR (general purpose computing) of a Gaussian process regression model, and taking the near-ground particulate matter concentration estimated result of the target monitoring area as an output variable of the GPR.
Further, the second acquisition module is specifically configured to normalize the environmental impact factor data through a Min-Max function.
Furthermore, the present invention provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the satellite remote sensing-based near-surface particulate matter concentration estimation method according to any one of the above-mentioned aspects.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of the near-ground particulate matter concentration estimation method based on satellite remote sensing according to any one of the technical schemes when executing the program.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium.
Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium may include content that is subject to appropriate increases and decreases as required by jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is not included as electrical carrier signals and telecommunication signals.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
The present invention is not limited to the above embodiments, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the present invention, and these modifications and substitutions are intended to be included in the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (7)
1. A near-ground particulate matter concentration estimation method based on satellite remote sensing, the method comprising:
acquiring satellite remote sensing data of a target monitoring area, and performing inversion calculation on the satellite remote sensing data to obtain aerosol optical thickness of the target monitoring area;
acquiring environmental impact factor data at the target monitoring area;
taking the optical thickness of the target aerosol and the environmental impact factor data as inputs of a trained particle concentration model to obtain a near-ground particle concentration estimation result of the target monitoring area, wherein the particle concentration model is built based on a mixed model LSTM-GPR of a long-term memory neural network and Gaussian process regression;
the method for obtaining near-ground particulate matter concentration estimation results of the target monitoring area by taking the target aerosol optical thickness and the environmental impact factor data as inputs of a trained particulate matter concentration model specifically comprises the following steps:
normalizing the environmental impact factor data;
taking the environment influence factor data after normalization processing as an input variable of the long-short-period memory neural network, and taking a near-ground particulate matter concentration predicted value as an output variable;
the near-ground particulate matter concentration predicted value obtained by the long-short-term memory neural network is used as an input variable of a Gaussian process regression model (GPR), and the near-ground particulate matter concentration estimated result of the target monitoring area is used as an output variable of the GPR;
the particulate matter concentration model is specifically built by the following method:
establishing a mixed model LSTM-GPR based on long-short-term memory neural network and Gaussian process regression;
training LSTM by using a training data set consisting of normalized historical environmental impact factor data and actual near-ground particulate matter concentration values;
inputting the normalized historical environmental impact factor data in the training data set into the trained LSTM to obtain a near-ground particulate matter concentration predicted value of the training data set;
and taking the near-surface particulate matter concentration predicted value as input of a GPR model, taking an actual near-surface particulate matter concentration value opposite to the near-surface particulate matter concentration predicted value as output of the GPR model, and training GPR to obtain the particulate matter concentration model.
2. The method for estimating the concentration of near-surface particulate matter based on satellite remote sensing according to claim 1, wherein the normalizing the environmental impact factor data specifically comprises:
and normalizing the environmental impact factor data through a Min-Max function.
3. The method for estimating a near-surface particulate matter concentration based on satellite remote sensing according to claim 1, wherein the acquiring environmental impact factor data at the target monitoring area specifically comprises:
acquiring environmental impact factor data of the target monitoring area, wherein the environmental impact factor data comprises pollution emission impact factor data, meteorological impact factor data and geographic impact factor data;
wherein the pollution emission impact factor data includes a pollution industrial quantity, a highway distribution profile, and a population density distribution profile of the target monitored area;
the geographic impact factors include a terrain grade and a surface coverage type of the target monitored area.
4. A near-ground particulate concentration estimation device based on satellite remote sensing for use in the estimation method of any one of claims 1-3, said device comprising:
the first acquisition module is used for acquiring satellite remote sensing data of a target monitoring area, and carrying out inversion calculation on the satellite remote sensing data to obtain the aerosol optical thickness of the target monitoring area;
the second acquisition module is used for acquiring environmental impact factor data at the target monitoring area;
the estimation module is used for taking the target aerosol optical thickness and the environmental impact factor data as inputs of a trained particle concentration model to obtain a near-ground particle concentration estimation result of the target monitoring area, wherein the particle concentration model is built based on a mixed model LSTM-GPR of a long-short-term memory neural network and Gaussian process regression;
the estimation module is specifically configured to normalize the environmental impact factor data;
taking the environment influence factor data after normalization processing as an input variable of the long-short-period memory neural network, and taking a near-ground particulate matter concentration predicted value as an output variable;
and taking the near-ground particulate matter concentration predicted value obtained by the long-short-term memory neural network as an input variable of a GPR (general purpose computing) of a Gaussian process regression model, and taking the near-ground particulate matter concentration estimated result of the target monitoring area as an output variable of the GPR.
5. The satellite remote sensing-based near-surface particulate matter concentration estimation device of claim 4, wherein the second acquisition module is specifically configured to normalize the environmental impact factor data by a Min-Max function.
6. A computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the satellite remote sensing based near-surface particulate matter concentration estimation method of any one of claims 1-3.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor performs the steps of the satellite remote sensing based near-surface particulate matter concentration estimation method of any one of claims 1 to 3.
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