CN114707344A - Method for calculating thickness of permafrost movable layer based on system dynamics - Google Patents
Method for calculating thickness of permafrost movable layer based on system dynamics Download PDFInfo
- Publication number
- CN114707344A CN114707344A CN202210399262.4A CN202210399262A CN114707344A CN 114707344 A CN114707344 A CN 114707344A CN 202210399262 A CN202210399262 A CN 202210399262A CN 114707344 A CN114707344 A CN 114707344A
- Authority
- CN
- China
- Prior art keywords
- data
- thickness
- system dynamics
- factors
- permafrost
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000004364 calculation method Methods 0.000 claims abstract description 11
- 230000001364 causal effect Effects 0.000 claims abstract description 10
- 238000009826 distribution Methods 0.000 claims abstract description 7
- 239000002689 soil Substances 0.000 claims description 52
- 238000010586 diagram Methods 0.000 claims description 24
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 20
- 230000008859 change Effects 0.000 claims description 17
- 238000001556 precipitation Methods 0.000 claims description 14
- 230000008014 freezing Effects 0.000 claims description 12
- 238000007710 freezing Methods 0.000 claims description 12
- 230000008018 melting Effects 0.000 claims description 12
- 238000002844 melting Methods 0.000 claims description 12
- 230000000694 effects Effects 0.000 claims description 9
- 238000004140 cleaning Methods 0.000 claims description 7
- 238000010219 correlation analysis Methods 0.000 claims description 6
- 230000005855 radiation Effects 0.000 claims description 6
- 230000009193 crawling Effects 0.000 claims description 5
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 238000013076 uncertainty analysis Methods 0.000 claims description 3
- 238000004088 simulation Methods 0.000 abstract description 8
- 238000004458 analytical method Methods 0.000 abstract description 6
- 238000005516 engineering process Methods 0.000 abstract description 5
- 238000010276 construction Methods 0.000 abstract description 4
- 238000007418 data mining Methods 0.000 abstract description 3
- 238000011160 research Methods 0.000 description 7
- 238000004590 computer program Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000012216 screening Methods 0.000 description 4
- 239000005431 greenhouse gas Substances 0.000 description 3
- 238000000611 regression analysis Methods 0.000 description 3
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 229910052799 carbon Inorganic materials 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000010257 thawing Methods 0.000 description 2
- 241000132092 Aster Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004177 carbon cycle Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000012821 model calculation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012314 multivariate regression analysis Methods 0.000 description 1
- 229910052757 nitrogen Inorganic materials 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
- 238000010792 warming Methods 0.000 description 1
- 238000005406 washing Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a permafrost movable layer thickness calculation method based on system dynamics, which belongs to the field of geology and comprises 5 steps of influence factor identification, correlation relationship establishment among factors, system dynamics model construction, dynamic data exchange and ALT space-time distribution analysis. The influence factor identification is to identify and screen out ALT main influence factors by using a big data mining technology; the establishment of the correlation among the factors is to determine the correlation among the influencing factors; the system dynamics model is constructed by utilizing a system dynamics method and constructing an ALT prediction model based on a causal feedback relation; the dynamic data exchange is to match the SD simulation result with the spatial information in the ArcGIS by using a dynamic data exchange technology; and the ALT space-time distribution analysis is to present the result after dynamic data exchange on the space, so as to be convenient for future engineering application.
Description
Technical Field
The invention relates to the field of geology, in particular to a method for calculating the thickness of a permafrost moving layer based on system dynamics.
Background
Climate change causes great disturbance to the ecological balance of the global freezing circle, hydrological processes, energy exchange and carbon cycle. The permafrost active layer is used as the most active area for water heat exchange in the freezing ring and is extremely sensitive to environmental changes. The climate change may affect the original freeze-thaw balance of the Thickness of the permafrost Active Layer (ALT), and then induce secondary disasters such as thaw mud flow, thermal thawing of lakes and ponds, and threaten the ecological environment safety of the freezing ring. Meanwhile, the frozen soil layer of the Qinghai-Tibet plateau contains a large amount of greenhouse gases. The ALT freezing and thawing alternation frequency is increased due to climate warming, and the decomposition rate of organic carbon and nitrogen in soil can be changed, so that the greenhouse gas emission amount and the emission rule are influenced. Once the greenhouse gases are discharged into the atmosphere, a series of climate problems are brought, and the realization of the strategic target of 'double carbon' in China is greatly influenced. ALT is influenced by multiple factors, and due to the lack of a large amount of basic data and the large area of the Qinghai-Tibet plateau, the significant spatial heterogeneity exists among the multiple factors, so that the existing prediction simulation is difficult to accurately predict the ALT. On the other hand, the parameter setting of various prediction models is complex, the data demand is large, the model calculation complexity is high, and the application to engineering practice is difficult
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a permafrost movable layer thickness calculation method based on system dynamics.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
a permafrost movable layer thickness calculation method based on system dynamics comprises the following steps:
s1, acquiring permafrost related data, extracting key influence factors, and establishing a frozen soil active layer thickness database by using the extracted key influence factors;
s2, performing correlation analysis on the extracted key influence factors and the actually measured thickness value of the frozen soil active layer, selecting the key influence factors according to the correlation to establish a system dynamic model, and determining the correlation among the selected key influence factors;
s3, constructing a frozen soil activity layer thickness influence factor causal loop diagram and a stock flow diagram according to the constructed system dynamics model and the correlation between the key influence factors, bringing the causal loop diagram and the stock flow diagram into the constructed system dynamics model to obtain a frozen soil activity layer thickness prediction value, and carrying out uncertainty analysis on a prediction result;
and S4, interacting the system dynamics model data and the ArcGIS data by utilizing dynamic data exchange to obtain the time-space distribution of the thickness value of the frozen soil active layer.
Further, the extracting of the key influencing factors in S1 specifically includes the following steps:
s11, compiling data conversion codes by utilizing a Python language to convert the multivariate data in the frozen soil related data into text data and storing the text data in a frozen soil active layer thickness text information base;
s12, crawling, cleaning and sorting the data in the frozen soil movable layer thickness text information base by using Python language;
and S13, merging synonyms and deleting the word frequency which cannot be quantified and is difficult to operate, so as to obtain key influence factors.
Further, the key influencing factors comprise longitude, latitude, short wave radiation, long wave radiation, precipitation, near-ground air pressure, near-ground air specific humidity, near-ground air temperature, near-ground full wind speed, vegetation index and elevation.
Further, the correlation analysis in S2 is calculated by using a Stefan equation, and the specific calculation method is as follows:
wherein: z represents the thickness of the active layer, K represents the thermal conductivity of the soil, QLRepresents the latent heat change caused by the phase change of soil moisture and QL＝Lρ(ω-ωu) L represents the latent heat of ice melting, rho is the dry volume weight of the soil, omega is the total water content, omega isuThe water content of unfrozen water, the freezing index of DDF and the melting index of DDT.
Further, the correlation between the selected key influencing factors in S2 is expressed as:
ω＝0.024P+0.001T+0.0006ALT+30.316
ωu＝0.327P+0.0009T+0.003ALT+0.024Veg+2.085
Veg＝0.018T+0.0002DEM+0.005ALT-5.477
P＝0.079T+0.0007DEM-24.88
DDD＝-0.88DEM-0.98P-0.99T+4674.11
DDT＝-0.99DEM-1.01P-0.94T+4660.61
wherein ,ωuThe water content of unfrozen water is represented, the DDF represents a freezing index, the DDT represents a melting index, the P represents precipitation, the T represents the near-surface air temperature, the ALT represents the thickness of a permafrost soil moving layer, the Veg represents a vegetation index, and the DEM represents the altitude.
Further, the specific manner of dynamic data exchange in S4 is as follows:
s41, importing the thickness data of the frozen soil moving layer with the spatial information in the ArcGIS into Excel to provide the spatio-temporal information for a system dynamics model;
s42, the thickness data of the frozen soil moving layer with the time sequence calculated by the system dynamics model is transmitted back to ArcGIS, so that the ArcGIS has the time sequence dynamic change characteristic
The invention has the following beneficial effects:
rapidly identifying and screening out main influence factors with ALT from massive related researches; the problem of high-order and nonlinear relation generation possibly existing among ALT influence factors is solved by using a system dynamics method; data transmission is carried out by utilizing the feedback relation among the factors of the SD model, so that the data demand is greatly reduced; by utilizing dynamic data exchange, the simulation result can be mutually transmitted between the SD model and the ArcGIS; the model parameters are simple to set, the required data volume is small and easy to obtain, and the model is easy to operate and easy to operate; the model has an open operating environment, and is convenient for secondary development.
Drawings
FIG. 1 is a flow chart of the calculation of thickness of a permafrost active layer based on system dynamics.
Fig. 2 is a road map of ALT major influencing factor screening in an embodiment of the present invention.
FIG. 3 is a diagram of the associated structural framework of SD and ArcGIS according to the embodiment of the present invention.
Fig. 4 is a fitting curve of the predicted value and the actual value of the active layer according to the embodiment of the present invention.
FIG. 5 is a causal circuit diagram of the system of an embodiment of the present invention.
FIG. 6 is a system inventory flow chart of an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
A method for calculating the thickness of a permafrost active layer based on system dynamics is shown in figure 1 and comprises the following steps:
s1, acquiring permafrost related data, extracting key influence factors, and establishing a frozen soil active layer thickness database by using the extracted key influence factors;
influence factor identification and extraction is the retrieval of the original text associated with ALT from the network or existing material. And screening out influence factors which are ahead of proportion after cleaning and data arrangement for statistical analysis, finally determining the main influence factors of ALT, collecting a large amount of permafrost related data in a network and an existing text based on a big data mining technology, mining useful information which is rarely noticed but valuable before, and drawing out the useful information from a huge information base and establishing an ALT database. The method comprises the steps of compiling data conversion codes by utilizing a Python language, converting metadata in an ALT database into text data, storing the text data in an ALT text information base, crawling, cleaning and sorting the data in the text information base by utilizing the Python language, deleting word frequencies which are difficult to quantify and operate by combining synonyms, and obtaining key influence factors.
The method for extracting the key influence factors specifically comprises the following steps:
s11, compiling data conversion codes by utilizing a Python language to convert multivariate data in the frozen soil related data into text data and storing the text data in a frozen soil movable layer thickness text information base;
and S12, crawling, cleaning and sorting the data in the frozen soil movable layer thickness text information base by using Python language.
And S13, merging synonyms and deleting the word frequency which cannot be quantified and is difficult to operate, so as to obtain key influence factors.
The invention selects Python2.7.6 as the big data analysis technology base for data mining, cleaning and sorting, as shown in FIG. 2. The database is constructed by collecting and organizing the relevant data. Then, the database is processed, and various data in the database are unified into a text format and stored in an ALT text information base for later use. And then taking the ALT main influence factors as a target, crawling the ALT main influence factors in the database by utilizing Python again, and cleaning and sorting the obtained result because the obtained result possibly comprises a large number of influence factors which do not accord with the actual condition, have the same meaning and are difficult to quantify at the present stage. Finally, 21,022 pieces of data were obtained after washing and sorting. Wherein, the word frequency of the influencing factors accounting for more than 90 percent is mainly as follows: longitude, latitude, short wave radiation, long wave radiation, precipitation, near-ground air pressure, near-ground air specific humidity, near-ground air temperature, near-ground full wind speed, vegetation index, and altitude.
S2, performing correlation analysis on the extracted key influence factors and the actually measured thickness value of the frozen soil active layer, selecting the key influence factors according to the correlation to establish a system dynamic model, and determining the correlation among the selected key influence factors;
in this embodiment, correlation analysis is performed on the identified factors and the measured ALT value, the factors with higher correlation are retained for SD model construction, and meanwhile, the correlation between the factors is determined by using a geostatistical method.
The invention utilizes SAS 9.2 software to carry out multiple linear regression analysis, and the result shows that the influence of variable precipitation, near-ground air pressure, near-ground air temperature, vegetation index and elevation on ALT is obvious. Subsequently, multivariate regression analysis is further performed on the 5 selected factors to check whether multiple collinearity exists among the influencing factors, and the 5 factor data are analyzed by being substituted into SAS 9.2, so that the model has significance as a whole (F is 37.11, P is less than 0.0001), the fitting degree is 40% (R2 is 0.40), but severe collinearity exists among the variables (VIF is 11.99 and more than 10).
TABLE 1 multiple regression analysis of influence factors on ALT
Note: p < 0.05; p < 0.01; p < 0.001.
The results are shown in table 1, where the regression model as a whole has significance, but there is a high degree of co-linearity between its variables. Therefore, the influence factors are screened again by adopting stepwise regression analysis, after the near-ground air pressure factor is removed, the collinearity among the variables is obviously reduced, and the influence effects of the residual factors on ALT are that precipitation is greater than altitude, vegetation is greater than near-ground air temperature in sequence. Therefore, the external influence factors of the model adopt 4 factors of precipitation, elevation, vegetation and near-ground air temperature.
Based on the results, a stepwise regression bi-directional variable screening method is adopted, and when a new variable is introduced, all introduced variables are tested and evaluated, and variables with insignificant contribution are removed, so that the problem of co-linearity among the variables is solved. Inclusion criterion α is 0.05, exclusion criterion α is 0.10, and P <0.05 is considered statistically significant. The stepwise regression results show that when the variable near-ground air pressure does not enter the equation, the precipitation, near-ground air temperature, vegetation and altitude have significant influence on ALT, the difference has statistical significance (P is less than 0.05), and the influence effects are as follows in sequence: precipitation > elevation > vegetation > near-surface air temperature, as shown in table 2.
TABLE 2 results of stepwise regression analysis of influence factors on ALT
Note: p < 0.05; p < 0.01; p < 0.001.
The ALT is positively predicted by the precipitation and the vegetation index, namely the greater the precipitation, the higher the vegetation index and the larger the thickness of the movable layer; the near-ground air temperature and the altitude play a negative prediction role in ALT, and the lower the temperature is, the lower the altitude is, and the larger the thickness of the active layer is. The results show that the regression model has significant significance as a whole, but the variables are highly collinear. Therefore, 4 factors of precipitation, altitude, vegetation and near-ground air temperature are selected for data input.
The internal influence factors are related parameters in a Stefan equation, and the related parameters are respectively as follows: the soil freezing rate, the soil melting rate, the soil freezing index, the soil melting index, the latent heat variation, the total water content, the water content of unfrozen water, the soil heat conductivity coefficient, the soil dry volume weight and the ice melting latent heat. And the Stefan equation is used as a basic skeleton of the model to link external influencing factors and extend outwards to link other relevant factors.
The relevant relation among the factors is established by taking a Stefan equation as a basic skeleton of a model for fitting an equation by connecting the influencing factors and extending outwards to connect other relevant factors, wherein the Stefan equation is expressed as follows:
in the formula: z represents the thickness (m) of the active layer, K represents the thermal conductivity (W.m) of the soil-1·℃-1)，QLRepresenting the change in latent heat, Q, caused by a phase change in soil moistureL＝Lρ(ω-ωu) L represents the latent heat of ice melting (3.3X 10)-5J·kg-1) Rho is the dry volume weight (kg. m) of the soil3) Omega is total water content (%), omega isuThe water content of unfrozen water, the freezing index (DEG C. d) of DDF, and the melting index (DEG C. d) of DDT.
Other feedback relations in the system are fitted by using the geostatistical data, and the main variable equation in the model is as follows:
ω＝0.024P+0.001T+0.0006ALT+30.316
Veg＝0.018T+0.0002DEM+0.005ALT-5.477
P＝0.079T+0.0007DEM-24.88
DDF＝-0.88DEM-0.98P-0.99T+4674.11
DDT＝-0.99DEM-1.01P-0.94T+4660.61
in the fitting equation above: omega denotes the total water content, omegauThe water content of unfrozen water is represented, the DDF represents a freezing index, the DDT represents a melting index, the P represents precipitation, the T represents the near-surface air temperature, the ALT represents the thickness of a permafrost soil moving layer, the Veg represents a vegetation index, and the DEM represents the altitude.
Substituting the data related to the relational expression into a system dynamics model, wherein the meteorological data is a national Qinghai-Tibet plateau scientific data center meteorological data set; the stratigraphic lithology data come from the national republic of China 1: 100 ten thousand geological vector maps "; the soil geological parameters are from the book 1-4 of "principles of permatology" compiled by permatists in Shao Fu; the vegetation data is from Google Earth search engine (https:// earth. google.com /), and the resolution is 50 m; elevation data is derived from a 30m resolution ASTER GDEM downloaded in the geospatial data cloud.
S3, constructing a frozen soil activity layer thickness influence factor causal loop diagram and a stock flow diagram according to the constructed system dynamics model and the correlation between the key influence factors, bringing the causal loop diagram and the stock flow diagram into the constructed system dynamics model to obtain a frozen soil activity layer thickness prediction value, and carrying out uncertainty analysis on a prediction result;
in the embodiment, an ALT influence factor causal loop map and a stock flow map are constructed according to the ALT main influence factors screened in the earlier stage and the feedback relationship among the factors, and then an ALT calculation model is constructed by substituting the related relationship among the factors in the previous step for measuring and calculating the ALT in the research area.
The SD model is constructed by drawing a causal loop diagram shown in figure 5 by combining with the feedback relationship between factors on the basis of identifying ALT main influence factors and determining the correlation relationship between the factors, building a basic framework of the SD model, and completing the drawing of a stock flow diagram, as shown in figure 6, and setting parameters in the model as shown in table 3. And finally, predicting the thickness of the frozen soil moving layer by means of system dynamics software Vensim, and carrying out uncertain analysis on a prediction result.
TABLE 3 model variable types
And S4, interacting the system dynamics model data and the ArcGIS data by utilizing dynamic data exchange to obtain the time-space distribution of the thickness value of the frozen soil active layer.
Specifically, the SD model will have time information because the simulation results are the integral of the rate change of the inventory over time, as in the equation
The obtained result is shown to have a time sequence, and the change situation of the whole system from a certain moment can be reflected. The ArcGIS can simultaneously have space inversion and temporal simulation characteristics through dynamic data exchange, and the characteristics have the advantages that the traditional ArcGIS belongs to static inversion, namely: the ArcGIS data at a certain time point are given, the result of the certain time point is inverted, the result is static, no time sequence exists, and the ALT change rule is difficult to be effectively analyzed; and the new characteristic given after the dynamic data exchange can enable ArcGIS to continuously and stably output the inversion result, so that the dynamic change condition of the ALT in the research area is reflected more visually, research and engineering construction personnel can analyze and judge the ALT change rule of the research area conveniently, and the short-term prediction of the ALT in the research area is realized.
The seamless connection of the simulation result between the SD model and the ArcGIS is realized by utilizing dynamic data exchange, so that the simulation result is presented in space, and the specific mode is as follows:
s41, importing the thickness data of the frozen soil moving layer with the spatial information in the ArcGIS into Excel to provide the spatio-temporal information for a system dynamics model;
and S42, transmitting the thickness data of the frozen soil active layer with the time sequence calculated by the system dynamics model back to the ArcGIS, so that the ArcGIS has the time sequence dynamic change characteristic.
The dynamic data exchange is used as a hub for data transmission between the ArcGIS and SD models by using a dynamic data exchange technology, so that data can flow from the ArcGIS to the SD and from the SD to the ArcGIS, and the data exchange is used as a compatible common interface by using Excel, and an associated framework is shown in fig. 3. And importing the spatial data in the ArcGIS into Excel to provide spatial information for the SD, and transmitting the ALT analog value with a time sequence back to the Excel for storage and transmitting to the ArcGIS through analog calculation by the SD so that the ArcGIS has the characteristic of time sequence dynamic change.
The ALT space-time distribution analysis is based on SD model output information, and is related to ArcGIS data and semantics through the SD model. Specifically, firstly, a research area needs to be subjected to grid division, the resolution is 500m × 500m, grid information is imported into Excel from ArcGIS through DDE, and coordinate points are used for matching with a data set. And then sequentially substituting the frozen soil information in the data set into the SD model according to the grid point number for calculation, and recording the calculation result into the data set according to the number sequence to complete the construction of the ALT space data set. Interpolation is carried out on data among the grid points by adopting a Kriging method, and ground feature information is labeled by comparing with Google Earth, so that an SD model simulation result is presented in space, and analysis on time-space distribution characteristics and causes of ALT is facilitated. At present, the model is applied to ALT calculation for years along the Qinghai-Tibet railway, the average relative error is 4.2%, the mean square error is 0.079, and the model has strong mobility and robustness, as shown in FIG. 4 and Table 4.
TABLE 4 System validation test results
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art, having the benefit of this disclosure, may effect numerous modifications thereto and changes may be made without departing from the scope of the invention in its aspects.
Claims (6)
1. A permafrost movable layer thickness calculation method based on system dynamics is characterized by comprising the following steps:
s1, acquiring permafrost related data, extracting key influence factors, and establishing a frozen soil active layer thickness database by using the extracted key influence factors;
s2, performing correlation analysis on the extracted key influence factors and the actually measured thickness value of the frozen soil active layer, selecting the key influence factors according to the correlation to establish a system dynamic model, and determining the correlation among the selected key influence factors;
s3, constructing a frozen soil activity layer thickness influence factor causal loop diagram and a stock flow diagram according to the constructed system dynamics model and the correlation between the key influence factors, bringing the causal loop diagram and the stock flow diagram into the constructed system dynamics model to obtain a frozen soil activity layer thickness prediction value, and carrying out uncertainty analysis on a prediction result;
and S4, interacting the system dynamics model data and the ArcGIS data by utilizing dynamic data exchange to obtain the time-space distribution of the thickness value of the frozen soil active layer.
2. The method for calculating the thickness of the permafrost active layer based on system dynamics as claimed in claim 1, wherein the step of extracting the key influencing factors in S1 specifically comprises the following steps:
s11, compiling data conversion codes by utilizing a Python language to convert multivariate data in the frozen soil related data into text data and storing the text data in a frozen soil movable layer thickness text information base;
s12, crawling, cleaning and sorting the data in the frozen soil movable layer thickness text information base by using Python language;
and S13, merging synonyms and deleting the word frequency which cannot be quantified and is difficult to operate, so as to obtain key influence factors.
3. The method of claim 2, wherein the key influencing factors include longitude, latitude, short wave radiation, long wave radiation, precipitation, near-ground air pressure, near-ground air specific humidity, near-ground air temperature, near-ground full wind speed, vegetation index, and altitude.
4. The method for calculating the thickness of the permafrost active layer based on system dynamics as claimed in claim 1, wherein the correlation analysis in S2 is calculated by using a Stefan equation in a specific manner:
wherein Z represents the thickness of the active layer, K represents the thermal conductivity of the soil, QLRepresents the latent heat change caused by the phase change of soil moisture and QL＝Lρ(ω-ωu) L represents the latent heat of ice melting, rho is the dry volume weight of the soil, omega is the total water content, omega isuThe water content of unfrozen water, the freezing index of DDF and the melting index of DDT.
5. The method for calculating thickness of permafrost active layer based on system dynamics as claimed in claim 1, wherein the correlation between the selected key influencing factors in S2 is expressed as:
ω＝0.024P+0.001T+0.0006ALT+30.316
ωu＝0.327P+0.0009T+0.003ALT+0.024Veg+2.085
Veg＝0.018T+0.0002DEM+0.005ALT-5.477
P＝0.079T+0.0007DEM-24.88
DDF＝-0.88DEM-0.98P-0.99T+4674.11
DDT＝-0.99DEM-1.01P-0.94T+4660.61
wherein ,ωuThe water content of unfrozen water is represented, the DDF represents a freezing index, the DDT represents a melting index, the P represents precipitation, the T represents the near-surface air temperature, the ALT represents the thickness of a permafrost soil moving layer, the Veg represents a vegetation index, and the DEM represents the altitude.
6. The method for calculating the thickness of the permafrost layer based on the system dynamics as claimed in claim 1, wherein the specific manner of the dynamic data exchange in S4 is as follows:
s41, importing the thickness data of the frozen soil moving layer with the spatial information in the ArcGIS into Excel to provide the spatio-temporal information for a system dynamics model;
and S42, returning the thickness data of the frozen soil moving layer with the time sequence calculated by the system dynamics model to ArcGIS, so that the ArcGIS has the time sequence dynamic change characteristic.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210399262.4A CN114707344B (en) | 2022-04-15 | 2022-04-15 | Permafrost active layer thickness calculation method based on system dynamics |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210399262.4A CN114707344B (en) | 2022-04-15 | 2022-04-15 | Permafrost active layer thickness calculation method based on system dynamics |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114707344A true CN114707344A (en) | 2022-07-05 |
| CN114707344B CN114707344B (en) | 2023-09-19 |
Family
ID=82174047
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210399262.4A Active CN114707344B (en) | 2022-04-15 | 2022-04-15 | Permafrost active layer thickness calculation method based on system dynamics |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114707344B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115540788A (en) * | 2022-11-08 | 2022-12-30 | 中南大学 | Method for estimating thickness of permafrost movable layer |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5867516A (en) * | 1996-03-12 | 1999-02-02 | Hewlett-Packard Company | Vertical cavity surface emitting laser with reduced turn-on jitter and increased single-mode output |
| US7138156B1 (en) * | 2000-09-26 | 2006-11-21 | Myrick Michael L | Filter design algorithm for multi-variate optical computing |
| CN101418565A (en) * | 2007-10-23 | 2009-04-29 | 中铁第一勘察设计院集团有限公司 | Qinghai-tibet railway permafrost wetland ground treatment technique |
| CN106126484A (en) * | 2016-07-06 | 2016-11-16 | 中交第公路勘察设计研究院有限公司 | The multi-factor comprehensive ever-frozen ground ground temperature zoning methods of multiple linear regression analysis |
| CN106120506A (en) * | 2016-06-29 | 2016-11-16 | 中交第公路勘察设计研究院有限公司 | Permafrost Area based on principle of energy balance hot-mix recycling Parameters design |
| CN107690864A (en) * | 2017-11-10 | 2018-02-16 | 中国科学院、水利部成都山地灾害与环境研究所 | A kind of Permafrost Area saltant type restoration of degraded grassland method |
| CN110502722A (en) * | 2019-08-27 | 2019-11-26 | 中国科学院、水利部成都山地灾害与环境研究所 | The measuring method of Alpine Grasslands Second productivity dynamic response under snow disaster load |
| CN113111531A (en) * | 2021-04-23 | 2021-07-13 | 中国水利水电科学研究院 | Distributed hydrological model-oriented frozen soil layer thickness simulation method for seasonal frozen soil area |
-
2022
- 2022-04-15 CN CN202210399262.4A patent/CN114707344B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5867516A (en) * | 1996-03-12 | 1999-02-02 | Hewlett-Packard Company | Vertical cavity surface emitting laser with reduced turn-on jitter and increased single-mode output |
| US7138156B1 (en) * | 2000-09-26 | 2006-11-21 | Myrick Michael L | Filter design algorithm for multi-variate optical computing |
| CN101418565A (en) * | 2007-10-23 | 2009-04-29 | 中铁第一勘察设计院集团有限公司 | Qinghai-tibet railway permafrost wetland ground treatment technique |
| CN106120506A (en) * | 2016-06-29 | 2016-11-16 | 中交第公路勘察设计研究院有限公司 | Permafrost Area based on principle of energy balance hot-mix recycling Parameters design |
| CN106126484A (en) * | 2016-07-06 | 2016-11-16 | 中交第公路勘察设计研究院有限公司 | The multi-factor comprehensive ever-frozen ground ground temperature zoning methods of multiple linear regression analysis |
| CN107690864A (en) * | 2017-11-10 | 2018-02-16 | 中国科学院、水利部成都山地灾害与环境研究所 | A kind of Permafrost Area saltant type restoration of degraded grassland method |
| CN110502722A (en) * | 2019-08-27 | 2019-11-26 | 中国科学院、水利部成都山地灾害与环境研究所 | The measuring method of Alpine Grasslands Second productivity dynamic response under snow disaster load |
| CN113111531A (en) * | 2021-04-23 | 2021-07-13 | 中国水利水电科学研究院 | Distributed hydrological model-oriented frozen soil layer thickness simulation method for seasonal frozen soil area |
Non-Patent Citations (2)
| Title |
|---|
| YONGHONG YI等: "Developing A Soil Inversion Model Framework for Regional Permafrost Monitoring", pages 4032 - 4035 * |
| 徐洪亮等: "青藏高原腹地多年冻土区活动层水热过程对气候变化的响应", pages 229 - 243 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115540788A (en) * | 2022-11-08 | 2022-12-30 | 中南大学 | Method for estimating thickness of permafrost movable layer |
| CN115540788B (en) * | 2022-11-08 | 2023-08-29 | 中南大学 | Active layer thickness estimation method combining multi-track InSAR deformation observation and unfrozen water content |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114707344B (en) | 2023-09-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sun et al. | Reconstruction of GRACE total water storage through automated machine learning | |
| CN110442666B (en) | Mineral resource prediction method and system based on neural network model | |
| CN112232522B (en) | Intelligent recommendation and dynamic optimization method for deep roadway support scheme | |
| CN111079977A (en) | Heterogeneous federated learning mine electromagnetic radiation trend tracking method based on SVD algorithm | |
| Karimi et al. | Prediction of fugitive landfill gas hotspots using a random forest algorithm and Sentinel-2 data | |
| CN111652404B (en) | All-weather earth surface temperature inversion method and system | |
| CN116975576B (en) | Mountain road geological disaster risk evaluation method based on key information statistics | |
| Su et al. | Set pair analysis in earth and environmental sciences: development, challenges, and future prospects | |
| CN114036841A (en) | Landslide incidence prediction method and system based on semi-supervised support vector machine model | |
| CN114399073A (en) | Ocean surface temperature field prediction method based on deep learning | |
| CN114494865A (en) | CSET method for future situation of mountain ecosystem | |
| Ma et al. | Intelligent location of microseismic events based on a fully convolutional neural network (FCNN) | |
| CN114707344A (en) | Method for calculating thickness of permafrost movable layer based on system dynamics | |
| Benestad et al. | A strategy to effectively make use of large volumes of climate data for climate change adaptation | |
| Medley et al. | Predicting Antarctic net snow accumulation at the kilometer scale and its impact on observed height changes | |
| Tyagi et al. | Prediction of the future landslide susceptibility scenario based on LULC and climate projections | |
| Ge et al. | [Retracted] A Statistical Prediction Model for Healthcare and Landslide Sensitivity Evaluation in Coal Mining Subsidence Area | |
| Saha et al. | Identification of Indian monsoon predictors using climate network and density-based spatial clustering | |
| Seddiki et al. | Using GIS Combined with AHP for Mapping landslide Susceptibility in Mila, in Algeria | |
| Li et al. | Deepphysinet: Bridging deep learning and atmospheric physics for accurate and continuous weather modeling | |
| Wang et al. | Soil organic carbon distribution for 0–3 m soils at 1 km 2 scale of the frozen ground in the Third Pole Regions | |
| Wang et al. | Operational climate prediction in the era of big data in China: Reviews and prospects | |
| CN117767579B (en) | AutoML-based power grid big data analysis method and automatic modeling system | |
| CN118037320B (en) | Land ecosystem carbon density estimation method based on multisource remote sensing data | |
| Mahmood et al. | Potential of Machine learning techniques compared to MIKE-SHE model for drain flow predictions in tile-drained agricultural areas of Denmark |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |