CN106126484A - The multi-factor comprehensive ever-frozen ground ground temperature zoning methods of multiple linear regression analysis - Google Patents
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Abstract
The present invention relates to the multi-factor comprehensive ever-frozen ground ground temperature zoning methods of multiple linear regression analysis, collect MODIS surface temperature data, MODIS vegetation index and SRTM digital terrain model dem data, extract relevant information；Carry out stepwise regression analysis, set up multiple linear regression model as prediction model；By environment, terrain factor are carried out stepwise regression analysis, set up ever-frozen ground identification Logistic model, carry out ever-frozen ground and there is probability calculation；The logistic model of ever-frozen ground mean annual cost prediction model with ever-frozen ground identification is overlapped, and then implements the drafting of ever-frozen ground ground temperature zoning map.The present invention relates to affect multiple factors of ever-frozen ground, use the frozen soil ground temperature predictive value acquired in linear regression model (LRM) higher with actual monitoring value degree of fitting, be necessity foundation instructing Frozen Ground Area Road Design, build and late maintaining science decision.
Description
Technical field
The invention belongs to sub level caving field, the multi-factor comprehensive being specifically related to a kind of multiple linear regression analysis is many
Year frozen soil ground temperature zoning methods.
Background technology
Qinghai-Tibet Platean is one of main Distribution Area of China's ever-frozen ground, existing ever-frozen ground area about 1.26 × 106km2,
Account for the 70% of whole nation ever-frozen ground area.Since nearly over half a century, along with fast development and " western part of Chinese national economy
Great developing strategy " progressively enforcement, " earth threeth pole " area extremely fragile in this ecological environment of Qinghai-Tibet Platean is repaiied in succession
Build a large amount of engineering, draw processed oil pipeline, Lanxi County to draw fiber optic communications engineering, high voltage power transmission including Qinghai-Tibet Highway, Qinghai-Tibet Railway, lattice
5 Important Project such as engineering, define a Qinghai-Tibet engineering passing through 550 km Permafrost Areas and walk corridor.
Frozen soil is a kind of geologic body more sensitive to variations in temperature, and frozen soil environment has with people's lives and economic construction
Close relationship, the engineering construction of permafrost region, the natural resources exploitation utilization of permafrost region, the ecological environmental protection of permafrost region all need
Want accurate ever-frozen ground zoning figure.Especially with China " band one tunnel " implementation, Tibet region economic development will
Welcoming the opportunities and challenges of a new round, Transportation Infrastructure Construction also will obtain unprecedented development, thus to ever-frozen ground
Zoning have higher requirement.
China's widely used ever-frozen ground zoning figure at present, including 1:1000 ten thousand China's frozen soil zoning and type map
(Zhou Youwu, China's frozen soil, Beijing: Science Press, 2000.) and 1: 400 ten thousand Chinese Glacier frozen soil desert figure (Wang Tao, in
State's dirt band desert figure (1: 400 ten thousand), Beijing: China Map Press, 2006.) etc., general when carrying out frozen soil zoning
It is the ever-frozen ground existence opening relationships verified with limited boring according to average temperature of the whole year observation, Combining with terrain, vegetation,
Manpower is relied on to come whether comprehensive descision ever-frozen ground exists.On the one hand Qinghai-xizang Plateau Region meteorological data is extremely limited, it is difficult to cover
Cover whole Qinghai-xizang Plateau Region, limited data cause the Permafrost Boundary determined to have the biggest uncertainty；On the other hand
Carry out comprehensive descision ever-frozen ground by manpower whether to exist there is bigger subjectivity, be difficult to evaluate the change of ever-frozen ground
Change.
Summary of the invention
It is an object of the invention to provide the multi-factor comprehensive ever-frozen ground ground temperature zoning side of a kind of multiple linear regression analysis
Method, integrated application remote sensing technology analyze comprehensively ever-frozen ground ground temperature distribution main affecting factors, use Logistic return and
Linear regression method carries out sum of squares of partial regression to predictor variables such as latitude, elevation, equivalent latitude and vegetation indexs and significantly checks,
Set up Permafrost On Qingzang Plateau Logistic distributed model and frozen soil ground temperature linear regression model (LRM), thus implement multi-factor comprehensive
Ever-frozen ground ground temperature zoning.
The technical solution adopted in the present invention is:
The multi-factor comprehensive ever-frozen ground ground temperature zoning methods of multiple linear regression analysis, it is characterised in that:
Comprise the following steps:
Step one: collect Qinghai-Tibet Platean Permafrost Area MODIS surface temperature data for many years, MODIS vegetation index and
SRTM digital terrain model dem data, the average surface temperature in extraction Permafrost Area for many years, Qinghai-Tibet Platean, average vegetation refer to
Number, equivalent latitude, latitude, elevation information；
Step 2: using ever-frozen ground mean annual cost as dependent variable, the earth's surface variable data conduct obtained by remote sensing technique
Independent variable, carries out stepwise regression analysis, sets up ever-frozen ground mean annual cost and latitude, elevation, equivalent latitude and vegetation index
Multiple linear regression model, walk the prediction model of corridor ever-frozen ground mean annual cost as Qinghai-Tibet Platean engineering；
Step 3: carry out stepwise regression analysis by the environment relevant to frozen soil distribution, terrain factor, sets up ever-frozen ground and exists
The ever-frozen ground identification Logistic model whether and contacted between terrestrial information, carries out ever-frozen ground and there is probability calculation, with P
=0.7 is judgment threshold, and the probability judgement more than 0.7 is frozen soil, is otherwise judged as melting soil；
Step 4: the logistic model of ever-frozen ground mean annual cost prediction model with ever-frozen ground identification is overlapped,
The accuracy that the reliability of raising ever-frozen ground identification and ground temperature are estimated, and then implement the drafting of ever-frozen ground ground temperature zoning map.
Described step one includes following sub-step:
(1) the average vegetation index factor:
The calculating of normalized differential vegetation index NDVI is calculated by following formula, and its codomain scope is [-1,1], big to calculate NDVI
Arithmetic mean of instantaneous value in 0 is used as average vegetation index, and draws annual vegetation index statistics block diagram:
In formula: ρirFor near infrared band reflectance；ρrFor red spectral band reflectance；
(2) average earth's surface temperature factor:
Take advantage of 0.02 to carry out ratio conversion MODIS surface temperature product, virtual value is carried out by the way of band math simultaneously
The calculating of annual mean surface temperature, Kelvin is converted to Celsius temperature the most at last；
(3) Elevation factor:
Require to be 1km according to computing unit, the DEM vector data using resolution to be 90m when building Qinghai-Tibet Platean DEM, so
After regenerate 1kmDEM data again；
(4) the Gradient factor and equivalent latitude calculate
In ARCGIS, SRTM-DEM is carried out surface analysis, thus generates the gradient of study area, slope aspect distribution i.e. DTM model,
Utilize the computing formula of equivalent latitude, by the equivalent latitude of grid computing generation study area:
Wherein, k is the gradient, and h is slope aspect, and φ is latitude, and φ ' is equivalent latitude, unit every in formula all degree of being.
Described step 2 includes following sub-step:
(1) correlation analysis:
The ground data of actual measurement frozen soil Geothermal data with relevant position is carried out Pearson correlation analysis and partial Correlation Analysis；
(2) stepwise regression analysis:
Regression variable is selected into one by one, after being often selected into a new variable, each variable being selected into is carried out significance inspection one by one
Test, and pick out insignificant variable, be the most repeatedly selected into, check, reject, till cannot picking out and cannot being selected into；Analyzed
Cheng Zhong, uses F significant level value as the criterion of stepwise regression method, and the probability parameter being selected into and rejecting independent variable is respectively provided with
It is 0.05 and 0.01；Using mean annual cost as dependent variable, the earth's surface variable data obtained by remote sensing technique as independent variable,
Carry out stepwise regression analysis；
(3) Multiple linear regression model is set up:
According to above-mentioned each Coefficient Fitting result, set up In Permafrost Regions of Qinghai-xizang Plateau engineering and walk the corridor polynary line of frozen soil mean ground temperature
Property regression model, is shown below:
In formula, lat is latitude, degree；H is elevation, Km；Equ is equivalent latitude, degree；NDVI is the arithmetic that vegetation index is more than 0
Meansigma methods.
Described step 3 includes following sub-step:
(1) dependency model between dependent variable and independent variable is set up:
Process below probability P is made:
Probability P is made non-linear conversion
In formula, R is the ratio of event occurrence rate and not probability of happening, and R is the monotonic increasing function of P simultaneously；R is carried out natural logrithm
Conversion, then have
After this conversion, lnR Yu R remains in that the concordance increasing or declining, and span is-∞～+∞, with one
As in linear regression model (LRM) span to dependent variable match；General linear regression model is utilized to set up dependent variable and certainly become
Dependency model between amount, it may be assumed that
(2) frozen soil distribution probability P is solved:
Solve P, can obtain
Above formula is the general expression of Logistic regression model, utilizes above formula to set up ever-frozen ground presence or absence and believes with ground
Contact between breath;Wherein, P is the probability that ever-frozen ground exists, xl, x2..., xnFor the environment relevant with frozen soil distribution, landform
The quantizating index of the factor, β1, β2..., βnFor regression equation coefficient；
Choosing cut value is that p >=0.7 carries out frozen soil forecast of distribution as threshold value, and carries out the parameter of each variable in regression equation
Estimating and inspection, if each parameter wals value is the biggest, and Sig value is less, say, that in equation, each variable is respectively provided with higher
Significance, it is believed that regression model is by inspection.
Described step 4 includes following sub-step:
(1) the frozen soil distribution in study area is judged initially with logistic model, for being judged as that the unit in tabetisol no longer enters
Line linearity regression model calculates；For being judged as the unit of permafrost region, use linear regression model (LRM) calculate, as calculate in still
There is the unit higher than 0 DEG C, be then judged as tabetisol, be included in the tabetisol that logistic model judges in advance；
(2) for calculating the unit less than 0 DEG C, still count in multi-factor comprehensive model according to linear regression model (LRM)；
(3) use multi-factor comprehensive model, study area calculated and judges, obtain the distribution of multi-factor comprehensive frozen soil ground temperature,
And carry out the drafting of ever-frozen ground ground temperature Division Butut.
The invention have the advantages that
The present invention has innovated the computational methods of Permafrost Area ground temperature zoning, by extract MODIS surface temperature data,
The remotely-sensed data factors such as MODIS vegetation index and SRTM digital terrain model (DEM) data, set up ever-frozen ground annual ground
Temperature and latitude, elevation, equivalent latitude and the multiple linear regression model of vegetation index and permafrost distribution logistic model,
Obtain multi-factor comprehensive frozen soil ground temperature by the two superposition to be distributed, thus implement ever-frozen ground ground temperature Division Butut and draw.Should
Invention relates to affecting multiple factors of ever-frozen ground, uses the frozen soil ground temperature predictive value acquired in linear regression model (LRM) and actual prison
Measured value degree of fitting is higher, is necessity foundation instructing Frozen Ground Area Road Design, build and late maintaining science decision.
Accompanying drawing explanation
Fig. 1 is the flow chart of method for designing of the present invention.
Fig. 2 walks corridor annual vegetation index for Qinghai-Tibet Platean engineering.
Fig. 3 walks corridor annual mean surface inverting temperature for Qinghai-Tibet Platean engineering.
Fig. 4 walks the distribution of corridor equivalent latitude for Qinghai-Tibet Platean engineering.
Fig. 5 is Multiple linear regression frozen soil ground temperature scattergram.
Fig. 6 is the frozen soil scattergram of cutting probability 0.6.
Fig. 7 is the frozen soil scattergram of cutting probability 0.7.
Fig. 8 is the frozen soil scattergram of cutting probability 0.8.
Fig. 9 is the frozen soil scattergram of cutting probability 0.9.
Figure 10 is the distribution of multi-factor comprehensive frozen soil ground temperature.
Detailed description of the invention
Below in conjunction with detailed description of the invention, the present invention will be described in detail.
It is an object of the invention to, from frozen soil coupled and heat-exchange process analysis, sum up and affect the main of frozen soil ground temperature distribution
Factor of influence, integrated application remote sensing technology analyze comprehensively each factor of influence in In Permafrost Regions of Qinghai-xizang Plateau Elemental characters distribution,
Set up In Permafrost Regions of Qinghai-xizang Plateau frozen soil distribution Logistic distributed model and frozen soil ground temperature linear regression model (LRM), thus carry out
Permafrost On Qingzang Plateau ground temperature zoning fast and accurately.Specifically include following steps:
Step one, collect Qinghai-Tibet Platean Permafrost Area MODIS surface temperature data for many years, MODIS vegetation index and
SRTM digital terrain model (DEM) data, the average surface temperature in extraction Permafrost Area for many years, Qinghai-Tibet Platean, average vegetation refer to
The information such as number, equivalent latitude, latitude, elevation.
Step 2, using ever-frozen ground mean annual cost as dependent variable, the earth's surface variable data obtained by remote sensing technique
As independent variable, carry out stepwise regression analysis, set up ever-frozen ground mean annual cost and latitude, elevation, equivalent latitude and vegetation
The multiple linear regression model of index, walks the prediction model of corridor ever-frozen ground mean annual cost as Qinghai-Tibet Platean engineering.
Step 3, carries out stepwise regression analysis by factors such as the environment relevant to frozen soil distribution, landform, sets up and freeze for many years
The ever-frozen ground identification Logistic model contacted between soil presence or absence and terrestrial information, carries out ever-frozen ground and there is probability meter
Calculating, with P=0.7 as judgment threshold, the probability judgement more than 0.7 is frozen soil, is otherwise judged as melting soil.
Step 4, folds the logistic model of ever-frozen ground mean annual cost prediction model with ever-frozen ground identification
Add, the accuracy that the reliability of raising ever-frozen ground identification and ground temperature are estimated, and then implement the drafting of ever-frozen ground ground temperature zoning map.
Described step one, has a following sub-step:
The 1.1 average vegetation index factors
The calculating of normalized differential vegetation index (NDVI) can be calculated by following formula, and its codomain scope is [-1,1].Owing to grinding
Study carefully district's height above sea level higher, vegetation growth seasonal substantially, be used as average vegetation and refer to calculating the NDVI arithmetic mean of instantaneous value more than 0
Number, and draw annual vegetation index statistics block diagram, see Fig. 2.
In formula: ρirFor near infrared band reflectance；ρrFor red spectral band reflectance.
1.2 average earth's surface temperature factors
Take advantage of 0.02 to carry out ratio conversion MODIS surface temperature product, virtual value is carried out by the way of band math simultaneously
The calculating of annual mean surface temperature, Kelvin is converted to Celsius temperature the most at last.
1.3 Elevation factor
Require to be 1km according to computing unit, the DEM vector data using resolution to be 90m when building Qinghai-Tibet Platean DEM, so
After regenerate 1kmDEM data again, Fig. 3 is shown in the study area elevation distribution generated.
The 1.4 Gradient factors and equivalent latitude calculate
In ARCGIS, SRTM-DEM is carried out surface analysis, thus generates the gradient of study area, slope aspect distribution i.e. DTM model,
Utilize the computing formula of equivalent latitude, generated the equivalent latitude of study area by grid computing, see Fig. 4.
Wherein, k is the gradient, and h is slope aspect, and φ is latitude, and φ ' is equivalent latitude, unit every in formula all degree of being.
Described step 2, has a following sub-step:
2.1 correlation analysis
The ground data of actual measurement frozen soil Geothermal data with relevant position is carried out Pearson correlation analysis and partial Correlation Analysis.
2.2 stepwise regression analysis
Regression variable is selected into one by one, after being often selected into a new variable, each variable being selected into is carried out significance inspection one by one
Test, and pick out insignificant variable, be the most repeatedly selected into, check, reject, till cannot picking out and cannot being selected into.Analyzed
Cheng Zhong, uses F significant level value as the criterion of stepwise regression method, and the probability parameter being selected into and rejecting independent variable is respectively provided with
It is 0.05 and 0.01.Using mean annual cost as dependent variable, the earth's surface variable data obtained by remote sensing technique as independent variable,
Carry out stepwise regression analysis.
2.3 set up Multiple linear regression model
According to above-mentioned each Coefficient Fitting result, set up In Permafrost Regions of Qinghai-xizang Plateau engineering and walk the corridor polynary line of frozen soil mean ground temperature
Property regression model, is shown below:
In formula, lat is latitude, degree；H is elevation, Km；Equ is equivalent latitude, degree；NDVI is the calculation that vegetation index is more than 0
Art meansigma methods.
Described step 3, has a following sub-step:
3.1 set up the dependency model between dependent variable and independent variable
Owing in general linear model model, relation between independent variable and probit is linear, the therefore conversion to probability P
Place ought to use non-linear transfer.Analyze based on above, process probability P work is following:
Probability P is made non-linear conversion
In formula, R is the ratio of event occurrence rate and not probability of happening, and R is the monotonic increasing function of P simultaneously；R is carried out natural logrithm
Conversion, then have
After this conversion, lnR Yu R remains in that the concordance increasing or declining, and span is-∞～+∞, with one
As in linear regression model (LRM) span to dependent variable match；General linear regression model is utilized to set up dependent variable and certainly become
Dependency model between amount, it may be assumed that
(2) frozen soil distribution probability P is solved:
Solve P, can obtain
Above formula is the general expression of Logistic regression model, utilizes above formula to set up ever-frozen ground presence or absence and believes with ground
Contact between breath;Wherein, P is the probability that ever-frozen ground exists, xl, x2..., xnFor the environment relevant with frozen soil distribution, landform
The quantizating index of the factor, β1, β2..., βnFor regression equation coefficient；
Choosing cut value is that p >=0.7 carries out frozen soil forecast of distribution as threshold value, and carries out the parameter of each variable in regression equation
Estimating and inspection, if each parameter wals value is the biggest, and Sig value is less, say, that in equation, each variable is respectively provided with higher
Significance, it is believed that regression model is by inspection.
Described step 4, has a following sub-step:
Initially with logistic model, 4.1 judge that the frozen soil in study area is distributed, for being judged as that the unit in tabetisol no longer enters
Line linearity regression model calculates；For being judged as the unit of permafrost region, use linear regression model (LRM) calculate, as calculate in still
There is the unit higher than 0 DEG C, be then judged as tabetisol, be included in the tabetisol that logistic model judges in advance；
4.2, for calculating the unit less than 0 DEG C, still count in multi-factor comprehensive model according to linear regression model (LRM).
4.3 use multi-factor comprehensive model, calculate study area and judge, obtaining multi-factor comprehensive frozen soil ground temperature and divide
Cloth, and carry out the drafting of ever-frozen ground ground temperature Division Butut.
Embodiment:
Step one, collect Permafrost Area, engineering corridor, Qinghai-Tibet MODIS surface temperature data, MODIS vegetation index and
SRTM digital terrain model (DEM) data, the average surface temperature in extraction Permafrost Area for many years, Qinghai-Tibet Platean, average vegetation refer to
The information such as number, equivalent latitude, latitude, elevation.
Step 2, sets up ever-frozen ground mean annual cost and latitude, elevation, equivalent latitude and the multiple linear of vegetation index
Regression model, walks the prediction model of corridor ever-frozen ground mean annual cost as Qinghai-Tibet Platean engineering.Modelling verification measured data
Being all from the Qinghai-Tibet Highway complete observational data of nearly 20 years, prediction model is the most identical with measured data, and prediction model is accurate.
In formula, lat is latitude, degree；H is elevation, Km；Equ is equivalent latitude, degree；NDVI is the calculation that vegetation index is more than 0
Art meansigma methods.
Step 3, choosing cut value is that p >=0.7 carries out frozen soil forecast of distribution as threshold value
Wherein, P is the probability that ever-frozen ground exists, xl, x2..., xnFor the environment relevant with frozen soil distribution, the amount of terrain factor
Change index, β1, β2..., βnFor regression equation coefficient；
Step 4, choosing cut value is that p >=0.7 carries out frozen soil forecast of distribution as threshold value.
Use multi-factor comprehensive model, study area calculated and judges, obtain the distribution of multi-factor comprehensive frozen soil ground temperature,
And carry out the drafting of ever-frozen ground ground temperature Division Butut.Concrete effect is shown in accompanying drawing 5.
Present disclosure is not limited to cited by embodiment, and those of ordinary skill in the art are by reading description of the invention
And the conversion of any equivalence that technical solution of the present invention is taked, the claim being the present invention is contained.
Claims (5)
1. the multi-factor comprehensive ever-frozen ground ground temperature zoning methods of multiple linear regression analysis, it is characterised in that:
Comprise the following steps:
Step one: collect Qinghai-Tibet Platean Permafrost Area MODIS surface temperature data for many years, MODIS vegetation index and
SRTM digital terrain model dem data, the average surface temperature in extraction Permafrost Area for many years, Qinghai-Tibet Platean, average vegetation refer to
Number, equivalent latitude, latitude, elevation information；
Step 2: using ever-frozen ground mean annual cost as dependent variable, the earth's surface variable data conduct obtained by remote sensing technique
Independent variable, carries out stepwise regression analysis, sets up ever-frozen ground mean annual cost and latitude, elevation, equivalent latitude and vegetation index
Multiple linear regression model, walk the prediction model of corridor ever-frozen ground mean annual cost as Qinghai-Tibet Platean engineering；
Step 3: carry out stepwise regression analysis by the environment relevant to frozen soil distribution, terrain factor, sets up ever-frozen ground and exists
The ever-frozen ground identification Logistic model whether and contacted between terrestrial information, carries out ever-frozen ground and there is probability calculation, with P
=0.7 is judgment threshold, and the probability judgement more than 0.7 is frozen soil, is otherwise judged as melting soil；
Step 4: the logistic model of ever-frozen ground mean annual cost prediction model with ever-frozen ground identification is overlapped,
The accuracy that the reliability of raising ever-frozen ground identification and ground temperature are estimated, and then implement the drafting of ever-frozen ground ground temperature zoning map.
The multi-factor comprehensive ever-frozen ground ground temperature zoning methods of multiple linear regression analysis the most according to claim 1, its
It is characterised by:
Described step one includes following sub-step:
(1) the average vegetation index factor:
The calculating of normalized differential vegetation index NDVI is calculated by following formula, and its codomain scope is [-1,1], big to calculate NDVI
Arithmetic mean of instantaneous value in 0 is used as average vegetation index, and draws annual vegetation index statistics block diagram:
In formula: ρirFor near infrared band reflectance；ρrFor red spectral band reflectance；
(2) average earth's surface temperature factor:
Take advantage of 0.02 to carry out ratio conversion MODIS surface temperature product, virtual value is carried out by the way of band math simultaneously
The calculating of annual mean surface temperature, Kelvin is converted to Celsius temperature the most at last；
(3) Elevation factor:
Require to be 1km according to computing unit, the DEM vector data using resolution to be 90m when building Qinghai-Tibet Platean DEM, so
After regenerate 1kmDEM data again；
(4) the Gradient factor and equivalent latitude calculate
In ARCGIS, SRTM-DEM is carried out surface analysis, thus generates the gradient of study area, slope aspect distribution i.e. DTM model,
Utilize the computing formula of equivalent latitude, by the equivalent latitude of grid computing generation study area:
Wherein, k is the gradient, and h is slope aspect, and φ is latitude, and φ ' is equivalent latitude, unit every in formula all degree of being.
The multi-factor comprehensive ever-frozen ground ground temperature zoning methods of multiple linear regression analysis the most according to claim 1, its
It is characterised by:
Described step 2 includes following sub-step:
(1) correlation analysis:
The ground data of actual measurement frozen soil Geothermal data with relevant position is carried out Pearson correlation analysis and partial Correlation Analysis；
(2) stepwise regression analysis:
Regression variable is selected into one by one, after being often selected into a new variable, each variable being selected into is carried out significance inspection one by one
Test, and pick out insignificant variable, be the most repeatedly selected into, check, reject, till cannot picking out and cannot being selected into；Analyzed
Cheng Zhong, uses F significant level value as the criterion of stepwise regression method, and the probability parameter being selected into and rejecting independent variable is respectively provided with
It is 0.05 and 0.01；Using mean annual cost as dependent variable, the earth's surface variable data obtained by remote sensing technique as independent variable,
Carry out stepwise regression analysis；
(3) Multiple linear regression model is set up:
According to above-mentioned each Coefficient Fitting result, set up In Permafrost Regions of Qinghai-xizang Plateau engineering and walk the corridor polynary line of frozen soil mean ground temperature
Property regression model, is shown below:
In formula, lat is latitude, degree；H is elevation, Km；Equ is equivalent latitude, degree；NDVI is the arithmetic that vegetation index is more than 0
Meansigma methods.
The multi-factor comprehensive ever-frozen ground ground temperature zoning methods of multiple linear regression analysis the most according to claim 1, its
It is characterised by:
Described step 3 includes following sub-step:
(1) dependency model between dependent variable and independent variable is set up:
Process below probability P is made:
Probability P is made non-linear conversion
In formula, R is the ratio of event occurrence rate and not probability of happening, and R is the monotonic increasing function of P simultaneously；R is carried out natural logrithm
Conversion, then have
After this conversion, lnR Yu R remains in that the concordance increasing or declining, and span is-∞～+∞, with one
As in linear regression model (LRM) span to dependent variable match；General linear regression model is utilized to set up dependent variable and certainly become
Dependency model between amount, it may be assumed that
(2) frozen soil distribution probability P is solved:
Solve P, can obtain
Above formula is the general expression of Logistic regression model, utilizes above formula to set up ever-frozen ground presence or absence and believes with ground
Contact between breath;Wherein, P is the probability that ever-frozen ground exists, xl, x2..., xnFor the environment relevant with frozen soil distribution, landform
The quantizating index of the factor, β1, β2..., βnFor regression equation coefficient；
Choosing cut value is that p >=0.7 carries out frozen soil forecast of distribution as threshold value, and carries out the parameter of each variable in regression equation
Estimating and inspection, if each parameter wals value is the biggest, and Sig value is less, say, that in equation, each variable is respectively provided with higher
Significance, it is believed that regression model is by inspection.
The multi-factor comprehensive ever-frozen ground ground temperature zoning methods of multiple linear regression analysis the most according to claim 1, its
It is characterised by:
Described step 4 includes following sub-step:
(1) the frozen soil distribution in study area is judged initially with logistic model, for being judged as that the unit in tabetisol no longer enters
Line linearity regression model calculates；For being judged as the unit of permafrost region, use linear regression model (LRM) calculate, as calculate in still
There is the unit higher than 0 DEG C, be then judged as tabetisol, be included in the tabetisol that logistic model judges in advance；
(2) for calculating the unit less than 0 DEG C, still count in multi-factor comprehensive model according to linear regression model (LRM)；
(3) use multi-factor comprehensive model, study area calculated and judges, obtain the distribution of multi-factor comprehensive frozen soil ground temperature,
And carry out the drafting of ever-frozen ground ground temperature Division Butut.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107194821A (en) * | 2017-05-23 | 2017-09-22 | 四川省草原科学研究院 | A kind of Alpine Meadow ecosystem health appraisal procedure |
| CN108761030A (en) * | 2018-03-26 | 2018-11-06 | 长安大学 | A kind of frozen soil ground temperature heat affecting assay method based on normalization spectrum entropy |
| CN110046415A (en) * | 2019-04-08 | 2019-07-23 | 中国科学院南京地理与湖泊研究所 | A kind of soil organic matter content remote sensing dynamic playback method of space-time fining |
| CN110598937A (en) * | 2019-09-18 | 2019-12-20 | 柳州市工人医院 | CO poisoning prediction method based on meteorological data |
| CN110633856A (en) * | 2019-09-18 | 2019-12-31 | 柳州市工人医院 | CO poisoning prediction method based on meteorological and atmospheric pollutant data |
| CN110909981A (en) * | 2019-10-22 | 2020-03-24 | 山东农业大学 | Method for evaluating grape climate zoning in Chinese continental monsoon climate zone |
| CN111812600A (en) * | 2020-06-29 | 2020-10-23 | 中南林业科技大学 | Self-adaptive terrain-dependent SRTM-DEM correction method |
| CN112380489A (en) * | 2020-11-03 | 2021-02-19 | 武汉光庭信息技术股份有限公司 | Data processing time calculation method, data processing platform evaluation method and system |
| CN112434262A (en) * | 2020-11-22 | 2021-03-02 | 同济大学 | Waterfront public space activity influence factor identification method and terminal |
| CN113095285A (en) * | 2021-04-30 | 2021-07-09 | 郑州大学 | Method for quantitatively analyzing vegetation space-time evolution driving mechanism based on pixel scale |
| CN113255148A (en) * | 2021-06-04 | 2021-08-13 | 中国科学院地理科学与资源研究所 | Method for estimating all-weather air temperature and space-time distribution thereof based on MODIS product data |
| CN114021371A (en) * | 2021-11-16 | 2022-02-08 | 中国科学院西北生态环境资源研究院 | Carbon reserve influence estimation method and device, electronic equipment and storage medium |
| CN114580207A (en) * | 2022-04-12 | 2022-06-03 | 中国林业科学研究院林业研究所 | Method for judging potential production capacity of large-diameter fir wood |
| CN114707344A (en) * | 2022-04-15 | 2022-07-05 | 西南交通大学 | Method for calculating thickness of permafrost movable layer based on system dynamics |
| CN117787503A (en) * | 2024-01-22 | 2024-03-29 | 中国科学院西北生态环境资源研究院 | Estimation and prediction method for dead area of grassland in permafrost region of Qinghai-Tibet plateau |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107194821A (en) * | 2017-05-23 | 2017-09-22 | 四川省草原科学研究院 | A kind of Alpine Meadow ecosystem health appraisal procedure |
| CN108761030A (en) * | 2018-03-26 | 2018-11-06 | 长安大学 | A kind of frozen soil ground temperature heat affecting assay method based on normalization spectrum entropy |
| CN110046415A (en) * | 2019-04-08 | 2019-07-23 | 中国科学院南京地理与湖泊研究所 | A kind of soil organic matter content remote sensing dynamic playback method of space-time fining |
| CN110598937A (en) * | 2019-09-18 | 2019-12-20 | 柳州市工人医院 | CO poisoning prediction method based on meteorological data |
| CN110633856A (en) * | 2019-09-18 | 2019-12-31 | 柳州市工人医院 | CO poisoning prediction method based on meteorological and atmospheric pollutant data |
| CN110909981A (en) * | 2019-10-22 | 2020-03-24 | 山东农业大学 | Method for evaluating grape climate zoning in Chinese continental monsoon climate zone |
| CN110909981B (en) * | 2019-10-22 | 2022-05-27 | 山东农业大学 | Method for evaluating grape climate zoning in Chinese continental monsoon climate zone |
| CN111812600A (en) * | 2020-06-29 | 2020-10-23 | 中南林业科技大学 | Self-adaptive terrain-dependent SRTM-DEM correction method |
| CN111812600B (en) * | 2020-06-29 | 2023-09-08 | 中南林业科技大学 | Self-adaptive terrain-related SRTM-DEM correction method |
| CN112380489A (en) * | 2020-11-03 | 2021-02-19 | 武汉光庭信息技术股份有限公司 | Data processing time calculation method, data processing platform evaluation method and system |
| CN112380489B (en) * | 2020-11-03 | 2024-04-16 | 武汉光庭信息技术股份有限公司 | Data processing time calculation method, data processing platform evaluation method and system |
| CN112434262A (en) * | 2020-11-22 | 2021-03-02 | 同济大学 | Waterfront public space activity influence factor identification method and terminal |
| CN113095285A (en) * | 2021-04-30 | 2021-07-09 | 郑州大学 | Method for quantitatively analyzing vegetation space-time evolution driving mechanism based on pixel scale |
| CN113255148A (en) * | 2021-06-04 | 2021-08-13 | 中国科学院地理科学与资源研究所 | Method for estimating all-weather air temperature and space-time distribution thereof based on MODIS product data |
| CN114021371B (en) * | 2021-11-16 | 2023-03-03 | 中国科学院西北生态环境资源研究院 | Carbon reserve influence estimation method and device, electronic equipment and storage medium |
| CN114021371A (en) * | 2021-11-16 | 2022-02-08 | 中国科学院西北生态环境资源研究院 | Carbon reserve influence estimation method and device, electronic equipment and storage medium |
| CN114580207A (en) * | 2022-04-12 | 2022-06-03 | 中国林业科学研究院林业研究所 | Method for judging potential production capacity of large-diameter fir wood |
| CN114580207B (en) * | 2022-04-12 | 2024-03-26 | 中国林业科学研究院林业研究所 | method for judging potential production capacity of fir large-diameter material |
| CN114707344A (en) * | 2022-04-15 | 2022-07-05 | 西南交通大学 | Method for calculating thickness of permafrost movable layer based on system dynamics |
| CN114707344B (en) * | 2022-04-15 | 2023-09-19 | 西南交通大学 | Permafrost active layer thickness calculation method based on system dynamics |
| CN117787503A (en) * | 2024-01-22 | 2024-03-29 | 中国科学院西北生态环境资源研究院 | Estimation and prediction method for dead area of grassland in permafrost region of Qinghai-Tibet plateau |
| CN117787503B (en) * | 2024-01-22 | 2024-05-28 | 中国科学院西北生态环境资源研究院 | Estimation and prediction method for dead area of grassland in permafrost region of Qinghai-Tibet plateau |
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