CN105005700A - Sedimentation criticality compartmenting method based on entropy, inner weight and outer weight - Google Patents

Abstract

The invention discloses a sedimentation criticality compartmenting method based on entropy, inner weight and outer weight. The sedimentation criticality compartmenting method comprises the following steps: S1) selecting an evaluation index of a sedimentation criticality; S2) adopting a GIS (Geographic Information System) and an RS (Remote Sensing) technology to carry out quantitative acquisition on the evaluation index data of each sedimentation criticality of a research area; S3) carrying out normalization processing on a data acquisition value; S4) calculating index entropy, and utilizing the entropy to eject undifferentiated indexes; S5) carrying out grading on the evaluation index; S6) calculating the inner weight of the evaluation index; S7) calculating the outer weight of the evaluation index; and S8) calculating the sedimentation criticality. The sedimentation criticality compartmenting based on multi-factor judgment reduces subjective judgment on a criticality evaluation index factor by human factors and is high in credibility.

Description

A kind of Harm of sedimentation degree zoning methods based on entropy and inside and outside weight

Technical field

The present invention relates to a kind of Harm of sedimentation degree zoning methods based on entropy and inside and outside weight.

Background technology

The analysis of Harm of sedimentation degree relates to the many aspects of multiple subject,, by certain object and principle, qualitative or quantitative classification is carried out to sedimentation evaluation index, in view of the difference of regional economy, social condition, the density of infection that Earth Subsidence Hazards causes also is not quite similar, but up to the present, there is no unified Harm of sedimentation degree criteria for division both at home and abroad.

Summary of the invention

For the problems referred to above, the invention provides a kind of Harm of sedimentation degree zoning methods based on entropy and inside and outside weight, based on the Harm of sedimentation degree zoning that multiple-factor is passed judgment on, reduce human factor to the subjective judgement of risk evaluation index factor, there is higher confidence level.

For realizing above-mentioned technical purpose, reach above-mentioned technique effect, the present invention is achieved through the following technical solutions:

Based on a Harm of sedimentation degree zoning methods for entropy and inside and outside weight, it is characterized in that, comprise the following steps:

Step 1: the evaluation index choosing Harm of sedimentation degree, comprise 4 first class index: origin cause of formation index, state index, harm index and vulnerability index, wherein, each first class index is corresponding 3 two-level index respectively, and 12 two-level index are followed successively by: amount of groundwater mining, pore water pressure, buildings load, accumulative settling amount, year subsidence rate, delaminating deposition amount, highway flatness, underground pipeline bending strength, railway 10m string measure maximum arrow degree, the density of population, roading density and site coverage;

Step 2: adopt the evaluation index data of GIS and RS technology to the every Harm of sedimentation degree in study area to carry out quantification collection, obtain raw data matrix U=(u _ij) _{m × n}, u _ijbe the data acquisition set value of i-th monitoring point at a jth two-level index;

Step 3: data acquisition value is normalized:

Obtain normalized data matrix Y=(y _ij) _{m × n};

Step 4: parameter entropy, utilizes entropy to reject indifference index;

Step 5: evaluation index grade classification: Harm of sedimentation degree evaluation index is divided into high target district, secondary high target district, medium Index areas, secondary low Index areas, low Index areas;

Step 6: weight in Calculation Estimation index: the weight allocation relation calculating single index factor inside;

Step 7: the outer weight of Calculation Estimation index: calculate the weight relationship that each index factor is mutual;

Step 8: Harm of sedimentation degree calculates: trying to achieve in every evaluation index factor after weight and outer weight, utilize the overlay analysis function in GIS spatial analysis module to carry out zoning to negative area density of infection.

The invention has the beneficial effects as follows: adopt GIS and RS technology to carry out quantification collection to evaluation index data, improve the confidence level of data, reduce the huge cost spent by artificial image data; By being normalized achievement data, solving the comparability between each index, being convenient to weight calculation; Calculated by entropy, reject indifference index; Pass judgment on negative area density of infection by weight in each index factor and outer weight, decrease the subjective judgement of human factor to risk evaluation index factor, make the Harm of sedimentation degree zoning methods passed judgment on based on multiple-factor have higher confidence level.

Accompanying drawing explanation

Fig. 1 is the process flow diagram of a kind of Harm of sedimentation degree zoning methods based on entropy and inside and outside weight of the present invention.

Embodiment

Below in conjunction with accompanying drawing and specific embodiment, technical solution of the present invention is described in further detail, can better understand the present invention to make those skilled in the art and can be implemented, but illustrated embodiment is not as a limitation of the invention.

Based on a Harm of sedimentation degree zoning methods for entropy and inside and outside weight, as shown in Figure 1, comprise the following steps:

Step 1: the evaluation index choosing Harm of sedimentation degree, comprises 4 first class index: origin cause of formation index, state index, harm index and vulnerability index.Following under science, level and multifarious principle, starting with from origin cause of formation index, state index, harm index and vulnerability index four first class index.

Wherein, each first class index is corresponding 3 two-level index respectively, 12 two-level index are followed successively by: amount of groundwater mining, pore water pressure, buildings load, accumulative settling amount, year subsidence rate, delaminating deposition amount, highway flatness, underground pipeline bending strength, railway 10m string measure maximum arrow degree, the density of population, roading density and site coverage, concrete corresponding relation is as shown in table 1:

Table 1 Index grading

Step 2: adopt the evaluation index data of GIS and RS technology to the every Harm of sedimentation degree in study area to carry out quantification collection, obtain raw data matrix U=(u _ij) _{m × n}, u _ijbe the data acquisition set value of i-th monitoring point at a jth two-level index;

Step 3: data acquisition value is normalized:

Obtain normalized data matrix Y=(y _ij) _{m × n};

In order to solve the comparability of data, being convenient to weight calculation, adopting the method for normalizing in dimensionless process to process it.

Step 4: parameter entropy, utilizes entropy to reject indifference index;

Step 5: evaluation index grade classification: Harm of sedimentation degree evaluation index is divided into high target district, secondary high target district, medium Index areas, secondary low Index areas, low Index areas;

Step 6: weight in Calculation Estimation index: the weight allocation relation calculating single index factor inside;

Step 7: the outer weight of Calculation Estimation index: calculate the weight relationship that each index factor is mutual;

Step 8: Harm of sedimentation degree calculates: trying to achieve in every evaluation index factor after weight and outer weight, utilize the overlay analysis function in GIS spatial analysis module to carry out zoning to negative area density of infection.

Preferably, in step 4, calculate each index entropy, formula is as follows:

In formula, e _jfor the entropy of a jth index, if when the entropy of all monitoring points on index j is just the same, illustrates that this index fails to provide effective information to decision maker, illustrate that under this index, the evaluation between all monitoring points is indifference, then remove this index.

Preferably, in step 5, by five-category method, each index factor is divided into height, secondary high, medium, secondary low, low five ranks, its formula is as follows:

Obtain Pyatyi demarcation interval:

In formula, y _ijmaxfor the maximal value in index row; y _iminfor the minimum value in index row; D is geometric ratio intermediate value between graded region; α _ifor span between graded region, wherein α ₁=y _ijmin, α ₂=y _ijmin+ d, α ₃=y _ijmin+ 2d, α ₄=y _ijmax-2d, α ₅=y _ijmax-d, α ₆=y _ijmax; s ₁~ s ₅be expressed as high target district, secondary high target district, medium Index areas, secondary low Index areas and low Index areas successively.

Preferably, in step 6, in index factor, weight represents the weight allocation relation of single index factor inside, and its formula is as follows:

In formula, w _pqfor weight in each harmfulness index factor, p is that in step 4,12 two-level index reject remaining number after indifference indexs, and t is density of infection grade classification number; μ _pqbe that p index is _qindividual grade place harmfulness achievement data nondimensionalization result presses the mean value after the division of five-category method.。

Preferably, in step 7, the outer weight of index factor represents each index factor weight relationship each other, and its relation is as follows:

In formula, w' _pfor the outer weight of each index factor, μ ' _pfor each index factor five grade average μ _pqand.

Preferably, after step 8, also comprise step 9: negative area density of infection classification display: by the spatial analysis module of the grade interval of negative area density of infection in conjunction with GIS software, according to five-category method, sedimentation evaluation region is divided into high-risk district, secondary high-risk district, middle risk factor district, Ci Di risk factor district and low risk factor district, obtains final density of infection zoning map.

Wherein, after obtaining negative area density of infection in step 8, analyze distribution situation and its codomain distribution range of settlement monitoring point, select suitable spatial interpolation methods to carry out interval division:

When survey region scope is less than setting value, and monitoring point quantity is less than setting value and is evenly distributed, then adopt inverse distance weighting to carry out interval division;

When survey region scope is greater than setting value, and monitoring point quantity is greater than setting value and concentrates on some interval, then adopt Natural neighbors method of interpolation to carry out interval division.

Adopt GIS and RS technology to carry out quantification collection to evaluation index data, improve the confidence level of data, reduce the huge cost spent by artificial image data; By being normalized achievement data, solving the comparability between each index, being convenient to weight calculation; Calculated by entropy, reject indifference index; Pass judgment on negative area density of infection by weight in each index factor and outer weight, decrease the subjective judgement of human factor to risk evaluation index factor, make the Harm of sedimentation degree zoning methods passed judgment on based on multiple-factor have higher confidence level.

These are only the preferred embodiments of the present invention; not thereby the scope of the claims of the present invention is limited; every utilize instructions of the present invention and accompanying drawing content to do equivalent structure or equivalent flow process conversion; or be directly or indirectly used in the technical field that other are relevant, be all in like manner included in scope of patent protection of the present invention.

Claims (7)

1., based on a Harm of sedimentation degree zoning methods for entropy and inside and outside weight, it is characterized in that, comprise the following steps:

Step 1: the evaluation index choosing Harm of sedimentation degree, comprise 4 first class index: origin cause of formation index, state index, harm index and vulnerability index, wherein, each first class index is corresponding 3 two-level index respectively, and 12 two-level index are followed successively by: amount of groundwater mining, pore water pressure, buildings load, accumulative settling amount, year subsidence rate, delaminating deposition amount, highway flatness, underground pipeline bending strength, railway 10m string measure maximum arrow degree, the density of population, roading density and site coverage;

Step 2: adopt the evaluation index data of GIS and RS technology to the every Harm of sedimentation degree in study area to carry out quantification collection, obtain raw data matrix U=(u _ij) _{m × n}, u _ijbe the data acquisition set value of i-th monitoring point at a jth two-level index;

Step 3: data acquisition value is normalized:

$y_{ij}=u_{ij}/\underset{i=1}{\overset{m}{\Σ}}{u}_{ij}---\left(1\right)$

Obtain normalized data matrix Y=(y _ij) _{m × n};

Step 4: parameter entropy, utilizes entropy to reject indifference index;

Step 5: evaluation index grade classification: Harm of sedimentation degree evaluation index is divided into high target district, secondary high target district, medium Index areas, secondary low Index areas, low Index areas;

Step 6: weight in Calculation Estimation index: the weight allocation relation calculating single index factor inside;

Step 7: the outer weight of Calculation Estimation index: calculate the weight relationship that each index factor is mutual;

Step 8: Harm of sedimentation degree calculates: trying to achieve in every evaluation index factor after weight and outer weight, utilize the overlay analysis function in GIS spatial analysis module to carry out zoning to negative area density of infection.

2. a kind of Harm of sedimentation degree zoning methods based on entropy and inside and outside weight according to claim 1, is characterized in that, in step 4,

Calculate each index entropy, formula is as follows:

$e_j=-\frac{\underset{i=1}{\overset{m}{\Σ}}{y}_{ij}}{\ln n}---\left(2\right)$

In formula, e _jfor the entropy of a jth index, if when the entropy of all monitoring points on index j is just the same, then remove this index.

3. a kind of Harm of sedimentation degree zoning methods based on entropy and inside and outside weight according to claim 1, it is characterized in that, in step 5, by five-category method, each index factor is divided into height, secondary high, medium, secondary low, low five ranks, its formula is as follows:

$d=\frac{y_{ijmax}-y_{ijmin}}{5}---\left(3\right)$

Obtain Pyatyi demarcation interval:

$\left\{\begin{array}{c}{s}_1={\mathrm{\α}}_1~{\mathrm{\α}}_2\\ s_2={\mathrm{\α}}_2~{\mathrm{\α}}_3\\ s_3={\mathrm{\α}}_3~{\mathrm{\α}}_4\\ s_4={\mathrm{\α}}_4~{\mathrm{\α}}_5\\ s_5={\mathrm{\α}}_5~{\mathrm{\α}}_6\end{array}\right.---\left(4\right)$

In formula, y _ijmaxfor the maximal value in index row; y _iminfor the minimum value in index row; D is geometric ratio intermediate value between graded region; α _ifor span between graded region, wherein α ₁=y _ijmin, α ₂=y _ijmin+ d, α ₃=y _ijmin+ 2d, α ₄=y _ijmax-2d, α ₅=y _ijmax-d, α ₆=y _ijmax; s ₁~ s ₅be expressed as high target district, secondary high target district, medium Index areas, secondary low Index areas and low Index areas successively.

4. a kind of Harm of sedimentation degree zoning methods based on entropy and inside and outside weight according to claim 1, it is characterized in that, in step 6, in index factor, weight represents the weight allocation relation of single index factor inside, and its formula is as follows:

$w_{pq}={\mathrm{\μ}}_{pq}/\underset{q=1}{\overset{t}{\Σ}}{\mathrm{\μ}}_{pq}---\left(5\right)$

In formula, w _pqfor weight in each harmfulness index factor, p is that in step 4,12 two-level index reject remaining number after indifference indexs, and t is density of infection grade classification number; μ _pqbe that p index is _qindividual grade place harmfulness achievement data nondimensionalization result presses the mean value after the division of five-category method.

5. a kind of Harm of sedimentation degree zoning methods based on entropy and inside and outside weight according to claim 4, is characterized in that, in step 7, the outer weight of index factor represents each index factor weight relationship each other, and its relation is as follows:

$\{\begin{array}{c}{w}_p^{\′}={\mathrm{\μ}}_p^{\′}/\underset{p=1}{\overset{n}{\Σ}}{\mathrm{\μ}}_p^{\′}\\ {\mathrm{\μ}}_p^{\′}=\underset{q=1}{\overset{t}{\Σ}}{\mathrm{\μ}}_{pq}\end{array}---\left(6\right)$

In formula, w' _pfor the outer weight of each index factor, μ ' _pfor each index factor five grade average μ _pqand.

6. a kind of Harm of sedimentation degree zoning methods based on entropy and inside and outside weight according to claim 1, it is characterized in that, after step 8, also comprise step 9: negative area density of infection classification display: by the spatial analysis module of the grade interval of negative area density of infection in conjunction with GIS software, according to five-category method, sedimentation evaluation region is divided into high-risk district, secondary high-risk district, middle risk factor district, Ci Di risk factor district and low risk factor district, obtains final density of infection zoning map.

7. a kind of Harm of sedimentation degree zoning methods based on entropy and inside and outside weight according to claim 6, is characterized in that, after obtaining negative area density of infection in step 8, analyzes distribution situation and its codomain distribution range of settlement monitoring point:

When survey region scope is less than setting value, and monitoring point quantity is less than setting value and is evenly distributed, then adopt inverse distance weighting to carry out interval division;

When survey region scope is greater than setting value, and monitoring point quantity is greater than setting value and concentrates on some interval, then adopt Natural neighbors method of interpolation to carry out interval division.