CN117764454B - Method for evaluating development degree of site flaky stripping - Google Patents
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Abstract
The invention provides a method for evaluating the degree of flaky stripping development of a great wall site in a Hexi corridor, which comprises the following steps: selecting an evaluation index related to the soil site flaky stripping disease in the arid region; the subjective weight of the evaluation index is calculated through a decision laboratory analysis method, the first objective weight of the evaluation index is determined through a unique weight coefficient method, the second objective weight of the evaluation index is determined through an inverse entropy weight method, and the subjective weight and each objective weight are averaged to obtain a combined weight; the normalized matrix of each evaluation index value of each arid region is subjected to weight adjustment through the combined weight to obtain a comprehensive weighting matrix; classifying and evaluating each arid region according to the comprehensive weighting matrix by a rank sum ratio comprehensive evaluation method; predicting the soil site flaky stripping disease of the arid region by a ridge regression model, and comparing with an evaluation result. According to the invention, the comprehensive weight is calculated through various weighting methods, so that the accuracy of classifying and evaluating the development degree of the flaking stripping in each region by a rank sum ratio comprehensive evaluation method is improved.
Description
Technical Field
The invention relates to the technical field of development degree of soil-site diseases, in particular to a method for evaluating development degree of site flaking, which is used for comprehensively evaluating and scientifically predicting the flaking diseases of the rammed soil sites based on rammed soil site flaking morphological characteristics, soil intrinsic factors and natural environment factors, and researching influence and protection of the development degree of the soil-site diseases on the soil sites.
Background
The earthen site plays an important role in the fields of cultural inheritance, historical exploration, scientific research and the like, and ancient earthen sites in northwest arid regions of China are divided into open-air earthen sites and indoor earthen sites according to the environment, and the open-air earthen sites are more seriously influenced by natural environments, so the protection of the open-air earthen sites is particularly important. Among the diseases of the open-air rammed earth remains, flaky stripping is one of the most common diseases, the wall body is dried and contracted due to rapid change of dry and wet circulation, cracks are formed on the surface, so that a soft surface is formed between the soil body on the surface layer and the wall body, the soil body is gradually stripped under the influence of wind power and other various actions, a certain protection effect on the resistance of the earth remains to weathering is achieved in the early stage of the formation, but the damage of the earth remains is accelerated by rapid stripping in the later stage, and the method has great significance in evaluation and prediction of the development degree of the earth remains.
The flaking is a disease generated by multi-factor coupling action, the influencing factors and the forming mechanism are complex, the influencing factors and the forming mechanism for the disease development are slightly achieved at present, but the evaluation and the prediction of the flaking disease are rarely carried out, and particularly, the scientific quantitative evaluation and the prediction early warning system for the flaking morphological characteristics are carried out under the influence of the intrinsic factors and the natural environment factors of soil.
The invention patent with the application number 202210744243.0 provides a method for evaluating and predicting the development degree of a rammed earth site crack disease, which comprises the following steps: selecting factors directly related to crack disease development and establishing an evaluation index system; calculating subjective weight according to an evaluation index system by using a fuzzy analytic hierarchy process, calculating first objective weight by using a multivariate unsteady index method, calculating second objective weight by using an improved entropy value method, and obtaining comprehensive weight by equal weight weighted average treatment; evaluating the development grade of the crack disease by utilizing a TOPSIS approach ideal solution and comprehensive weight; and constructing a BP neural network prediction model, and predicting future development trend of the rammed earth site crack diseases in the northwest drought areas by taking the data of the evaluation index as input data and the evaluation result as output data. Said invention utilizes natural environment characteristics to evaluate and predict development of rammed earth site crack disease, and provides high-accuracy crack disease development trend prediction method so as to raise effectiveness and controllability of crack disease treatment. However, consistency test based on the fuzzy hierarchy method in the invention lacks scientific basis, and TOPSIS approaches to an ideal solution and has strong sensitivity to data, if abnormal values or missing values exist, the decision result can be greatly influenced, and the effect of nonlinear problem such as the flaking development degree can be greatly reduced due to the fact that each index is assumed to be linear; meanwhile, the structure of the BP neural network needs to be selected according to practical problems, and the accuracy of a predicted result is directly influenced by the suitability of the network structure, so that a unified and complete theoretical guidance for the selection of the structure is not available so far, and the structure can be generally selected only by experience.
Disclosure of Invention
Aiming at the technical problems that the existing evaluation and prediction method for the development degree of the flaky stripping disease cannot comprehensively consider influence factors and formation mechanisms and has low prediction accuracy, the invention provides the evaluation method for the development degree of the flaky stripping disease of the site, which is used for determining the development condition of the existing part of the soil site through comprehensive evaluation, and obtaining the development condition of the flaky stripping disease of other soil sites by using the evaluation standard and the prediction model, thereby improving the timeliness and effectiveness of the protection of the soil site to a certain extent.
In order to achieve the above purpose, the technical scheme of the invention is realized as follows: a method for evaluating the development degree of site flaky stripping comprises the following steps:
step one: selecting an evaluation index related to the soil site flaky stripping disease in the arid region to establish an evaluation index system;
Step two: the subjective weight of each evaluation index is calculated through a decision laboratory analysis method according to an evaluation index system, the first objective weight of each evaluation index is determined through a independent weight coefficient method according to the actual data of each evaluation index, the second objective weight of each evaluation index is determined through an anti-entropy weight method, and the subjective weight, the first objective weight and the second objective weight are subjected to arithmetic average to obtain a combined weight;
Step three: carrying out weight adjustment on the normalized matrix of the numerical value of each evaluation index in each arid region by combining weights to obtain a comprehensive weighting matrix;
step four: classifying and evaluating each arid region by a rank sum ratio comprehensive evaluation method according to the comprehensive weighting matrix obtained in the step three;
Step five: predicting the soil site flaky stripping disease in the arid region by a ridge regression model, and comparing the disease with the evaluation result in the step four.
Preferably, the evaluation index includes: the method comprises the following steps of flaky stripping morphological characteristics, soil intrinsic factors and natural environment factors, wherein the flaky stripping morphological characteristics comprise: shell layer stripping thickness, powder layer stripping thickness and stripping area; intrinsic factors of soil mass include: specific surface area, disintegration rate, liquid index, porosity, total amount of soluble salt; natural environmental factors include: precipitation, evaporation, daily poor annual average temperature and dryness;
The specific surface area is a positive index, and the shell layer stripping thickness, the powder layer stripping thickness, the stripping area, the disintegration speed, the liquid index, the porosity, the total salt soluble amount, the precipitation amount, the evaporation amount, the daily poor annual average temperature and the dryness are negative indexes.
Preferably, the method for calculating subjective weight by using the decision laboratory analysis method comprises the following steps:
1) Quantifying the interrelationship among the evaluation indexes in the evaluation index system to obtain a direct influence matrix O;
2) Normalizing the direct influence matrix O to obtain a normalized direct influence matrix N;
3) Calculating a comprehensive influence matrix t=n (I-N) -1 from the canonical direct influence matrix N; wherein I is an identity matrix, (I-N) -1 is the inverse of matrix (I-N);
4) Obtaining the centerness M j＝Dj+Cj of the jth evaluation index from the comprehensive influence matrix T, wherein the influence degree Degree of influence To synthesize the element values of the j-th row and i 1 -th column in the influence matrix T,For the element values of the ith 1 row and the jth column in the comprehensive influence matrix T, n is the number of evaluation indexes;
5) And normalizing the centrality M j to obtain the subjective weight w 1 of the j-th evaluation index.
Preferably, the method for determining the first objective weight by the independent weight coefficient method is as follows:
1) Determining the values of all evaluation indexes of the arid region and constructing an original numerical matrix;
2) Calculating complex correlation coefficients of each evaluation index and other evaluation indexes according to the original numerical matrix;
3) And carrying out normalization processing on the reciprocal of the complex correlation coefficient to obtain the first objective weight of each evaluation index.
Preferably, the value on the diagonal line of the direct influence matrix O is represented by 0, and the value of the other element value in the direct influence matrix O determines the relationship strength of the two evaluation indexes by adopting a 5-level scale;
the normalization method is a range normalization method;
The complex correlation coefficient is
Wherein y represents an evaluation index value in the original value matrix,Represents the average value of all values under the evaluation index of the value y,A regression value representing the value y;
The regression value Is obtained by linear regression with all other values under the same evaluation index as independent variables.
Preferably, the method for determining the second objective weight of each evaluation index by using the inverse entropy weight method comprises the following steps:
1) Determining the values of all evaluation indexes of the arid region and constructing an original numerical matrix;
2) Normalizing the element values of the numerical matrix to obtain a normalized matrix;
3) Calculating the proportion of the ith sample in the jth evaluation index to the evaluation index;
4) Calculating an inverse entropy value h j of the j-th evaluation index according to the specific gravity;
5) And calculating a second objective weight w 3 of the j-th evaluation index according to the inverse entropy value h j.
Preferably, the proportion of the ith sample to all sample values in the jth evaluation index is
Wherein n ij represents a sample value of an ith arid region of the jth evaluation index in the normalized matrix, and m is the number of arid regions.
The inverse entropy value of the j-th evaluation index is
Second objective weight of jth evaluation indexN is the number of evaluation indexes;
The combining weights
The weight adjustment method in the third step comprises the following steps: the numerical value of each evaluation index for each arid region is multiplied by the combining weight.
Preferably, the implementation method of the rank sum ratio comprehensive evaluation method comprises the following steps:
1) Sequentially sequencing the arid regions in sequence;
2) According to the sequence numbers, the ranks of all arid regions under each evaluation index are compiled through a total rank sum ratio method, and rank matrixes are listed; calculating a rank sum ratio WRSR value after weighting according to the rank matrix;
3) Listing the distribution table condition of the rank sum ratio WRSR value and each group of frequency f, calculating the accumulated frequency Sigma f and the accumulated frequency of each arid region, and converting the accumulated frequency into a probability unit to obtain a inhibit value;
4) Taking the Probit value as an independent variable, taking the WRSR value as a dependent variable, performing simple linear regression, and fitting a corresponding WRSR estimated value regression equation;
5) And sequencing according to the fitted WRSR estimated value as a characteristic value, and grading each arid region.
Preferably, the method for listing the rank matrix by the whole rank sum ratio method is as follows: sequencing according to the index value of each evaluation index, wherein the positive index is ranked from small to large, the negative index is ranked from large to small, and the average ranks of the same evaluation index data are ranked to obtain a rank matrix R a＝(Rij)m×n;
The calculation formula of the rank sum ratio WRSR value is as follows:
Wherein WRSR i is the rank sum ratio of the ith arid region, R ij is the rank of the jth evaluation index of the ith arid region, and W j represents the weight of the jth evaluation index;
According to the accumulated frequency query percentage and the probability unit comparison table, converting the percentage form of the evaluation rank number, namely the evaluation rank number/n 100%, into a probability unit;
4 steps in the step, 4 steps are classified according to the WRSR estimated value, and are positioned in [ -, 0.2392) to be low in development degree, [0.2392,0.4794) is moderately developed, [0.4794,0.7196) is highly developed, [0.7196 ] the number of the cells, +++) is very developmental.
Preferably, the implementation method for predicting the ridge regression model comprises the following steps:
(1) Constructing a ridge regression model through SPSS software to obtain a predicted value of the stripping characteristic under the ridge regression model; respectively constructing a ridge regression model for the thickness of the shell layer, the thickness of the powder layer and the stripping area to obtain regression model coefficients, and respectively multiplying other non-morphological characteristic factors by the regression model coefficients to obtain predicted values;
(2) Substituting the actual value of the stripping morphological feature in the original numerical matrix constructed by the numerical values of each evaluation index of the arid region with a predicted value, and obtaining a new evaluation result by a rank sum ratio evaluation method to obtain a predicted result;
(3) Analyzing and comparing the predicted result with the real result: the ridge regression model fitness test was performed by paired sample T test using SPSS software.
Compared with the prior art, the invention has the beneficial effects that: selecting factors related to the flaky stripping diseases and establishing a basic evaluation index system; the decision laboratory analysis method is utilized to give weight to subjective weight to each evaluation index, a plurality of factors are considered, and the factors and the relations among the factors are quantized, so that the relative importance and degree among different factors are reflected, meanwhile, the data and the results are visually displayed in the form of charts and graphs, and the visual analysis can more intuitively represent the interrelationship among the factors. The invention determines the first objective weight by using each actual value through a independent weight coefficient method, determines the index weight according to the collinearity intensity between each evaluation index and other indexes, avoids the influence of subjective factors, ensures the objectivity of weight determination, ensures clear interpretability of the obtained result, can clearly see the contribution degree and relative independence of each index, and is beneficial to guiding decision and evaluating the importance of the index. The invention determines the second objective weight by using the inverse entropy weight method, can avoid weight conflict generated by high correlation among indexes, and considers the correlation among evaluation indexes when calculating the weight, thereby effectively resisting noise interference; the average subjective weight and each objective weight obtain a combined weight, and the combined weight is adopted to reduce the error of subjective factors caused by subjective judgment on one hand, improve the stability of the objective weight on the other hand, avoid the direct influence of a single weight and improve the reliability of the weight; on the basis of the combination weight, classifying and evaluating each region by a rank and ratio comprehensive evaluation method (RSR), and selecting an evaluation index on the basis of an nonparametric method has no special requirement, so that the method is applicable to various evaluation objects, and because the numerical value used in calculation is rank order, the interference of abnormal values can be eliminated; the ridge regression model is constructed, the development degree of the flaky stripping disease is predicted by inputting related index parameters, and the prediction method is beneficial to timely protecting the earthen site which does not show the flaky stripping morphological characteristics or has unobvious characteristics. According to the invention, the comprehensive weight is calculated by adopting various weighting methods, so that the accuracy of classifying and evaluating the flaking development degree of each region by using a rank sum ratio comprehensive evaluation method is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a schematic diagram of the evaluation index system of the present invention.
FIG. 3 is a schematic diagram of the centering-reason degree in the method Dematel of the present invention.
FIG. 4 is a schematic diagram of the evaluation results of the present invention.
Fig. 5 is a fitted view of the predictive model of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without any inventive effort, are intended to be within the scope of the invention.
As shown in FIG. 1, the method for evaluating the development degree of the site flaky peeling comprises the following steps:
Step one: selecting an evaluation index related to the soil site flaky stripping disease in the arid region to establish an evaluation index system, wherein the evaluation indexes comprise: form characteristics of flaking, intrinsic factors of soil and natural environment factors.
The flaky stripping morphological characteristics of the earthen site in the evaluation index comprise: shell layer stripping thickness, powder layer stripping thickness and stripping area; intrinsic factors of soil mass include: specific surface area, disintegration rate, liquid index, porosity, total amount of soluble salt; natural environmental factors include: precipitation, evaporation, daily average temperature, and dryness.
The specific surface area in the evaluation index is a positive index, and the shell layer stripping thickness, the powder layer stripping thickness, the stripping area, the disintegration speed, the liquid index, the porosity, the total salt soluble amount, the precipitation amount, the evaporation amount, the daily poor annual average temperature and the dryness are negative indexes. The positive index and the negative index can be distinguished to more accurately reflect the quality degree of the evaluation object, and the accuracy and the rationality of the evaluation are improved.
Step two: the subjective weight of each evaluation index is calculated through Dematel decision laboratory analysis according to an evaluation index system, the first objective weight of the evaluation index is determined through independent weight coefficient method according to the actual data of each evaluation index, the second objective weight of the evaluation index is determined through inverse entropy weight method, and the subjective weight, the first objective weight and the second objective weight are processed through arithmetic average method to obtain the combined weight.
The implementation method for calculating subjective weight by Dematel decision laboratory analysis method comprises the following steps:
1) From the research purpose, the evaluation indexes are determined, and the interrelation among the evaluation indexes is quantized to obtain the direct influence matrix O.
The binary relation between the evaluation indexes is determined by pairwise comparison. The evaluation index S i1 and the evaluation index S j are compared twice, which is the direct influence of the evaluation index S i1 on the evaluation index S j and the direct influence of the evaluation index S j on the evaluation index S i1. N evaluation indexes are compared n (n-1) times for the whole evaluation index system. The evaluation index itself does not need to be compared, i.e. the value on the diagonal line of the direct impact matrix O is usually represented by 0, and the measurement method for the relation strength is measured by adopting a 5-level scale, i.e. a five-division method, i.e. a method of taking 0 to 4, as shown in table 1.
The data source of the judgment matrix is expert judgment, and the relative influence degree among all evaluation indexes can be more clearly represented by drawing a network diagram, namely a figure 2. The number of the evaluation indexes of the direct influence matrix o= (O i1j)n*n, n) is obtained through the above steps, and is shown in table 2.
TABLE 1 determination of the scale meaning of the matrix
TABLE 2 direct impact matrix
2) The direct influence matrix O is normalized to obtain a normalized direct influence matrix N, as shown in table3 below, by which problems due to order of magnitude or dimension differences can be reduced while maintaining the relative relationship between the data.
All normalization methods in the invention adopt the same calculation method, namely the range normalization method, and formulas are not shown one by one in the follow-up content, and are as follows: Wherein, X represents any element in the direct influence matrix, X min represents the minimum value of the column of the element X, X max represents the maximum value of the column of the element X, and X' represents the normalized value of the element X.
Table 3 Specification direct impact matrix
3) The comprehensive influence matrix T is obtained by calculating the standard direct influence matrix N, and the calculation formula is as follows, as shown in the following table 4:
T＝N(I-N)-1
Wherein T is a comprehensive influence matrix, N is a canonical direct influence matrix, I is an identity matrix, and (I-N) -1 is an inverse of the matrix (I-N). Since the canonical direct influence matrix N can only represent direct influence between evaluation indexes, the comprehensive influence matrix needs to be obtained through calculation, so that the comprehensive influence matrix can reflect direct and indirect influence relations between all indexes in the system, and thus, the influence relation in the system can be more comprehensively described.
TABLE 4 comprehensive impact matrix
4) The influence degree D j, the influenced degree C j, the center degree M j and the cause degree R j of each evaluation index are obtained from the comprehensive influence matrix T, and the calculation formulas are respectively as follows:
Mj＝Dj+Cj
Rj＝Dj-Cj
and drawing the figure 3 by calculating the center degree and the cause degree and taking the center degree and the cause degree as a coordinate system, and revealing the position and the action mode of each evaluation index in the system.
Table 5 influence, affected, center, and cause of each evaluation index
5) The centrality M i is normalized to obtain subjective weights, see table 6 below. The centrality is an index parameter for measuring the status and the action of the evaluation index in the evaluation index system, and considers the influence degree of the evaluation index on all other evaluation indexes and the influence degree of all other evaluation indexes. The cause degree can reflect the action and the influence mode of the evaluation index in the evaluation index system, but the cause degree is more focused on revealing the causal relation among the evaluation indexes. The evaluation index having a high cause degree has a large influence on other evaluation indexes, but is not necessarily of equal importance in the evaluation index system. Thus, normalizing the weights using the causal degree may produce some bias and unreasonable results.
Table 6 subjective weights of the evaluation indexes
The Dematel decision laboratory analysis method determines that subjective weights can fully consider all relevant evaluation indexes and give corresponding weights to the relevant evaluation indexes, so that more comprehensive information is covered in the decision process, and the influence degree of each evaluation index on the final decision can be clearly explained, so that the decision process has transparency and understandability.
The independent weight coefficient refers to a measure of linear dependence between a random variable and a set of random variables, which shows the correlation between data, and is implemented by: the weight is determined by using the collinearity strength between the evaluation indexes, if the correlation between a certain evaluation index and other evaluation indexes is strong, the information is indicated to have larger overlapping, which means that the weight of the evaluation index is lower, otherwise, if the correlation between the certain evaluation index and other evaluation indexes is weaker, the information carried by the evaluation index is indicated to be larger, and the evaluation index is supposed to be given higher weight. The independent weight coefficient method only considers the correlation between data, and the calculation mode is that the complex correlation coefficient R value obtained by regression analysis is used for representing the collinearity strength (namely, the correlation strength), and the larger the value is, the stronger the collinearity is, and the lower the weight is.
The implementation method for determining the first objective weight through the independent weight coefficient method comprises the following steps:
1) And determining the numerical value of each evaluation index of the arid region and constructing a numerical matrix.
The numerical matrix X 1,X2,....,Xn is determined according to the numerical value of each evaluation index of each arid region, n represents the number of the evaluation indexes, and the content of index items is obtained by previous experiments and researches, and is shown in the following table 7.
TABLE 7 study area and evaluation index
2) Calculating complex correlation coefficients of each evaluation index and other evaluation indexes, wherein the correlation coefficients are shown in a formula:
wherein y represents any value under a certain evaluation index, Represents the average of all values under the evaluation index of y,And (3) representing a regression value of y, wherein the regression value is obtained by performing linear regression by taking all other values under the same evaluation index as independent variables and can be obtained by adopting SPSS software.
The reciprocal of the complex correlation coefficient R 1,R2,....,Rn and the complex correlation coefficient of each evaluation index was obtained as shown in table 8 below.
TABLE 8 Complex correlation coefficient and reciprocal of evaluation indices
3) And weighting each evaluation index to obtain a first objective weight.
The inverse of the complex correlation coefficient is normalized to obtain the first objective weight for each evaluation index, see table 9 below.
Table 9 objective weight of each index
The inverse entropy weight method is a method for reflecting the data information amount based on the entropy weight method, and the inverse entropy shows the difference of indexes, and the realization method is as follows: and (3) calculating the inverse entropy value of each evaluation index to obtain the difference of the evaluation indexes, wherein the larger the inverse entropy is, the higher the corresponding weight is.
The implementation method for determining the second objective weight of the evaluation index by using the inverse entropy weight method comprises the following steps:
1) Determining each evaluation index item and constructing a data matrix
The data matrix X 1,X2,....,Xn for each evaluation index was determined and the matrix is shown in Table 7.
2) The numerical matrix was normalized by the pole difference method, as shown in table 10 below.
Table 10 normalization processing results
3) The specific gravity of the ith sample value in the jth evaluation index to the evaluation index was calculated, as shown in table 11 below.
Wherein r ij represents the proportion of the ith sample to all sample values in the jth evaluation index, n ij represents the ith sample value in the jth evaluation index in the normalized matrix, and m is the number of samples.
Table 11 specific gravity of each sample in the evaluation index
4) The inverse entropy value of the j-th evaluation index is calculated, see table 12 below.
Table 12 inverse entropy values of evaluation indexes
5) The second objective weight of the j-th evaluation index is calculated, see table 13 below.
TABLE 13 second objective weight for evaluation indicators
The combination weights were obtained by the method of arithmetic averaging of the subjective weights and the objective weights obtained as described above, and the specific data are shown in table 14 below.
The advantages of using combining weights are: ① The Demtal decision laboratory analysis method only considers the influence among all evaluation indexes, has a certain subjective limitation, and reduces the one-sided influence of subjective factors by combining and adjusting assignment added with an objective weight method. ② The 3 methods perform arithmetic average combination to reduce the adverse effect on the overall evaluation when error values occur in a single method. ③ The combined weight is adopted to adjust the weight of each evaluation index, so that the data can be more accurate and stable when rank and ratio evaluation is carried out.
Table 14 combining weights
Step three: and carrying out weight adjustment on the normalized matrix of the numerical value of each evaluation index in each arid region by combining weights to obtain a comprehensive weighting matrix.
And (3) carrying out weight adjustment on each evaluation index according to the combined weight obtained in the step (II) by using a normalized matrix of the values of each evaluation index in each arid region, namely a matrix normalized in the table (10), namely multiplying the value of each evaluation index in the table (10) by the corresponding weight to obtain a comprehensive weighting matrix.
Step four: and step three, the comprehensive weighting matrix obtained in the step is used for classifying and evaluating each arid region by a rank sum ratio comprehensive evaluation method.
The method for realizing the rank sum ratio comprehensive evaluation method comprises the following steps:
1) The arid region is sequentially numbered from 1 to 18 according to the sequence of Jiayuguan, jiuquan, gaoshi, linzhi, zhangye, wu Wei, cloisonne, yongchang, guide, ledu, folk music, safety, yongjing, yongdeng, tianzhu, datong, huangzhong and Haidong, so that the description is convenient in sorting, and no other significance is realized.
2) Listing a rank matrix by a whole rank sum ratio method, and compiling ranks of each arid region under each evaluation index: and sorting according to the index value of each specific evaluation index, wherein the positive index is ranked from small to large, the negative index is ranked from large to small, the same evaluation index data are ranked equally, and a rank matrix is obtained, and R a＝(Rij)m×n is recorded as shown in tables 15 and 16.
Table 15 comprehensive weighting matrix
Table 16 rank matrix
Rank sum ratio WRSR value and WRSR value rank after weighting is obtained through calculation, the correlation value and rank are shown in a table 17, and the calculation formula is as follows:
wherein, R ij is the rank of the j-th evaluation index of the i-th arid region, W j represents the weight of the j-th evaluation index, and the sum of the weights is 1. The larger the value of WRSR i, the better the evaluation object.
Table 17WRSR values and ranks
The dimensionless statistic WRSR obtained by rank conversion ranks or ranks the merits of the evaluation objects (i.e., arid regions) by WRSR values. In the comprehensive evaluation, the WRSR value can contain information of all evaluation indexes, and the comprehensive level of the evaluation indexes is displayed, and the larger the WRSR value is, the better the comprehensive evaluation is.
3) The distribution table condition of the WRSR value and the frequency f of each group are listed, the cumulative frequency Σf and the cumulative frequency p of each group are calculated, and the cumulative frequency can be queried into a percentage form of the evaluation rank, namely, the evaluation rank/n×100% in table 18, is converted into a probability unit Probit by a percentage and probability unit comparison table, so as to obtain a Probit value, and the Probit value is shown in the following table 18.
Table 18 distribution table case of WRSR and Probit values
4) Simple linear regression is performed by SPSS software with the Probit value as an independent variable and the WRSR value as a dependent variable, and a regression equation of the corresponding WRSR estimated value is fitted and F test (variance ratio test), significance test and t test (difference test) are performed, and the regression result and analysis result are shown in the following table 19.
Table 19WRSR estimate regression equation and analysis
Wherein B represents the coefficient of the independent variable, the standard error represents the standard deviation of the sample, beta represents the standard regression coefficient, t represents the t value at the time of t test, namely the statistic of the difference between the comparison mean values, P represents the P value in the significance test, namely the significance level, VIF represents the severity of multiple collinearity, R 2 represents the fitting degree of curve regression, and the adjustment R2 is a penalty term for adding the number of independent variables on the basis of the fitting degree R 2 to prevent the excessive fitting of the model, and F represents whether one statistic in the F test is used for comparing the variances of the samples to be significantly different.
5) And sorting according to the fitted WRSR values as characteristic values, and grading. In the case of the classification, three classification cases of 3, 4 and 5 were adopted, and according to the related studies on the sheet peeling in the past, the classification case of 4 was good, and the classification case of 4 was preferentially selected, as shown in tables 20 and 21 below, wherein the classification was 4 grades according to the WRSR value, and the classification was located in [ - ≡ 0.2392) to be low in development level, [0.2392,0.4794) to be medium in development level, [0.4794,0.7196) to be high in development level, [0.7196, ++ ] to be extremely high in development level. The WRSR threshold (fitting value) is obtained from the regression equation substituted into table 19 with the Probit value. A scatter diagram as shown in fig. 4 can be drawn by taking a sample of each arid region as an abscissa and a WRSR value as an ordinate, so as to show classification conditions of each arid region.
TABLE 20 ranking threshold table
TABLE 21 extent of development of arid regions
The method for analyzing parameters is integrated by adopting a rank sum comparison comprehensive evaluation method, the result is more accurate, because the numerical value used for calculation is rank order, the interference of abnormal values can be eliminated, and in order to be more matched with the evaluation data, three, four and five grade evaluation categories are respectively carried out for comparing the evaluation result, more reasonable classification grades are selected, and the difference caused by subjective evaluation classification grade selection is eliminated.
Step five: predicting the soil site flaky stripping disease in the arid region by a ridge regression method, and comparing the disease with the evaluation result in the step four. Since the peeling morphology features are the features of a long-term dynamic development process and can not be directly observed, the peeling morphology features of the rammed earth site can be predicted by intrinsic factors and environmental factors and then substituted into an evaluation system to obtain an evaluation result, thereby obtaining an evaluation and prediction method for the development degree of the peeling disease of the earth site in the arid region.
And a certain technical support is provided for protecting and reinforcing the earthen site through comprehensive evaluation of the flaky stripping diseases. However, the invention provides a prediction model based on the limited effective information generated by the comprehensive evaluation result.
The implementation method of the ridge regression model prediction comprises the following steps: the method comprises the steps of establishing a ridge regression (RSR) model through SPSS software, classifying and evaluating the arid regions by the obtained fitting characteristic values, predicting the flaky peeling morphological characteristics through non-characteristic factors, combining predicted data with a rank and ratio evaluation method, namely replacing the actual flaky peeling morphological characteristics in a rank and ratio evaluation system with predicted values to obtain a predicted evaluation structure, comparing the predicted evaluation structure with actual evaluation results, and carrying out verification analysis to finally obtain the flaky peeling development conditions of each arid region through the non-characteristic factors obtained through some experiments. Because the weights of the indexes are adjusted through comprehensive weights before calculating the rank value, the WRSR is used for replacing the RSR, the meaning of the WRSR is similar, the WRSR is a characteristic value after weight adjustment, and the greater the characteristic value is, the smaller the development degree of the flaky stripping disease is indicated.
1) And constructing a ridge regression model through SPSS software to obtain a predicted value of the stripping characteristic under the ridge regression model. Since ridge regression belongs to one of linear regression, it is necessary to construct ridge regression models of shell thickness, powder layer thickness and stripping area to obtain regression model coefficients, and perform R 2 value calculation and F test on the ridge regression models, where the three models show significance on the level based on the F test significance P value of 0<0.01, and reject the original assumption, indicating that there is a regression relationship between independent variables and dependent variables. Meanwhile, the goodness of fit R 2 of the three models is 0.959, 0.954 and 0.926 respectively, and the models are excellent. The correlation coefficients are shown in Table 22 below. The model coefficient can be used for obtaining a ridge regression model of three morphological characteristic indexes, and other non-morphological characteristic factors are multiplied by the coefficients respectively to directly obtain a predicted value.
Table 22 model coefficients
2) The true values of the peeling morphology features in table 7 are replaced by predicted values so as to be convenient for evaluation when the sheet development features are not obvious, namely, the sheet development features cannot be directly observed, the new predicted values and the true values are shown in the following table, the predicted values are obtained through substituting the model in the step five (1) into the calculation, and then new evaluation results are obtained through a rank sum ratio evaluation method, and the following table 23 is shown. And (3) reevaluating the predicted value and the true value of other indexes according to the steps S1-S5, and carrying out fitting degree test on the evaluation result to obtain an excellent prediction model. The predicted situation is further shown by plotting the WRSR value true value and the predicted value into a double-line graph, see fig. 5.
TABLE 23 prediction of extent of development
3) Analyzing and comparing the predicted result with the real result: model fitting degree test is carried out through paired sample T test by using SPSS software, the calculation results are shown in the following table 24, the paired sample T test results show that the paired WRSR predicted value is paired based on the actual value of the variable WRSR, the significance value P is 1.000, the significance is not presented horizontally, the original assumption cannot be refused, and therefore, no significance difference exists between the paired WRSR predicted values of the WRSR actual value. The difference amplitude value is as follows: 0.0, the difference amplitude is very small, and the model prediction condition is good.
Table 24 fitness test table
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (8)
1. The method for evaluating the development degree of the site flaky stripping is characterized by comprising the following steps of:
step one: selecting an evaluation index related to the soil site flaky stripping disease in the arid region to establish an evaluation index system;
the evaluation index includes: the method comprises the following steps of flaky stripping morphological characteristics, soil intrinsic factors and natural environment factors, wherein the flaky stripping morphological characteristics comprise: shell layer stripping thickness, powder layer stripping thickness and stripping area; intrinsic factors of soil mass include: specific surface area, disintegration rate, liquid index, porosity, total amount of soluble salt; natural environmental factors include: precipitation, evaporation, daily poor annual average temperature and dryness;
Step two: the subjective weight of each evaluation index is calculated through a decision laboratory analysis method according to an evaluation index system, the first objective weight of each evaluation index is determined through a independent weight coefficient method according to the actual data of each evaluation index, the second objective weight of each evaluation index is determined through an anti-entropy weight method, and the subjective weight, the first objective weight and the second objective weight are subjected to arithmetic average to obtain a combined weight;
Step three: carrying out weight adjustment on the normalized matrix of the numerical value of each evaluation index in each arid region by combining weights to obtain a comprehensive weighting matrix;
step four: classifying and evaluating each arid region by a rank sum ratio comprehensive evaluation method according to the comprehensive weighting matrix obtained in the step three;
the method for realizing the rank sum ratio comprehensive evaluation method comprises the following steps:
1) Sequentially sequencing the arid regions in sequence;
2) According to the sequence numbers, the ranks of all arid regions under each evaluation index are compiled through a total rank sum ratio method, and rank matrixes are listed; calculating a rank sum ratio WRSR value after weighting according to the rank matrix;
3) Listing the distribution table condition of the rank sum ratio WRSR value and each group of frequency f, calculating the accumulated frequency Sigma f and the accumulated frequency of each arid region, and converting the accumulated frequency into a probability unit to obtain a inhibit value;
4) Taking the Probit value as an independent variable, taking the WRSR value as a dependent variable, performing simple linear regression, and fitting a corresponding WRSR estimated value regression equation;
5) Sequencing according to the fitted WRSR estimated value as a characteristic value, and grading each arid region;
Step five: predicting the soil site flaky stripping disease of the arid region by a ridge regression model, and comparing with the evaluation result in the step four;
the implementation method for predicting the ridge regression model comprises the following steps:
(1) Constructing a ridge regression model through SPSS software to obtain a predicted value of the flaky stripping morphological characteristic under the ridge regression model; respectively constructing a ridge regression model for the shell layer stripping thickness, the powder layer stripping thickness and the stripping area to obtain regression model coefficients, and respectively multiplying the intrinsic factors and the natural environment factors of the soil body by the regression model coefficients to obtain predicted values;
(2) Substituting the actual value of the flaky peeling morphology feature in the original numerical matrix constructed by the numerical values of each evaluation index of the arid region with a predicted value, and obtaining a new evaluation result by a rank sum ratio evaluation method as a predicted result;
(3) Analyzing and comparing the predicted result with the real result: the ridge regression model fitness test was performed by paired sample T test using SPSS software.
2. The method for evaluating the degree of development of site exfoliation according to claim 1, wherein the specific surface area is a positive indicator, and the shell exfoliation thickness, the powder exfoliation thickness, the exfoliation area, the disintegration rate, the liquid index, the porosity, the total salt soluble amount, the precipitation amount, the evaporation amount, the daily average air temperature, and the dryness are negative indicators.
3. The method for evaluating the degree of development of site exfoliation according to claim 1 or 2, wherein the method for calculating subjective weights by decision laboratory analysis is as follows:
1) Quantifying the interrelationship among the evaluation indexes in the evaluation index system to obtain a direct influence matrix O;
2) Normalizing the direct influence matrix O to obtain a normalized direct influence matrix N;
3) Calculating a comprehensive influence matrix t=n (I-N) -1 from the canonical direct influence matrix N; wherein I is an identity matrix, (I-N) -1 is the inverse of matrix (I-N);
4) Obtaining the centerness M j＝Dj+Cj of the jth evaluation index from the comprehensive influence matrix T, wherein the influence degree Degree of influence To synthesize the element values of the j-th row and i 1 -th column in the influence matrix T,For the element values of the ith 1 row and the jth column in the comprehensive influence matrix T, n is the number of evaluation indexes;
5) And normalizing the centrality M j to obtain the subjective weight w 1 of the j-th evaluation index.
4. The method for evaluating the degree of development of site exfoliation according to claim 3, wherein the method for determining the first objective weight by the independent weight coefficient method is as follows:
1) Determining the values of all evaluation indexes of the arid region and constructing an original numerical matrix;
2) Calculating complex correlation coefficients of each evaluation index and other evaluation indexes according to the original numerical matrix;
3) And carrying out normalization processing on the reciprocal of the complex correlation coefficient to obtain a first objective weight w 2 of each evaluation index.
5. The method for evaluating the degree of development of the site exfoliation in accordance with claim 4, wherein the value on the diagonal of the direct influence matrix O is represented by 0, and the value of the other element value in the direct influence matrix O is determined by determining the relationship strength of two evaluation indexes by using a 5-level scale;
the normalization method is a range normalization method;
The complex correlation coefficient is
Wherein y represents an evaluation index value in the original value matrix,Represents the average value of all values under the evaluation index of the value y,A regression value representing the value y;
The regression value Is obtained by linear regression with all other values under the same evaluation index as independent variables.
6. The method for evaluating the degree of development of site delamination according to claim 4, wherein the method for determining the second objective weight of each evaluation index by the inverse entropy weight method comprises:
1) Determining the values of all evaluation indexes of the arid region and constructing an original numerical matrix;
2) Normalizing the element values of the numerical matrix to obtain a normalized matrix;
3) Calculating the proportion of the ith sample in the jth evaluation index to the evaluation index;
4) Calculating an inverse entropy value h j of the j-th evaluation index according to the specific gravity;
5) And calculating a second objective weight w 3 of the j-th evaluation index according to the inverse entropy value h j.
7. The method for evaluating the degree of development of site delamination as claimed in claim 6, wherein the specific gravity of the ith sample in all sample values in the jth evaluation index is
Wherein n ij represents a sample value of an ith arid region of a jth evaluation index in the normalized matrix, and m is the number of arid regions;
the inverse entropy value of the j-th evaluation index is
Second objective weight of jth evaluation indexN is the number of evaluation indexes;
The combining weights Wherein w 1 is a subjective weight, w 2 is a first objective weight, and w 3 is a second objective weight;
the weight adjustment method in the third step comprises the following steps: the numerical value of each evaluation index for each arid region is multiplied by the combining weight.
8. The method for evaluating the degree of development of site exfoliation according to any one of claims 1 and 4 to 7, wherein the method for listing the rank matrix by the overall rank sum method is as follows: sequencing according to the index value of each evaluation index, wherein the positive index is ranked from small to large, the negative index is ranked from large to small, and the average ranks of the same evaluation index data are ranked to obtain a rank matrix R a＝(Rij)m×n;
The calculation formula of the rank sum ratio WRSR value is as follows:
Wherein WRSR i is the rank sum ratio of the ith arid region, R ij is the rank of the jth evaluation index of the ith arid region, and W j represents the weight of the jth evaluation index;
According to the accumulated frequency query percentage and the probability unit comparison table, converting the percentage form of the evaluation rank number, namely the evaluation rank number/n 100%, into a probability unit;
4 steps in the step, 4 steps are classified according to the WRSR estimated value, and are positioned in [ -, 0.2392) to be low in development degree, [0.2392,0.4794) is moderately developed, [0.4794,0.7196) is highly developed, [0.7196 ] the number of the cells, +++) is very developmental.
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