CN101853290A - Meteorological service performance evaluation method based on geographical information system (GIS) - Google Patents

Meteorological service performance evaluation method based on geographical information system (GIS) Download PDF

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CN101853290A
CN101853290A CN201010182343A CN201010182343A CN101853290A CN 101853290 A CN101853290 A CN 101853290A CN 201010182343 A CN201010182343 A CN 201010182343A CN 201010182343 A CN201010182343 A CN 201010182343A CN 101853290 A CN101853290 A CN 101853290A
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performance evaluation
data
meteorological services
evaluation
meteorological
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毕硕本
董学士
梁静涛
王启富
何晓庆
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Nanjing University of Information Science and Technology
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Nanjing University of Information Science and Technology
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    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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    • Y02A90/10Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention discloses a meteorological service performance evaluation method based on a geographical information system (GIS), which is used for carrying out meteorological service performance analysis and evaluation by comprehensively utilizing a neural network method, an analytic hierarchy process, a structural equation method, a gray system method and a statistical analysis technique through using corresponding social statistics and survey data. The method is used for carrying out uniform control and management on hydrologic information, humanity information and meteorological information in a researched region by using a cartographic database and remote sensing data as basis geospatial data through using population data, casualty loss data and geographical hydrologic thematic data based on a GIS technology and a database technology by adopting a design mode of centralized management and maintenance. The invention realizes graphic and feature integrated maintenance management, comprehensive query, space analysis, thematic map, simulation analysis and meteorological service evaluation functions, improves the informatization level and the aid decision making capability of the meteorological service evaluation, and provides aid decision basis for the geographical hydrologic management and the meteorological service performance evaluation.

Description

Meteorological Services performance evaluation method based on Geographic Information System
Technical field
The present invention is the Meteorological Services performance evaluation method based on Geographic Information System, mainly comprises attribute query, thematic maps, spatial analysis, sunykatuib analysis, forecast analysis and Meteorological Services performance evaluation function.Relevant knowledge comprises Geographic Information System, meteorological science and technology, computer technology, computerized mapping, database technology, statistics etc.
Background technology
In September, 1994 World Meteorological Organization (WMO) (WMO) has held the ad hoc meeting of for the second time meteorological hydrology service of nineteen nineties performance evaluation, meeting thinks that the Meteorological Services performance evaluation is important and a valuable job, also is a problem that difficulty is bigger.The expert of country such as the U.S., USSR (Union of Soviet Socialist Republics), Britain carried out analyzing and estimating to the Meteorological Services benefit from different perspectives, but did not form a kind of internationally recognized evaluation method and evaluation model so far as yet.
Domestic many experts also once explored the Meteorological Services economical efficiency, had organized " meteorological scientific and technological economic benefit research " and " Meteorological Services evaluation of economic benefit method " research by China Meteorological Administration respectively nineteen eighty-three, 1987.But because condition restriction and operability are not strong, achievement in research fails to promote the use of.Along with improving constantly of Meteorological Services level, the research of Meteorological Services performance evaluation seems more and more important, how to carry out the research of Meteorological Services performance evaluation with scientific and rational method, is the difficult problem of pendulum in face of many researchists.
The present domestic Meteorological Services performance evaluation Geographic Information System method of still not having such based on Geographic Information System.The present invention is applied to technology such as geosystem system in the Meteorological Services performance evaluation, utilize the powerful geodata administrative analysis of GIS, visual and science calculating, express-analysis and assessment casualty loss, monitoring and simulation flood inundation on tracks and set up the prediction scheme function of taking precautions against natural calamities, for all departments provide good aid decision making data, be intended to improve the decision-making level and the efficiency of service of Meteorological Services performance evaluation.
List of references
[1] economic benefit compiling group. Meteorological Services economic benefit collection of translations [C]. Beijing: Meteorology Publishing House, 1986.
[2] Huang Zongjie. Meteorological Services benefit outline [M]. Meteorology Publishing House, 1996.
[3] China Meteorological Administration. public's Meteorological Services effectiveness qualitative assessment embodiment [G]. Beijing, 2006.
[4] Xu Zida, Guan Yexiang. flood damage countermeasure and performance evaluation thereof [M]. Nanjing: publishing house of Hohai University, 1996.
[5] Wang new life, Lu Dachun etc. the public of Anhui Province Meteorological Services performance evaluation [J]. meteorological science and technology, 2007,35 (6): 853-857.
[6]Wang?Xinsheng,Lu?Dachun,Wang?Labao.Hu?WujiuBenefit?Analysis?and?Assessment?ofPublic?Meteorological?Service?in?Anhui?Provinc[J].Meteorological?Science?andTechnology,2007,35(6):853-857.
[7] Li Feng etc. Shandong public's Meteorological Services performance evaluation [J]. Shandong meteorology, 2007,27 (1): 22-24.
[8] the Pu plum is beautiful, Xie Lingyun, Liu Lizhong etc. Jiangsu Province's Meteorological Services benefit research (I) _ public's Meteorological Services performance evaluation [J]. and meteorological science, 1997,17 (2): 196-202.
[9] China Meteorological Administration. public's Meteorological Services performance evaluation report [C]. national Meteorological Services performance evaluation symposial collected works .2006:326-380.
Summary of the invention
The present invention is a geo-spatial data with map data base and remotely-sensed data, utilize demographic data, casualty loss data, meteorological hydrology thematic data, based on GIS technology and database technology, the Design Mode that adopts centralized management to safeguard carries out unified control and management to the hydrographic information in the research area, weather information, humane information.This invention realizes graphic and feature integrated maintenance management, comprehensive inquiry, spatial analysis, thematic maps, three-dimensional simulation, forecast analysis and Meteorological Services performance evaluation function, for meteorological hydrologic management and Meteorological Services performance evaluation provide the aid decision making foundation.
The present invention adopts following technical scheme for achieving the above object:
1) obtaining of spatial data: utilize corresponding GIS software that existing raster data or map datum lack of standardization are carried out digitized processing, make a width of cloth digital map;
2) obtaining of attribute data: comprise the required hydrology data of Meteorological Services performance evaluation, social investigation data, flood lost data;
3) utilize geographical information technology and database technology,, set up corresponding spatial database and attribute database according to hydrology data, social investigation data, the flood lost data after screening, the statistical treatment;
4) utilize social statistics data and enquiry data, carry out assessment methods of shallow price, the pay structure of method, cost saving method Meteorological Services assessment models voluntarily;
5) utilization neural network method, analytical hierarchy process, structural equation method, gray system method, data envelopment method are utilized corresponding social statistics data and enquiry data, carry out Meteorological Services performance evaluation and analysis.
6) be the nucleus module constructing system with Meteorological Services performance evaluation method, design and exploitation Meteorological Services performance evaluation Geographic Information System.
Preferably, the construction method of described assessment methods of shallow price Meteorological Services performance evaluation model is as follows:
W=PCT(M 1G 1/N 1+M 2G 2/N 2),
Parameter in the formula: P is the TV coverage rate; C is a shadow price; T is the temporal extension coefficient; M 1Expression city resident number; M 2Expression cottar number; G 1The expression city resident listens to the total degree of watching weather forecast; G 2The expression cottar listens to the total degree of watching weather forecast; N 1City resident's number in the expression actual recovered sample survey table; N 2Cottar's number in the expression actual recovered sample survey table.
Preferably, the construction method of described voluntary paying method Meteorological Services performance evaluation model is as follows:
W = P [ ( M 1 Σ i = 1 n C i B 1 i / N 1 ) + ( M 2 Σ i = 1 n C i B 2 i / N 2 ) ] ,
Parameter in the formula: W: public's Meteorological Services assessment benefit; P is the TV coverage rate; M 1Expression city resident number; M 2Expression cottar number; N 1City resident's number in the expression actual recovered sample survey table; N 2Cottar's number in the expression actual recovered sample survey table; C iThe intermediate value value of representing each voluntary paying grade; B 1Represent the city resident's number in each paying grade; B 2Represent the cottar's number in each paying grade; N represents the number of degrees of paying voluntarily.
Preferably, the construction method of described cost saving method Meteorological Services performance evaluation model is as follows:
1. computational mathematics model per capita
W = P [ ( M 1 / N 1 Σ i = 1 n D i E 1 i ) + ( M 2 / N 2 Σ i = 1 n D i E 2 i ) ] ,
Parameter in the formula: P is the TV coverage rate; M 1Expression city resident number; M 2Expression cottar number; N 1City resident's number in the expression actual recovered sample survey table; N 2Cottar's number in the expression actual recovered sample survey table; N: the number of degrees of expression cost saving; D i=C i: the cost saving grade classification is with voluntary paying grade classification; E 1i: the city resident's number in each cost saving grade; E 2i: the cottar's number in each cost saving grade;
2. by family computational mathematics model
W = F Σ i = 1 n D i E i / M ,
Coefficient in the formula:
W: Meteorological Services performance evaluation benefit; F: family's amount; D iThe grade intermediate value of cost saving method; N: the number of degrees of expression cost saving; M: reclaim the questionnaire number; E i: the resident's number in each cost saving grade.
Preferably, the construction method of described neural network method Meteorological Services performance evaluation model is as follows:
Step 6012 initiation parameter;
Step 6013 input training sample;
Step 6014 is calculated hidden layer and each neuron output of output layer;
Step 6015 computational grid error;
Step 6017 is calculated each layer error signal;
Step 6018 is adjusted each layer weights;
Whether step 6019a supervising network total error reaches accuracy requirement, if execution in step 60191 then; Otherwise change step 60190.
Preferably, the construction method of described analytical hierarchy process Meteorological Services performance evaluation model is as follows:
Step 6021 is established the scale that quantification is judged in thinking;
Step 6022 is set up hierarchy Model, and this model comprises destination layer, rule layer, solution layer;
Step 6023 structure judgment matrix, the method that utilization is compared is in twos relatively marked in twos to each coherent element, according to some indexs in middle layer, can obtain some judgment matrixs that compare in twos;
Step 6024 is calculated single preface weight vector and is done consistency check;
Each paired comparator matrix is calculated eigenvalue of maximum and characteristic of correspondence vector thereof, utilize coincident indicator, coincident indicator and Consistency Ratio are done consistency check at random: if upcheck, the proper vector after the normalization is weight vector; If do not pass through, re-construct paired comparator matrix;
Step 6025 is calculated total ordering weight vector and is done consistency check;
Calculate the weight vector of orlop to the total ordering of the superiors, utilize total ordering Consistency Ratio to test: if pass through, then the result who represents according to total ordering weight vector makes a strategic decision, otherwise need rethink model or re-construct the bigger paired comparator matrix of those Consistency Ratios.
Preferably, the construction method of described structural equation method Meteorological Services performance evaluation model is as follows:
The analysis of step 6031 practical problems;
Step 6032 proposes Research Hypothesis;
Step 6033 makes up model: select to determine variable, analyze cause-effect relationship, build path figure;
Step 6034 questionnaire, sampling and data acquisition;
Step 6035 model fitting solving model parameter;
Step 6036 model evaluation;
The correction of step 6037 model;
Step 6038 finishes.
Preferably, the construction method of described gray system method Meteorological Services performance evaluation model is as follows:
The analysis of step 6041 evaluation index;
Step 6042 evaluation index Weight Determination;
The analyzing and processing of step 6043 evaluation index;
Step 6044 is set up triangle albefaction weight function and degree of membership;
Step 6045 evaluation grade is determined.
Preferably, the construction method of described data envelopment method Meteorological Services performance evaluation model is as follows:
Step 6051 determines to estimate purpose;
Step 6052 trade-off decision unit;
Step 6053 is set up the input and output index system;
Step 6054 is selected data envelopment method model;
Step 6055 is carried out the evaluation analysis of data envelopment method;
Step 6056 is adjusted the input and output index system;
Step 6057 provides the comprehensive evaluation analysis conclusion.
The beneficial effect of technical scheme provided by the invention is:
1) integrated use neural network method of the present invention, analytical hierarchy process, structural equation method, gray system method statistical analysis technique, the method and the technology of research Meteorological Services benefit, form by " Meteorological Services performance evaluation general character model ", on Meteorological Services performance evaluation theory and method, realize innovation;
2) the application of the invention is carried out the Meteorological Services performance evaluation and can be obtained the Meteorological Services performance evaluation conclusion that some can carry out international comparison, as the meteorological input-output ratio of China (present 1: 40, and be commonly 1 in the world: objective basis 10-20), can strive for more state revenue and expenditure support on the one hand, can weigh the ability and the level of each business department of Meteorological Services, province, city and region's Meteorological Services on the other hand, instruct Meteorological Services work to optimize structure and winding level;
3) the present invention utilizes the powerful geodata administrative analysis of GIS, visual and science computing function, express-analysis and assessment casualty loss, monitoring and simulation flood inundation on tracks, and set up the prediction scheme function of taking precautions against natural calamities, for relevant departments provide good aid decision making data, improve the decision-making level and the efficiency of service of Meteorological Services performance evaluation.
Description of drawings
Fig. 1 is based on the process flow diagram of the Meteorological Services performance evaluation method of Geographic Information System;
Fig. 2 is neural network method Meteorological Services performance evaluation implementing procedure figure;
Fig. 3 is analytical hierarchy process Meteorological Services performance evaluation implementing procedure figure;
Fig. 4 is structural equation method Meteorological Services performance evaluation implementing procedure figure;
Fig. 5 is gray system method Meteorological Services performance evaluation implementing procedure figure;
Fig. 6 is data envelopment method Meteorological Services performance evaluation implementing procedure figure;
Fig. 7 is Meteorological Services performance evaluation Geographic Information System implementing procedure figure;
Fig. 8 is Meteorological Services performance evaluation Geographic Information System development structure figure.
Embodiment
Below in conjunction with accompanying drawing and instantiation the Meteorological Services performance evaluation method based on Geographic Information System of the present invention is described in further detail.
As shown in Figure 1, the present invention includes following steps:
Step 20 is obtained spatial data and attribute data.Utilize corresponding GIS software that raster data or map datum lack of standardization are carried out digitized processing, make the digital map that a width of cloth has actual application value.Attribute data comprises the required hydrology data of Meteorological Services performance evaluation, social investigation data, flood lost data;
Step 30 is utilized geographic information system technology and database technology, according to hydrology data, social investigation data, the flood lost data after screening, the statistical treatment, sets up corresponding spatial database and attribute database;
Step 40 makes up Meteorological Services performance evaluation model, comprises quantitative Meteorological Services performance evaluation model and qualitative Meteorological Services assessment models;
Step 50 is carried out the structure (public's Meteorological Services performance evaluation) of quantitative Meteorological Services performance evaluation method according to social statistics data and enquiry data;
The content that mainly comprises according to Fig. 1 public's Meteorological Services performance evaluation is:
The structure of step 501 assessment methods of shallow price Meteorological Services performance evaluation model;
According to the public is listened to the investigation of watching the weather forecast number of times every day, calculate and listen to the total degree of watching weather forecast every year, calculate the benefit of annual public's Meteorological Services with shadow price.
1. mathematical model
W=PCT(M 1G 1/N 1+M 2G 2/N 2),
Parameter in the formula: P is the TV coverage rate; C is a shadow price; T is the temporal extension coefficient, is unit with the year, and value is 365.M 1Expression city resident number; M 2Expression cottar number; G 1The expression city resident listens to the total degree of watching weather forecast; G 2The expression cottar listens to the total degree of watching weather forecast; N 1City resident's number in the expression actual recovered sample survey table; N 2Cottar's number in the expression actual recovered sample survey table.
Step 502 is the structure of paying method Meteorological Services performance evaluation model voluntarily;
1. mathematical model
W = P [ ( M 1 Σ i = 1 n C i B 1 i / N 1 ) + ( M 2 Σ i = 1 n C i B 2 i / N 2 ) ] ,
Parameter in the formula: W: public's Meteorological Services assessment benefit; P, M 1, M 2, N 1, N 2With the shadow price mathematical model; C i: the intermediate value value of each grade of paying voluntarily; B 1: the city resident's number in each paying grade; B 2: the cottar's number in each paying grade; N: the number of degrees that expression is paid voluntarily.
The structure of step 503 cost saving method Meteorological Services performance evaluation model.
To " in daily life, you think that weather forecast can be your individual every year or how many expenses family saves ".Two kinds of methods are arranged: calculate per capita and calculate by the family.
1. computational mathematics model per capita
W = P [ ( M 1 / N 1 Σ i = 1 n D i E 1 i ) + ( M 2 / N 2 Σ i = 1 n D i E 2 i ) ] , Parameter in the formula:
P, M J, M 2, N 1, N 2Same assessment methods of shallow price; N: the number of degrees of expression cost saving;
D i=C i: the cost saving grade classification is with voluntary paying grade classification;
E 1i: the city resident's number in each cost saving grade;
E 2i: the cottar's number in each cost saving grade.
2. by family computational mathematics model
W = F Σ i = 1 n D i E i / M ,
Coefficient in the formula:
W: Meteorological Services performance evaluation benefit; F: family's amount; D iThe grade intermediate value of cost saving method; N: the number of degrees of expression cost saving; M: reclaim the questionnaire number; E i: the resident's number in each cost saving grade.
Step 60 is carried out the structure of qualitative Meteorological Services performance evaluation method according to social statistics data and enquiry data.Integrated use neural network method, analytical hierarchy process, structural equation method, gray system method statistical analysis technique utilize corresponding social statistics data and enquiry data, carry out the structure of Meteorological Services performance evaluation method and technology.
The structure of step 601 neural network method Meteorological Services performance evaluation model.Step in conjunction with Fig. 2 neural network method is as follows:
Neural network method input vector is X=(x 1, x 2..., x n) TThe hidden layer output vector is Y=(y 1, y 2..., y m) TOutput layer output vector: O=(o 1, o 2..., o l) TThe desired output vector is d=(d 1, d 2..., d l) TInput layer represents with V to the weight matrix between the hidden layer,
V=(V 1, V 2..., V m), column vector V wherein j(1≤j≤m) is the weight vector of j neuron correspondence of hidden layer; Hidden layer is represented with W to the weight matrix between the output layer, W=(W 1, W 2..., W l), column vector W wherein k(1≤k≤l) is the weight vector of k neuron correspondence of output layer.Satisfy following relation for above each vector:
For output layer, have: o k=f (net k) (k=1,2 ..., l) and
For hidden layer, have: y j=f (net j) (j=1,2 ..., m) and
Transfer function selects formula to select unipolarity S function.Promptly F ' (x)=f (x) [1-f (x)].
Step 6012 initiation parameter;
Weight matrix W, V are composed random number, sample mode counter p and frequency of training counter q are put 1, error E puts 0, and learning rate η is made as the decimal of (0,1), the precision E that reaches behind the network training MinBe made as a positive decimal.
Step 6013 input training sample calculates each layer output with current sample X P, d PTo Vector Groups X, d assignment, use o k=f (net k) (k=1,2 ..., l) and y j=f (net j) (j=1,2 ..., m) formula calculates each component among O and the Y;
Step 6014 is calculated hidden layer and each neuron output of output layer;
Step 6015 computational grid error is established total P to training sample, and the corresponding different sample of network has different error E P, available wherein the maximum E MaxRepresent the total error of network.
The network error formula:
Step 6017 is calculated each layer error signal;
Application of formula With Calculate With
Step 6018 is adjusted each layer weights;
Application of formula Δ w Jk=η (d k-o k) o k(1-o k) y jWith Calculate each component among W, the V.
Whether step 6019a supervising network total error reaches accuracy requirement, if execution in step 60191 then; Otherwise change step 60190.
The structure of step 602 analytical hierarchy process Meteorological Services performance evaluation model.As follows in conjunction with Fig. 3 analytical hierarchy process step:
Step 6021 is established the scale that quantification is judged in thinking;
Structural equation method (AHP) method is generally introduced the important relatively ratio scale of nine fens positions according to psychologic requirement when the relative significance level of index is measured, constitute a judgment matrix A, each element P in the matrix A IjRepresent that horizontal index is to each row index P iThe fiducial value in twos (P is the next stage index of A) of relative significance level.
Step 6022 is set up hierarchy Model, and this model comprises destination layer, rule layer, solution layer;
Step 6023 structure judgment matrix;
The method that utilization is compared is in twos relatively marked in twos to each coherent element, according to some indexs in middle layer, can obtain some judgment matrixs that compare in twos.
Step 6024 is calculated single preface weight vector and is done consistency check;
Each paired comparator matrix is calculated eigenvalue of maximum and characteristic of correspondence vector thereof, utilize coincident indicator, coincident indicator and Consistency Ratio are done consistency check at random.If upcheck, proper vector (after the normalization) is weight vector; If do not pass through, need re-construct paired comparator matrix.
1. at first each column element of judgment matrix is made normalized, the general term of its element is
P ‾ ij = p ij Σ k = 1 n p kj ‾ ( i , j = 1,2,3 , . . . , n ) ,
2. the judgment matrix after each row normalization is pressed the row addition
W ‾ i = Σ j = 1 n p ij ‾ ( i , j = 1,2,3 , . . . , n ) ,
3. again with vector Normalization obtains
W i = W i ‾ Σ j = 1 n W j ‾ ( i , j = 1,2,3 , . . . , n ) ,
The W=[W that obtains 1, W 2..., W n] TBe the proper vector of asking.
4. the maximum characteristic root that calculates judgment matrix is
In the formula: (PW) iI component element for PW.
5. carry out consistency check.
Calculate coincident indicator CI.
CI = λ max - n n - 1 ,
Calculate coincident indicator CR at random.
CR = CI RI ,
Generally speaking CR is littler, and the consistance of judgment matrix it has been generally acknowledged that CR<0.1 o'clock better, and judgment matrix satisfies consistency check; Otherwise the reply judgment matrix is suitably adjusted.RI is the mean random coincident indicator in the formula, is the mean value of the coincident indicator calculated of the judgment matrix that takes place at random of enough a plurality of bases.
Step 6025 is calculated total ordering weight vector and is done consistency check.
Calculate the weight vector of orlop to the total ordering of the superiors.Utilize total ordering Consistency Ratio to test.If pass through, then can make a strategic decision, otherwise need rethink model or re-construct the bigger paired comparator matrix of those Consistency Ratios according to the result that total ordering weight vector is represented.
The structure of step 603 structural equation method Meteorological Services performance evaluation model.As follows in conjunction with Fig. 4 structural equation method step:
The analysis of step 6031 practical problems;
Assessment is analyzed to Meteorological Services, determines corresponding research approach.This step is the model developing stage.The starting point of the equation of structure is to set up concrete Causal model for the cause-effect relationship of supposing between observation variable, just can be with the causal relation between the clear and definite named variable of path profile.
Step 6032 proposes Research Hypothesis;
This step is the model hypothesis stage, is to express theoretical model with linear equation system.
Step 6033 makes up model: select to determine variable, analyze cause-effect relationship, build path figure;
1. structural model
For the relation between the latent variable, can be write as the following equation of structure:
η=Bη+Γξ+ζ,
Wherein, η is interior living latent variable (Endogenous Observable is subjected to the influence of its dependent variable in the model), and ξ is external latent variable (Exogenous Observable is not influenced by its dependent variable self in the model, only influences its dependent variable); B is interior living latent variable matrix of coefficients, has described the relation between the interior living latent variable η; Γ is external latent variable matrix of coefficients, has described the influence of external latent variable ξ to interior living latent variable η; ζ is the residual error item of the equation of structure, has reflected that η fails the part explained in equation.
2. measurement model
For the relation between index and the latent variable, write as following measurement equation usually:
X=Λxξ+δY=Λyη+ε,
Wherein X is the observational variable of external latent variable ξ; Λ x is the relational matrix between observational variable X and the external latent variable ξ, is made of the factor loading matrix of X on ξ; δ is the measuring error of X; Y is the observational variable of interior living latent variable η; Λ y is the relational matrix between observational variable Y and the interior living latent variable η, is made of the factor loading matrix of Y on η; ε is the measuring error of Y.
Step 6034 questionnaire, sampling and data acquisition;
This step is that the researchist carries out with regard to certain (or certain aspect) concrete problem, just will go to sample and measure the data that can measure according to the index of setting, to make the usefulness of model analysis.
Step 6035 model fitting solving model parameter;
This process is also named model fitting (model fitting) or is made model estimate (model estimating).This method uses maximum likelihood estimate, least square method to remove model of fit.
1. maximum likelihood estimate mathematical model
F ML=log|∑(θ)|+tr[S∑ -1(θ)]-log|S|-(p+q),
Wherein, tr[S ∑ -1(θ)] be matrix [S ∑ -1Diagonal entry sum (θ)]; Log| ∑ (θ) | be matrix | ∑ (θ) | the logarithm of determinant, log|S| is the logarithm of the determinant of matrix S; P, q are respectively the numbers of Nei Sheng and external observational variable.
Matrix ∑ (θ) and S are approaching more, then log| ∑ (θ) | and log|S| is approaching more, the tr[S ∑ -1(θ)] then more near tr[I], promptly more near p+q, thereby make F MLApproach minimum.
Adopting maximum likelihood estimate to require observational variable is continuous variable and Normal Distribution, and the skewness distribution can cause parameter estimation poor effect and the standard error of mistake and higher value.Make matrix ∑ (θ) and S more approaching, need large sample and ∑ (θ) to have inverse matrix, if ∑ (θ) does not exist then can't find the solution.
2. least square method mathematical model
F ULS = 1 2 tr [ ( S - Σ ( θ ) ) 2 ] ,
Wherein, S-∑ (θ) is a residual matrix.Make F ULSReach minimum estimation and be called non-weighted least-squares estimation.Make F ULSReach minimum, must make tr (S-∑ (θ)) 2Minimum just makes corresponding element gap minimum in each element and the matrix S in the matrix ∑ (θ).
Step 6036 model evaluation;
Whether this step is in existing evidence and theoretical scope, investigate the model that is proposed and can be made explanations in fullest ground by observed data.Revise a model that match is bad, can change its measurement model (measurement model), increase structural parameters (structural parameters), it is relevant perhaps to set some error term (measurement errors orstructural errors), perhaps limits some structural parameters.
The correction of step 6037 model;
This step is that model is carried out corresponding modification, with going this new model is tested with the same observation data, up to finding one reasonably to the data match preferably, can make the model of reasonable dismissal simultaneously again to the fact again.
Step 6038 finishes.
The structure of step 604 gray system method Meteorological Services performance evaluation model.Step in conjunction with Fig. 5 gray system method is as follows:
The analysis of step 6041 evaluation index;
Index to the Meteorological Services performance evaluation is analyzed.
Step 6042 evaluation index Weight Determination;
For Weight Determination, at first raw data is carried out standardization, calculate the related coefficient between the variable, form correlation matrix, then computation of characteristic values and proper vector are calculated contribution rate and accumulation contribution rate in view of the above, generally get the eigenwert that the accumulation contribution rate reaches 85% or more and are the major component of correspondence, calculate the major component load capacity then, calculate at last the principal component scores of each variable according to proper vector and major component load capacity.
The analyzing and processing of step 6043 evaluation index;
Meteorological Services performance evaluation index { X 1, X 2..., X j..., X mAnd corresponding weights { K 1, K 2..., K j..., K m, the grade ash class that requires according to assessment is counted s then, and the span of each index also correspondingly is divided into S grey class.For example, with X jSpan [a of index 1, a S+1] be divided into [a 1, a 2] ..., [a K-1, a k] ..., [a S-1, a s], [a s, a S+1].
Step 6044 is set up triangle albefaction weight function and degree of membership;
After every index carried out normalized, can set up the triangle albefaction weight function of each evaluation index, and calculate the degree of membership of each evaluation index about corresponding Meteorological Services benefit grade ash class about respective level ash class.
Order The albefaction weight function value that belongs to k grade ash class is 1, Starting point a with (k-1) individual grade ash class K-1Terminal point a with (k+1) individual grade ash class K+2Link obtains X jIndex is about the triangle albefaction weight function of k grade ash class J=1,2..., m, k=1,2 ..., s, its expression formula is as follows.
f j k ( X ) 0 , X ∉ [ a k - 1 , a k + 2 ] X - a k - 1 λ k - a k + 1 X ∉ [ a k - 1 , λ k ] , a k + 2 - X a k + 2 - λ k X ∉ [ λ k , a k + 2 ]
Wherein, Calculate each evaluation index X j(j=1,2 ..., m) belong to grade ash class k (k=1,2 ... S) degree of membership
Step 6045 evaluation grade is determined.
Calculate evaluated grade ash class k (k=1,2 ..., comprehensive cluster coefficients δ s) k
δ k = Σ j m f j k ( X j ) g η j ,
Wherein, η jBe evaluation index X j(j=1,2 ..., the m) weight in comprehensive cluster.
By Judge evaluated grade gray scale k.
The structure of step 605 data envelopment method Meteorological Services performance evaluation model.Step in conjunction with Fig. 6 data envelopment method is as follows:
Step 6051 determines to estimate purpose;
Step 6052 trade-off decision unit;
Step 6053 is set up the input and output index system;
Step 6054 is selected data envelopment method model;
Choose the effective and effective comprehensive evaluation model C of scale of assessment technique simultaneously 2R.C 2The master pattern of R as shown in the formula, suppose to have n DMU, each DMU has m kind input and s kind to export DMU jInput and output be
x j=(x 1j,x 2j,...,x mj) T,y j=(y 1j,y 2j,...,y sj) T,j=1,2,...,n.
max u T y 0 v T x 0 s . t . u T y i v T x j ≤ 1 , u ≥ 0 , v ≥ 0
Implication is to satisfy Make objective function under the condition Maximum, v=(v 1, v 2..., v m) TAnd u=(u 1, u 2..., u s) TThe weight coefficient of representing input of m kind and the output of s kind respectively.
Following formula can be converted into following formula by the dual linear programming of equivalence:
min θ s . t Σ j = 1 n x j λ ≤ θ x 0 , Σ j = 1 n y j λ j ≥ v 0 λ j ≥ 0 , j = 1,2 , . . . , n , θ ∉ E 1 +
Find the solution for convenience, main utilization is through the dual linear programming conversion, the model after non-Archimedes infinitesimal (ε) is handled again, as shown in the formula:
min [ θ - e T s - + e T s + ] s . t . Σ j = 1 n x j λ j + s - = θ x 0 , Σ j = 1 n y j λ j - s + = y 0 λ j ≥ 0 , j = 1,2 , . . . , n , θ ∉ E 1 + , s - ≥ 0
E wherein T=(1,1 ..., 1) T, if optimum solution θ 0, λ 0jλ Oj, j=1,2 ..., n, s -, s +Satisfy θ 0=1, s -=0, s +=0 claims DMU J0For data envelopment method (DEA) effective.
Step 6055 is carried out the evaluation analysis of data envelopment method;
Step 6056 is adjusted the input and output index system;
Step 6057 provides the comprehensive evaluation analysis conclusion.
Step 70 is the nucleus module design system with Meteorological Services performance evaluation method, and design and exploitation are based on the Meteorological Services performance evaluation business platform of Geographic Information System.As follows in conjunction with Fig. 7 Meteorological Services performance evaluation Geographic Information System implementation step:
Step 701 demand analysis;
Determine Meteorological Services performance evaluation Geographic Information System, must finish which work, just this system is proposed complete, accurate, concrete requirement.
Step 702 feasibility analysis and primary design;
Feasibility analysis is mainly started with from technical feasibility, economic feasibility, social feasibility three aspects.
Step 703 detailed design;
This step is to determine how to realize desired system particularly, draws the accurate description to goal systems, thereby designs " blueprint " of program, can write out actual program code according to this blueprint later on.
Step 704 database design;
Mainly comprise Logic Structure Design and physical design etc.
Step 705 software development;
Step 706 is set up database;
Step 707 program composition;
Step 708 software test, debugging, examination.
This step is tested functional module, mainly comprises unit testing, integration testing, affirmation test etc.; The mistake that program exists is debugged; Examination is installed to relevant departments by the system that test is passed through.
The incorporate multi-functional operation of picture and text of the present invention interface:
The present invention is applied to the GIS technology in the Meteorological Services performance evaluation, utilize the powerful geodata administrative analysis of GIS, visual and science calculating, express-analysis and assessment casualty loss, monitoring and simulation flood inundation on tracks and the foundation prediction scheme function of taking precautions against natural calamities, for all departments provide good aid decision making data, thus the aid decision making level and the efficiency of service of raising Meteorological Services performance evaluation.
The combination of spatial database of the present invention and attribute database:
The present invention is a geo-spatial data with map data base and corresponding remotely-sensed data, utilize demographic data, casualty loss data, meteorological hydrology thematic data, based on GIS technology and database technology, the Design Mode that adopts centralized management to safeguard, integrated data in the survey region is carried out effective organization and management, the combination of implementation space database and attribute database.
Basic operation module of the present invention:
1) file operation: comprise and open the workspace, close the workspace, preserve, save as, add data, print plot, withdraw from.
2) map edit: comprise duplicate, cancel, shear, paste, repetition, deletion action.
3) map operation: comprise the control of figure layer, new window, amplify, dwindle, roaming, full figure display operation.
Spatial analysis of the present invention and attribute query module:
1) spatial analysis: comprise that overlay analysis, buffering are analyzed, space measurement.
Wherein, overlay analysis is finished the overlay analysis operation to various spatial entities; Buffering analysis is finished influences correlation analysis to entity buffering in the survey region; Space measurement comprises area measuring and distance measurements calculation.
2) attribute query: comprise fuzzy query, SQL query, locus inquiry.
Fuzzy query: input presentation-entity title or number, can inquire about; SQL query: be common a kind of inquiry in the inquiry, inquire about after corresponding condition and statement are set in query interface; Locus inquiry: be primarily aimed at spatial relation and inquire about.
Thematic maps of the present invention and sunykatuib analysis module:
1) thematic maps: comprise monodrome thematic map, scope segmentation thematic map, dot density thematic map, statistics thematic map, grade symbol thematic map, label thematic map and self-defined special topic.
2) sunykatuib analysis: finish the simulation of the flood inundation on tracks in the survey region, the three-dimensional scenic of relevant informations such as the flood inundation on tracks range computation and the meteorological hydrology is browsed, inquires about, is located.
Forecast analysis module of the present invention:
Forecast analysis: extract the descriptive model of data, be used to predict following trend.This module can be carried out time series forecasting to relevant meteorological disaster, for all departments provide corresponding decision-making foundation; Also can predict corresponding data situation in the following certain hour according to the data and the result of existing Meteorological Services performance evaluation, the present invention is according to meteorological service information system and professional needs, the algorithm that is mainly concerned with has: neural network method, gray system method, gene expression programming, but be not limited to this several algorithms.
Meteorological Services performance evaluation model of the present invention:
Mainly comprise qualitative evaluation and two kinds of Meteorological Services performance evaluations of qualitative assessment method.The qualitative evaluation method that the present invention adopts comprises neural network method, analytical hierarchy process, structural equation method, gray system method, DEA method, but is not limited to this five kinds of methods; Quantitative evaluating method comprises cost saving method, pay method, assessment methods of shallow price voluntarily, but is not limited to this three kinds of methods.

Claims (9)

1. Meteorological Services performance evaluation method based on Geographic Information System is characterized in that:
1) obtaining of spatial data: utilize corresponding GIS software that existing raster data or map datum lack of standardization are carried out digitized processing, make a width of cloth digital map;
2) obtaining of attribute data: comprise the required hydrology data of Meteorological Services performance evaluation, social investigation data, flood lost data;
3) utilize geographical information technology and database technology, according to screen, hydrology data, social investigation data, flood lost data after the statistical treatment, set up corresponding spatial database and attribute database;
4) utilize social statistics data and enquiry data, carry out assessment methods of shallow price, the pay structure of method, cost saving method Meteorological Services assessment models voluntarily;
5) utilization neural network method, analytical hierarchy process, structural equation method, gray system method, data envelopment method are utilized corresponding social statistics data and enquiry data, carry out Meteorological Services performance evaluation and analysis.
2. as right 1 described Meteorological Services performance evaluation method, be characterised in that the construction method of described assessment methods of shallow price Meteorological Services performance evaluation model is as follows based on Geographic Information System:
W=PCT(M 1G 1/N 1+M 2G 2/N 2),
Parameter in the formula: P is the TV coverage rate; C is a shadow price; T is the temporal extension coefficient; M 1Expression city resident number; M 2Expression cottar number; G 1The expression city resident listens to the total degree of watching weather forecast; G 2The expression cottar listens to the total degree of watching weather forecast; N 1City resident's number in the expression actual recovered sample survey table; N 2Cottar's number in the expression actual recovered sample survey table.
3. as right 1 described Meteorological Services performance evaluation method, be characterised in that the construction method of described voluntary paying method Meteorological Services performance evaluation model is as follows based on Geographic Information System:
W = P [ ( M 1 Σ i = 1 n C i B 1 i / N 1 ) + ( M 2 Σ i = 1 n C i B 2 i / N 2 ) ] ,
Parameter in the formula: W: public's Meteorological Services assessment benefit; P is the TV coverage rate; M 1Expression city resident number; M 2Expression cottar number; N 1City resident's number in the expression actual recovered sample survey table; N 2Cottar's number in the expression actual recovered sample survey table; C iThe intermediate value value of representing each voluntary paying grade; B 1Represent the city resident's number in each paying grade; B 2Represent the cottar's number in each paying grade; N represents the number of degrees of paying voluntarily.
4. as right 1 described Meteorological Services performance evaluation method, be characterised in that the construction method of described cost saving method Meteorological Services performance evaluation model is as follows based on Geographic Information System:
1. computational mathematics model per capita
W = P [ ( M 1 / N 1 Σ i = 1 n D i E 1 i ) + ( M 2 / N 2 Σ i = 1 n D i E 2 i ) ] ,
Parameter in the formula:
P is the TV coverage rate; M 1Expression city resident number; M 2Expression cottar number; N 1City resident's number in the expression actual recovered sample survey table; N 2Cottar's number in the expression actual recovered sample survey table; N: the number of degrees of expression cost saving; D i=C i: the cost saving grade classification is with voluntary paying grade classification; E 1i: the city resident's number in each cost saving grade; E 2i: the cottar's number in each cost saving grade;
2. by family computational mathematics model
W = F Σ i = 1 n D i E i / M ,
Coefficient in the formula:
W: Meteorological Services performance evaluation benefit; F: family's amount; D iThe grade intermediate value of cost saving method; N: the number of degrees of expression cost saving; M: reclaim the questionnaire number; E i: the resident's number in each cost saving grade.
5. as right 1 described Meteorological Services performance evaluation method, be characterised in that the construction method of described neural network method Meteorological Services performance evaluation model is as follows based on Geographic Information System:
Step 6012 initiation parameter;
Step 6013 input training sample calculates each layer output;
Step 6014 is calculated hidden layer and each neuron output of output layer;
Step 6015 computational grid error;
Step 6017 is calculated each layer error signal;
Step 6018 is adjusted each layer weights;
Whether step 6019a supervising network total error reaches accuracy requirement, if execution in step 60191 then; Otherwise change step 60190.
6. as right 1 described Meteorological Services performance evaluation method, be characterised in that the construction method of described analytical hierarchy process Meteorological Services performance evaluation model is as follows based on Geographic Information System:
Step 6021 is established the scale that quantification is judged in thinking;
Step 6022 is set up hierarchy Model, and this model comprises destination layer, rule layer, solution layer;
Step 6023 structure judgment matrix, the method that utilization is compared is in twos relatively marked in twos to each coherent element, according to some indexs in middle layer, can obtain some judgment matrixs that compare in twos;
Step 6024 is calculated single preface weight vector and is done consistency check;
Each paired comparator matrix is calculated eigenvalue of maximum and characteristic of correspondence vector thereof, utilize coincident indicator, coincident indicator and Consistency Ratio are done consistency check at random: if upcheck, the proper vector after the normalization is weight vector; If do not pass through, re-construct paired comparator matrix.
Step 6025 is calculated total ordering weight vector and is done consistency check;
Calculate the weight vector of orlop to the total ordering of the superiors, utilize total ordering Consistency Ratio to test: if pass through, then the result who represents according to total ordering weight vector makes a strategic decision, otherwise need rethink model or re-construct the bigger paired comparator matrix of those Consistency Ratios.
7. as right 1 described Meteorological Services performance evaluation method, be characterised in that the construction method of described structural equation method Meteorological Services performance evaluation model is as follows based on Geographic Information System:
The analysis of step 6031 practical problems;
Step 6032 proposes Research Hypothesis;
Step 6033 makes up model: select to determine variable, analyze cause-effect relationship, build path figure;
Step 6034 questionnaire, sampling and data acquisition;
Step 6035 model fitting solving model parameter;
Step 6036 model evaluation;
The correction of step 6037 model;
Step 6038 finishes.
8. as right 1 described Meteorological Services performance evaluation method, be characterised in that the construction method of described gray system method Meteorological Services performance evaluation model is as follows based on Geographic Information System:
The analysis of step 6041 evaluation index;
Step 6042 evaluation index Weight Determination;
The analyzing and processing of step 6043 evaluation index;
Step 6044 is set up triangle albefaction weight function and degree of membership;
Step 6045 evaluation grade is determined.
9. as right 1 described Meteorological Services performance evaluation method, be characterised in that the construction method of described data envelopment method Meteorological Services performance evaluation model is as follows based on Geographic Information System:
Step 6051 determines to estimate purpose;
Step 6052 trade-off decision unit;
Step 6053 is set up the input and output index system;
Step 6054 is selected data envelopment method model;
Step 6055 is carried out the evaluation analysis of data envelopment method;
Step 6056 is adjusted the input and output index system;
Step 6057 provides the comprehensive evaluation analysis conclusion.
CN201010182343A 2010-05-25 2010-05-25 Meteorological service performance evaluation method based on geographical information system (GIS) Pending CN101853290A (en)

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