CN111104640A - Rainfall observation and evaluation method and system based on analytic hierarchy process - Google Patents
Rainfall observation and evaluation method and system based on analytic hierarchy process Download PDFInfo
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Abstract
The invention discloses a rainfall observation evaluation method and system based on an analytic hierarchy process, which comprises the steps of firstly preprocessing rainfall observation time-space data and establishing a rainfall time-space data matrix; secondly, establishing a rainfall observation technical index evaluation system; then, determining the weight of each index in an index system based on an analytic hierarchy process; and finally, establishing a rainfall observation technology comprehensive evaluation model. The invention provides a comprehensive evaluation index system covering information such as rainfall data time resolution, spatial resolution, precision, cost and the like, and provides a scientific evaluation standard for multi-source heterogeneous rainfall information evaluation; by matrixing rainfall space-time data and combining a mathematical model, a multi-level evaluation model is established, and the problem that space-time resolution cannot be considered in the prior art is solved.
Description
Technical Field
The invention belongs to the technical field of meteorological monitoring and evaluation, and particularly relates to a rainfall observation and evaluation method and system based on an analytic hierarchy process.
Background
Rainfall observation data is one of basic data for researching global and regional scale climate change, atmospheric circulation, water circulation and other processes; accurate and real-time rainfall data can drive a climate model, a hydrological model and the like to perform short, medium and long-term meteorological forecast, hydrological forecast and the like, so that the capacity of coping with disaster events is improved. At present, rainfall observation technologies have various forms, mainly comprise a ground observation station, a weather radar, satellite remote sensing and the like, and can provide point position data or grid data.
With the rapid development of a new generation of communication technology, inverting the surface rainfall intensity by using attenuation information propagated by microwave communication signals also becomes an effective monitoring method, and an effective data source is provided for high-precision fine rainfall monitoring. However, unlike the traditional approach, the microwave network provides line-scale rainfall data at different locations, and this completely new data format brings new challenges for the evaluation of rainfall observations. On one hand, if the line scale data is directly converted into point location data or grid data, the path information contained in the line scale data cannot be effectively reflected; on the other hand, different rainfall observation methods have different characteristics, and the application conditions, the spatial-temporal resolution, the cost, the precision and the like of the methods are different, so that the single index cannot be used for transverse comparison, and therefore a system evaluation system is urgently needed to comprehensively evaluate the rainfall observation technology from a multi-index layer.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provides a rainfall observation and evaluation method and system based on an analytic hierarchy process, establishes a multi-level evaluation model and solves the problem that the space-time resolution cannot be considered in the prior art.
In order to solve the technical problem, the invention provides a rainfall observation and evaluation method based on an analytic hierarchy process, which is characterized by comprising the following steps of:
acquiring rainfall observation data of the same space-time scale;
determining an evaluation index, and calculating the value of each index;
determining the weight of each index based on an analytic hierarchy process;
and calculating to obtain a rainfall observation and evaluation result according to the value and the weight of each index.
Further, the acquiring rainfall observation data of the same spatiotemporal scale includes:
acquiring rainfall observation data;
carrying out spatial scale pretreatment and time scale pretreatment on rainfall observation data;
and (3) matrixing and expressing the rainfall observation time-space data.
Further, the indexes comprise a reliability index and an economic index; the reliability index comprises matrix similarity, deviation, relative deviation and root-mean-square deviation; the economic index is the sum of the investment fixed cost and the use and maintenance cost.
Further, the determining the weight of each index based on the analytic hierarchy process includes:
criterion layer weight matrix:
in the formula of U1Representing a criterion layer reliability index, U2Representing a criterion layer economic index; c. C1,c2Respectively represents U1,U2A weight relative to the target;
index layer weight matrix:
reliability index layer reciprocal judgment matrix:
in the formula ai,jI.e. the importance of the ith factor relative to the jth factor, aj,iI.e. the importance of the jth factor relative to the ith factor, aji＝1/aij；
Maximum characteristic root lambda of reciprocal judgment matrix of calculation reliability index layermaxAnd its corresponding featuresThe feature vector is normalized to obtain a reliability index layer weight matrix:
then, carrying out consistency check on the weight matrix; taking the normalized weight matrix as a weight vector through consistency check, otherwise reconstructing a reciprocal judgment matrix;
the economic index is the sum of the investment fixed cost and the use and maintenance cost, is equally important, does not need weight, and is uncertain.
Further, the calculating the rainfall observation and evaluation result according to the value and the weight of each index includes:
weight W1Multiplying the rainfall product index calculation result R to obtain a single rainfall observation product comprehensive scoring result y:
wherein, the reliability index layer index
Economic index layer index R2＝cost；a1、a2、a3、a4Is the column vector of matrix a.
Correspondingly, the invention also provides a rainfall observation and evaluation system based on the analytic hierarchy process, which is characterized by comprising a data acquisition module; an index calculation module; the index weight calculation module and the evaluation result calculation module;
the data acquisition module is used for acquiring rainfall observation data of the same space-time scale;
the index calculation module is used for determining the evaluated indexes and calculating the values of all the indexes;
the index weight calculation module is used for determining each index weight based on an analytic hierarchy process;
and the evaluation result calculation module is used for calculating to obtain a rainfall observation evaluation result according to the value and the weight of each index.
Further, in the data obtaining module, the obtaining of the rainfall observation data of the same spatio-temporal scale includes:
acquiring rainfall observation data;
carrying out spatial scale pretreatment and time scale pretreatment on rainfall observation data;
and (3) matrixing and expressing the rainfall observation time-space data.
Further, in the index calculation module, the indexes include a reliability index and an economic index; the reliability index comprises matrix similarity, deviation, relative deviation and root-mean-square deviation; the economic index is the sum of the investment fixed cost and the use and maintenance cost.
Further, in the index weight calculation module, the determining the weight of each index based on the analytic hierarchy process includes:
criterion layer weight matrix:
in the formula of U1Representing a criterion layer reliability index, U2Representing a criterion layer economic index; c. C1,c2Respectively represents U1,U2A weight relative to the target;
index layer weight matrix:
reliability index layer reciprocal judgment matrix:
in the formula ai,jI.e. the importance of the ith factor relative to the jth factor, aj,iI.e. the importance of the jth factor relative to the ith factor, aji＝1/aij；
Maximum characteristic root lambda of reciprocal judgment matrix of calculation reliability index layermaxAnd the corresponding characteristic vector, wherein the characteristic vector is normalized to obtain the reliability indexLayer-labeled weight matrix:
then, carrying out consistency check on the weight matrix; taking the normalized weight matrix as a weight vector through consistency check, otherwise reconstructing a reciprocal judgment matrix;
the economic index is the sum of the investment fixed cost and the use and maintenance cost, is equally important, does not need weight, and is uncertain.
Further, in the evaluation result calculation module, the calculating the rainfall observation evaluation result according to the value and the weight of each index includes:
weight W1Multiplying the rainfall product index calculation result R to obtain a single rainfall observation product comprehensive scoring result y:
wherein, the reliability index layer index
Economic index layer index R2＝cost；a1、a2、a3、a4Is the column vector of matrix a.
Compared with the prior art, the invention has the following beneficial effects:
(1) the invention provides a comprehensive evaluation index system covering information such as rainfall data time resolution, spatial resolution, precision, cost and the like, and provides a scientific evaluation standard for multi-source heterogeneous rainfall information evaluation;
(2) by matrixing rainfall space-time data and combining a mathematical model, a multi-level evaluation model is established, and the problem that space-time resolution cannot be considered in the prior art is solved;
(3) the user can adjust the index layer weight matrix according to self demand to adjust the influence degree of each element on the overall evaluation index, and can better adapt to the use demands of different users.
Drawings
FIG. 1 is an algorithm diagram of a bilinear interpolation algorithm;
FIG. 2 is a schematic diagram of a matrixing expression process of rainfall spatiotemporal data;
FIG. 3 is a flow chart of the comprehensive evaluation method of the present invention;
fig. 4 is a structural diagram of an index evaluation system established in the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The rainfall observation and evaluation method based on the analytic hierarchy process disclosed by the invention is shown in a figure 3 and specifically comprises the following processes:
and step S1, performing spatial scale preprocessing and time scale preprocessing on the acquired rainfall observation data to acquire data of the same space-time scale.
The purpose of this is to ensure the temporal and spatial consistency of the rainfall observation evaluation: the spatial scale evaluation of different rainfall observation technologies comprises point scale, regional scale and global scale; the time scale evaluation covered also includes minutes, hours, days, months, years, etc. If the scales are not uniform, the evaluation cannot be performed.
(1) Spatial scale pre-processing
The rainfall observation data space preprocessing method comprises an inverse distance weight interpolation method, a bilinear interpolation method and the like.
The principle of inverse distance weight interpolation is as follows:
inverse Distance Weight (IDW) interpolation uses a set of linear weights of a set of sample points to determine the pel value. The weight is an inverse distance function. This method assumes that the mapped variable is reduced by the distance from its sampling location. The inverse distance weighting method relies primarily on the power value of the inverse distance, and the power parameter can control the effect of a known point on interpolation based on the distance of the interpolated point. The power parameter is a positive real number, and is 2 by default. The higher the power parameter, the more recent effects on the interpolated points can be emphasized, so the neighboring data will be more affected, the fitted surface is more detailed (less smooth), and the interpolated values are closer to the values of the neighboring sampled points. Conversely, the smaller the power parameter is, the greater the influence of the sampling points at longer distances on the interpolation points is, and the smoother the fitting surface is.
The algorithm is described as follows:
there are n discrete points Z (x)1,y1)，Z(x2,y2)，…，Z(xn,yn) The interpolation point (x, y) needs to be interpolated and predicted to obtain Z (x, y), and the steps are as follows:
a. and (3) calculating the distance h between each discrete point and the interpolation point by using a distance function, wherein an Euclidean distance formula is adopted as a distance function formula:
wherein (x, y) is the coordinate of the interpolation point, (x)i,yi) Known ith discrete point coordinates;
b. in the case of power parameter determination, the weight w of each discrete point is calculated using a weight functioni：
Wherein p is a power parameter, n is the number of discrete points, and the weighting function is not unique.
c. Calculating the value Z (x, y) of the interpolation point
The principle of bilinear interpolation is as follows:
four known points near the known interpolation point are Q11(x1,y1),Q21(x2,y1),Q12(x1,y2),Q22(x2,y2) The interpolation point is P (x, y), and the P point is interpolated using a nearby known point. The bilinear interpolation algorithm is schematically shown in the attached figure 1, and the relationship between an interpolation point and four known points can be published in the figure.
a. Firstly, linear interpolation is carried out twice in the X direction to obtain R1(X, y)1),R2(x,y1) Function value f (x, y) of point1) And f (x, y)2) The calculation formula is as follows:
where f () is an unknown function.
Then, linear interpolation is carried out in the Y direction once to obtain a function value f (x, Y) of the P point, and the calculation formula is as follows:
or interpolation is carried out in the Y direction and then in the X direction, and the principles are consistent.
(2) Time scale pre-processing
The method for preprocessing the rainfall observation data time scale comprises a linear interpolation method, an accumulation calculation method and the like. The linear interpolation method is mainly used for interpolating a high time resolution with a low time resolution, and the cumulative calculation method is used for interpolating a high time resolution with a high time resolution.
And performing time-space resolution consistency conversion on different evaluated rainfall observation data according to the method to obtain rainfall time-space data at the same time interval within a certain spatial scale.
(3) Rainfall observation space-time data matrixing expression
The method specifically comprises the following steps:
1) the rainfall observation time sequence result in a certain space scale can describe a three-dimensional matrix Z ═ Zijt]Wherein i is 1 …. r; j ═ 1 … c; t is 1 … n, and r and c each represent the nullThe number of rows and columns of grid points in the inter-scale, t represents the time series data length of any grid point, and the values of r, c and n can be any values. When r is 1, the precision of rainfall observation data of a certain point scale is evaluated; when n is 1, the evaluation represents the precision of rainfall observation data of a certain time node.
2) Reducing dimension of grid data (two-dimensional data) of space scale, converting two-dimensional matrix data into one-dimensional column vector represented by s, and if dimension of the two-dimensional matrix is [ r, c ]]Then, the dimension is converted into a one-dimensional column vector dimension [1, r × c]Combining the one-dimensional column vector and the time dimension to form a new two-dimensional matrix Z ═ Zst]The spatial and temporal dimensions are denoted by s, t, respectively, where s is 1 …. m; t is 1 … n; that is, the dimension of the final rainfall space-time data matrix is [ m, n ]]Where m is r c, n is the time series data length of any grid point.
FIG. 2 is a schematic diagram of a matrixing expression process of rainfall spatiotemporal data; in the figure, the left figure is preprocessed space-time three-dimensional original data. And the intermediate graph is space dimensionality reduction to obtain space-time two-dimensional data. The right diagram is a matrixing expression of space-time two-dimensional data.
Through the rainfall spatio-temporal data preprocessing and matrixing expression, rainfall spatio-temporal data matrixes of different rainfall observation technical data under any spatial scale and any time scale can be obtained.
And S2, calculating indexes of reliability and economy by using the rainfall data of the same space-time scale obtained in the step S1, and establishing a rainfall observation technology index evaluation system.
Specific indexes and structures are shown in fig. 4. The two indexes of reliability and economy are selected by combining the rainfall observation technology and the application characteristics of products, and the cost, effect and income of the monitoring technology are considered.
Each index definition and calculation method description:
(1) reliability index
The reliability index mainly refers to the precision evaluation of rainfall observation. The precision indexes comprise a series of indexes for evaluating rainfall observation precision and deviation, for example, the deviation is used for evaluating system deviation values among measurement results of different rainfall observation technologies; the relative deviation is used for quantifying the relative deviation degree among the measurement results of different rainfall observation technologies; the root mean square difference is used for quantifying the average error degree among different rainfall observation technology measurement results, and the matrix similarity can be used for evaluating the consistency degree among different rainfall observation results.
Taking the station observation data as a reference matrix B, the different index calculation method of the measurement result A to be evaluated is as follows:
matrix similarity r:
wherein, aij,bijThe matrices a and B represent rainfall data acquired by two different techniques.
Deviation Bias and relative deviation rBias:
root mean square difference RMSE:
among the above indexes, the higher the matrix similarity is, the higher the reliability is, and the smaller the other three index values are, the higher the reliability is. In order to keep the consistency of the calculation, the opposite numbers of the last three indexes are taken as final calculated values.
(2) Index of economic efficiency
The economic index mainly considers two aspects of investment fixed cost and use and maintenance cost, the investment benefit is not considered temporarily, and the total investment index cost of unit space-time data is as follows:
cost＝mn(cf+co) (13)
here, cfInvestment fixed cost per unit time unit space scale, coThe unit time unit space scale use and maintenance cost is shown, m is the total grid number of the regional space resolution, and n is the time sequence length. The higher the cost, the more uneconomical, and the opposite of the calculation results is taken as the final calculation value in order to maintain the consistency of the calculation.
And step S3, determining the weight of each index in the index system based on the analytic hierarchy process, and the purpose of doing so is to consider the influence degree of each index on the evaluation result more scientifically.
Collecting a plurality of expert judgment results, specifically:
criterion layer weight matrix:
in the formula of U1Representing a criterion layer reliability index, U2Representing a criterion layer economic indicator. c. C1,c2Respectively represents U1,U2Weight relative to the target. Since the criterion layer has only two indices, the corresponding weights are given directly by the expert, and c1+c2＝1。
Index layer weight matrix:
when the index factors are more than two, the weight is difficult to directly distribute among all the factors, so that different factors are compared pairwise to obtain a reciprocal judgment matrix, as follows:
reliability index layer reciprocal judgment matrix:
in the formula ai,jIs the ith causeThe importance of the element relative to the jth factor. The scale standards are respectively 1-9, wherein the level 1 represents that the ith factor has the same influence as the jth factor; level 3 represents that the ith factor has a slightly stronger effect than the jth factor; level 5 indicates that the ith factor has a stronger influence than the jth factor; 7 represents that the ith factor has stronger influence than the jth factor; 9 represents that the ith factor has an absolute stronger influence than the jth factor; 2,4,6,8 are between the two adjacent levels. In addition aj,iI.e. the importance of the jth factor relative to the ith factor, aji＝1/aij。V1,V2,V3,V4Reliability index layer matrix similarity, deviation, relative deviation, and root mean square deviation, respectively.
Maximum characteristic root lambda of reciprocal judgment matrix of calculation reliability index layermaxAnd the characteristic vector corresponding to the reliability index layer weight matrix is obtained after normalization of the characteristic vector:
then, the consistency check is carried out on the weight matrix: consistency ratio
Wherein CI ═ λmaxN)/(n-1), RI, as determined from Table 1. Where n is the number of vector elements.
TABLE 1 RI Tan
| n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| RI | 0.00 | 0.00 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 |
| n | 11 | 12 | 13 | 14 | 15 | |||||
| RI | 1.51 | 1.48 | 1.56 | 1.57 | 1.59 |
Generally, when the consistency ratio CR is less than 0.1, the above normalized weight matrix is used as a weight vector through consistency test, otherwise, a reciprocal judgment matrix is reconstructed.
The economic index is the sum of the investment fixed cost and the use and maintenance cost, is equally important, does not need weight, and is uncertain.
And step S4, calculating the comprehensive evaluation result of the rainfall observation technology.
Weight W1Multiplying the rainfall product index calculation result R to obtain a single rainfall observation product comprehensive scoring result y:
wherein, the reliability index layer index
Economic efficiency meansLayer index R2＝cost；a1、a2、a3、a4Is the column vector of matrix a.
When a plurality of rainfall observation products are evaluated and compared, the evaluation result of each product is obtained through the calculation method, the smaller the calculation result is, the higher the comprehensive score is, and the better the rainfall observation technology is.
Correspondingly, the invention also provides a rainfall observation and evaluation system based on the analytic hierarchy process, which is characterized by comprising a data acquisition module; an index calculation module; the index weight calculation module and the evaluation result calculation module;
the data acquisition module is used for acquiring rainfall observation data of the same space-time scale;
the index calculation module is used for determining the evaluated indexes and calculating the values of all the indexes;
the index weight calculation module is used for determining each index weight based on an analytic hierarchy process;
and the evaluation result calculation module is used for calculating to obtain a rainfall observation evaluation result according to the value and the weight of each index.
Further, in the data obtaining module, the obtaining of the rainfall observation data of the same spatio-temporal scale includes:
acquiring rainfall observation data;
carrying out spatial scale pretreatment and time scale pretreatment on rainfall observation data;
and (3) matrixing and expressing the rainfall observation time-space data.
Further, in the index calculation module, the indexes include a reliability index and an economic index; the reliability index comprises matrix similarity, deviation, relative deviation and root-mean-square deviation; the economic index is the sum of the investment fixed cost and the use and maintenance cost.
Further, in the index weight calculation module, the determining the weight of each index based on the analytic hierarchy process includes:
criterion layer weight matrix:
in the formula of U1Representing a criterion layer reliability index, U2Representing a criterion layer economic index; c. C1,c2Respectively represents U1,U2A weight relative to the target;
index layer weight matrix:
reliability index layer reciprocal judgment matrix:
in the formula ai,jI.e. the importance of the ith factor relative to the jth factor, aj,iI.e. the importance of the jth factor relative to the ith factor, aji＝1/aij；
Maximum characteristic root lambda of reciprocal judgment matrix of calculation reliability index layermaxAnd the characteristic vector corresponding to the reliability index layer weight matrix is obtained after normalization of the characteristic vector:
then, carrying out consistency check on the weight matrix; taking the normalized weight matrix as a weight vector through consistency check, otherwise reconstructing a reciprocal judgment matrix;
the economic index is the sum of the investment fixed cost and the use and maintenance cost, is equally important, does not need weight, and is uncertain.
Further, in the evaluation result calculation module, the calculating the rainfall observation evaluation result according to the value and the weight of each index includes:
weight W1Multiplying the rainfall product index calculation result R to obtain a single rainfall observation product comprehensive scoring result y:
wherein, the reliability index layer index
Economic index layer index R2＝cost；a1、a2、a3、a4Is the column vector of matrix a.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A rainfall observation and evaluation method based on an analytic hierarchy process is characterized by comprising the following steps:
acquiring rainfall observation data of the same space-time scale;
determining an evaluation index, and calculating the value of each index;
determining the weight of each index based on an analytic hierarchy process;
and calculating to obtain a rainfall observation and evaluation result according to the value and the weight of each index.
2. The method as claimed in claim 1, wherein the acquiring rainfall observation data of the same spatiotemporal scale comprises:
acquiring rainfall observation data;
carrying out spatial scale pretreatment and time scale pretreatment on rainfall observation data;
and (3) matrixing and expressing the rainfall observation time-space data.
3. The method of claim 1, wherein the indices include reliability index and economic index; the reliability index comprises matrix similarity, deviation, relative deviation and root-mean-square deviation; the economic index is the sum of the investment fixed cost and the use and maintenance cost.
4. The method of claim 3, wherein the determining the weights of the indexes based on the analytic hierarchy process comprises:
criterion layer weight matrix:
in the formula of U1Representing a criterion layer reliability index, U2Representing a criterion layer economic index; c. C1,c2Respectively represents U1,U2A weight relative to the target;
index layer weight matrix:
reliability index layer reciprocal judgment matrix:
in the formula ai,jI.e. the importance of the ith factor relative to the jth factor, aj,iI.e. the importance of the jth factor relative to the ith factor, aji＝1/aij；
Maximum characteristic root lambda of reciprocal judgment matrix of calculation reliability index layermaxAnd the characteristic vector corresponding to the reliability index layer weight matrix is obtained after normalization of the characteristic vector:
then, carrying out consistency check on the weight matrix; taking the normalized weight matrix as a weight vector through consistency check, otherwise reconstructing a reciprocal judgment matrix;
the economic index is the sum of the investment fixed cost and the use and maintenance cost, is equally important, does not need weight, and is uncertain.
5. The method of claim 4, wherein the calculating the rainfall observation and evaluation result according to the value and the weight of each index comprises:
weight W1Multiplying the rainfall product index calculation result R to obtain a single rainfall observation product comprehensive scoring result y:
wherein, the reliability index layer index
Economic index layer index R2＝cost；a1、a2、a3、a4Is the column vector of matrix a.
6. A rainfall observation and evaluation system based on an analytic hierarchy process is characterized by comprising a data acquisition module; an index calculation module; the index weight calculation module and the evaluation result calculation module;
the data acquisition module is used for acquiring rainfall observation data of the same space-time scale;
the index calculation module is used for determining the evaluated indexes and calculating the values of all the indexes;
the index weight calculation module is used for determining each index weight based on an analytic hierarchy process;
and the evaluation result calculation module is used for calculating to obtain a rainfall observation evaluation result according to the value and the weight of each index.
7. The system of claim 6, wherein the data obtaining module is configured to obtain the rainfall observation data of the same spatiotemporal scale by:
acquiring rainfall observation data;
carrying out spatial scale pretreatment and time scale pretreatment on rainfall observation data;
and (3) matrixing and expressing the rainfall observation time-space data.
8. The system of claim 6, wherein the indices of the index calculation module comprise reliability indices and economic indices; the reliability index comprises matrix similarity, deviation, relative deviation and root-mean-square deviation; the economic index is the sum of the investment fixed cost and the use and maintenance cost.
9. The system of claim 8, wherein the determining the weights of the indexes based on the analytic hierarchy process in the index weight calculation module comprises:
criterion layer weight matrix:
in the formula of U1Representing a criterion layer reliability index, U2Representing a criterion layer economic index; c. C1,c2Respectively represents U1,U2A weight relative to the target;
index layer weight matrix:
reliability index layer reciprocal judgment matrix:
in the formula ai,jI.e. the importance of the ith factor relative to the jth factor, aj,iI.e. the importance of the jth factor relative to the ith factor, aji＝1/aij；
Maximum characteristic root lambda of reciprocal judgment matrix of calculation reliability index layermaxAnd the characteristic vector corresponding to the reliability index layer weight matrix is obtained after normalization of the characteristic vector:
then, carrying out consistency check on the weight matrix; taking the normalized weight matrix as a weight vector through consistency check, otherwise reconstructing a reciprocal judgment matrix;
the economic index is the sum of the investment fixed cost and the use and maintenance cost, is equally important, does not need weight, and is uncertain.
10. The system of claim 9, wherein the calculating of the rainfall observation and evaluation result according to the value and the weight of each index in the evaluation result calculating module comprises:
weight W1Multiplying the rainfall product index calculation result R to obtain a single rainfall observation product comprehensive scoring result y:
wherein, the reliability index layer index
Economic index layer index R2＝cost；a1、a2、a3、a4Is the column vector of matrix a.
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