CN115994639B - Water grid pattern and topography association evaluation method based on redundancy analysis - Google Patents
Water grid pattern and topography association evaluation method based on redundancy analysis Download PDFInfo
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Abstract
The invention discloses a water grid pattern and topography association evaluation method based on redundancy analysis, which is characterized by collecting topography data and river network data of an area to be analyzed, dividing evaluation indexes into two types of water grid pattern indexes and topography index, and carrying out evaluation index selection and pretreatment; normalizing the evaluation index to obtain a normalized water grid pattern index and a normalized topography index; performing redundancy analysis on the water grid pattern and the topography, comprehensively considering the water grid structure index and the topography index characteristics of the region to be evaluated, and establishing an evaluation model, wherein the evaluation model is based on multiple regression relation and variance interpretation of the water grid structure index and the topography index, so as to generate the matching degree between the water grid pattern index and the topography index; and evaluating the association of the water grid pattern and the topography and landform according to the evaluation model. The invention improves the management and planning level of the regional water network.
Description
Technical Field
The invention relates to the field of river network evaluation and management, in particular to a water network pattern and topography association evaluation method.
Background
The spatial pattern of the river network is the basic characteristics and horizontal effect channels of the river basin, influences the hydrologic process of the whole river basin, and adjusts the biological population diversity and the spatial distribution of physical attributes of the river channel and the river bank. The river network is not a random topological structure, the spatial pattern is determined by the physical properties of the river basin, and the physical factors forming the differential river network mode are quantified by researching the structure, geometry and topological characteristics of the river network so as to better understand the dynamic state of the river network in a changing environment. River networks are important components of regional landscape patterns, and the topography evolution and the river network change are basically mutually coupled. The topography of the area determines the path and the residence time of water flow, which affects the physical space pattern of the water network to a great extent, the internal relevance of the regional water pattern and the topography is a key factor affecting the safety and stability of the river channel, and along with the promotion of the high-quality development of the river basin, the matching degree and the adaptability research and the planning of the water pattern are more demanding. The existing research on the correlation between the water grid pattern and the topography is mainly qualitative analysis, and no accurate method is available for comprehensively quantifying the correlation between the water grid pattern and the topography. Moreover, the research on the river network structure from the aspect of topography often focuses on several indexes of specific aspects, and has limitations. Therefore, the quantitative association of the water grid pattern and the topography is difficult to clearly describe, and an innovative method is needed to construct an association evaluation model of the water grid pattern and the topography. The problems can be well solved by adopting redundancy analysis, and the establishment of a quantitative evaluation system for the relevance of the water grid pattern and the topography is of great significance.
Disclosure of Invention
The invention aims to provide a dynamic evaluation method for river network connectivity based on probability estimation.
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
a water grid pattern and topography association evaluation method based on redundancy analysis comprises the following steps:
collecting the topography data and river network data of the area to be analyzed, dividing the evaluation indexes into two types of water grid indexes and topography indexes, and selecting and preprocessing the evaluation indexes;
normalizing the evaluation index to obtain a normalized water grid pattern index and a normalized topography index;
performing redundancy analysis on the water grid pattern and the topography, comprehensively considering the water grid structure index and the topography index characteristics of the region to be evaluated, and establishing an evaluation model based on multiple regression relation and variance interpretation of the water grid structure index and the topography index to generate the matching degree between the water grid pattern index and the topography index;
and evaluating the association of the water grid pattern and the topography and landform according to the evaluation model.
Compared with the prior art, the invention has the following beneficial effects:
1) The matching degree between the current water grid pattern and the topography of the production area;
2) The basis is provided for the function and adaptability analysis of the water network by finding the water network inconsistent with the topographical features;
3) The water network recognition and management capability of the river basin management department is improved, and the management and planning level of the regional water network is improved.
Drawings
FIG. 1 is a flow chart of a water grid pattern and topography association evaluation method based on redundancy analysis;
fig. 2 is a schematic diagram of a data architecture of a water grid pattern and topography association evaluation method based on redundancy analysis.
Detailed Description
The technical scheme of the invention will be described in detail below with reference to the accompanying drawings.
As shown in FIG. 1, the method is a flow chart of a water grid pattern and topography association evaluation method based on redundancy analysis. Fig. 2 is a schematic diagram of a data architecture of a water grid pattern and topography association evaluation method based on redundancy analysis.
The specific steps of the flow of the invention are as follows:
step 1, collecting topography data and river network data of an area to be analyzed, and checking the accuracy and rationality of the data; the landform data at least comprise regional digital elevation data, land utilization type distribution data and gradient data; the river network data are river line vector data;
step 2, determining an evaluation partition and an evaluation scale; dividing the area to be analyzed into four types of an integral area, a hilly area, a transition area and a plain area, wherein the evaluation scale is set to be 4km; dividing the area to be analyzed into square grids with equal areas according to the evaluation scale, and taking the square grids as space sample points;
step 3, selecting and preprocessing an evaluation index, wherein the specific steps are as follows:
step 3-1, selecting an evaluation index, wherein the evaluation index is divided into two types of water grid indexes and topography indexes, and comprises selecting river network density (D r ) Fractal dimension (D) n ) Number of intersections (N) j ) As evaluation indexes for representing the water patterns, an Elevation (Elevation), a Slope (Slope), a Relief (Relief), a topography humidity index (TWI), an Elevation Stress Index (ESI), a Slope Stress Index (SSI), a Relief Stress Index (RSI), a topography humidity stress index (TSI), and a cultivated land area (farm) are selected as evaluation indexes for representing the topography;
water grid pattern index s i And the topography index d i The expression is as follows:
s i ＝[s i，1 ，s i，2 ，…，s i，n ]
d i ＝[d i，1 ，d i，2 ，…，d i，n ]
wherein: n is the total number of grids divided into areas, s i，n A water grid pattern index value d for grid n i The topography index value of the grid n is obtained, and i is the sample number; the water grid pattern index s i As a response variable of the flow, the topography index d i As an interpretation vector for the present flow;
step 3-2, calculating the topography index d in order to avoid deviation of the result i Variance expansion factor VIF of (v) i The expression is as follows:
wherein: r is R i Is a topography index D i Complex correlation coefficients for the remaining indicators;
taking a certain area as an example, performing colinear analysis on the landform indexes, removing two indexes of gradient and fluctuation degree with colinear (namely, a variance expansion factor is larger than 10), and removing indexes with VIF larger than 10 according to the result of the table 1;
as shown in table 1, an example of the result of the variance expansion factor of the topography index is shown.
TABLE 1
Step 4, normalizing the evaluation index to obtain a normalized water grid pattern index and a normalized topography index:
the expression of the normalized value x is as follows:
wherein: x is an index value to be normalized, X max To be normalized to the maximum value of the index X min The minimum value of the index to be normalized;
and 5, performing redundant analysis on the water grid pattern and the topography, wherein the method comprises the following specific steps of:
step 5-1, combining the normalized water grid pattern indexes into a response variable matrix S, and taking a specific embodiment as an example, the expression is as follows:
and 5-2, combining the topography index after normalization processing into an explanatory variable matrix D, wherein the expression is as follows by taking a specific embodiment as an example:
step 5-3, performing multiple regression calculation on the interpretation variable matrix D by using the response variable matrix S to obtain a fitting value matrix of the response variableThe expression is as follows:
wherein: b is a fitting coefficient matrix, and D' is a transpose matrix of the interpretation variable matrix D;
the variance interpretation rate of the interpretation variable versus the response variable is calculated as follows:
wherein:explaining the index for the variance of D versus S, +.>Is->STD (S) is the square sum of the variances of S, n is the number of rows of D, and m is the number of columns of D;
according to significance of replacement test, importance ranking is carried out on the explanatory variables, each explanatory variable is sequentially added in sequence to carry out redundancy analysis, and the contribution rate of the explanatory variables is determined through variance explanation difference values, wherein the expression is as follows:
D i ＝[d 1 ,…,d i ]
D i-1 ＝[d 1 ,…,d i-1 ]
wherein:to explain the variable d i Contribution ratio, D i Is comprised of d i Interpretation variable matrix of D i-1 To not contain d i Interpretation variable matrix of>For D i Explaining the index of variance of S,>for D i-1 Interpreting the index for variance of S;
5-4, comprehensively considering the water network structural index and the topography index characteristics of the region to be evaluated, and establishing an evaluation model, wherein the evaluation model generates the matching degree between the water network structural index and the topography index through multiple regression relation and variance interpretation of the water network structural index and the topography index;
as shown in table 2, an example of the result of interpretation of the correlation variance between the water grid index and the topography index is shown.
TABLE 2
As shown in table 3, the result is an example of the contribution rate (percentage) of the topography index to the relevance.
TABLE 3 Table 3
And 6, evaluating the association of the water pattern and the topography according to an evaluation model, analyzing to obtain the situation that the association and the matching degree of the water pattern and the topography of the example area are weaker, and the TWI has higher contribution rate in variance interpretation and has a certain influence on the water pattern.
The above technical solutions and the detailed description are only for helping to understand the core idea of the present invention, but are not limited to the above embodiments, and anyone should know: all structural changes made under the teaching of the invention, which have the same or similar technical scheme as the invention, should be considered to fall within the protection scope of the invention.
Claims (1)
1. A water grid pattern and topography association evaluation method based on redundancy analysis is characterized by comprising the following steps:
collecting the topography data and river network data of the area to be analyzed, dividing the evaluation indexes into two types of water grid indexes and topography indexes, and selecting and preprocessing the evaluation indexes; the evaluation index selection and pretreatment specifically comprises the following steps:
water grid pattern index s i And the topography index d i The expression is as follows:
s i ＝[s i，1 ，s i，2 ，…，s i，n ]
d i ＝[d i，1 ，d i，2 ，…，d i，n ]
wherein: n is the total number of grids divided into areas, s i，n A water grid pattern index value d for grid n i The topography index value of the grid n is obtained, and i is the sample number; the water grid pattern index s i As a response variable of the flow, the topography index d i As an interpretation vector for the present flow;
calculating a topography index d i Variance expansion factor VIF of (v) i The expression is as follows:
wherein: r is R i Is a topography index D i For the other indexesComplex correlation coefficients;
normalizing the evaluation index to obtain a normalized water grid pattern index and a normalized topography index;
performing redundancy analysis on the water grid pattern and the topography, comprehensively considering the water grid structure index and the topography index characteristics of the region to be evaluated, and establishing an evaluation model, wherein the evaluation model is based on multiple regression relation and variance interpretation of the water grid structure index and the topography index, so as to generate the matching degree between the water grid pattern index and the topography index;
evaluating the association of the water grid pattern and the topography and landform according to the evaluation model, wherein the evaluation comprises the steps of performing redundancy analysis of the water grid pattern and the topography and landform:
combining the normalized water grid pattern indexes into a response variable matrix s, wherein the expression is as follows:
combining the topography index after normalization processing into an explanatory variable matrix D, wherein the expression is as follows:
performing multiple regression calculation on the interpretation variable matrix D by the response variable matrix S to obtain a fitting value matrix of the response variableThe expression is:
wherein: b is a fitting coefficient matrix; d' is a transpose of the interpretation variable matrix D;
calculating the variance interpretation rate of the interpretation variable to the response variable, wherein the expression is as follows:
wherein:explaining the index for the variance of D versus S, +.>Is->STD (S) is the square sum of the variances of S, n is the number of rows of D, and m is the number of columns of D;
according to significance of replacement test, importance ranking is carried out on the explanatory variables, each explanatory variable is sequentially added in sequence to carry out redundancy analysis, the contribution rate of the explanatory variables is determined through variance explanation difference values, and a calculation formula is as follows:
D i ＝[d 1 ,…,d i ]
D i-1 ＝[d 1 ,…,d i-1 ]
wherein:to explain the variable d i Contribution ratio, D i Is comprised of d i Interpretation variable matrix of D i-1 To not contain d i Interpretation variable matrix of>For D i Explaining the index of variance of S,>for D i-1 The index is interpreted as the variance of S.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107862435A (en) * | 2017-10-13 | 2018-03-30 | 北京林业大学 | A kind of landscape pattern and the quantitative correlation method and system of ecosystem service function |
| CN113256101A (en) * | 2021-05-20 | 2021-08-13 | 东南大学 | Key driving force analysis method for water storage capacity change of urban and rural lakes |
| CN113379198A (en) * | 2021-05-20 | 2021-09-10 | 上海勘测设计研究院有限公司 | Method, system, medium and device for evaluating physical habitat of river at watershed scale |
| CN113553551A (en) * | 2021-07-28 | 2021-10-26 | 生态环境部华南环境科学研究所 | Ozone concentration prediction model of coupling view pattern |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| SG188534A1 (en) * | 2010-09-14 | 2013-04-30 | Amitsur Preis | System and method for water distribution modelling |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107862435A (en) * | 2017-10-13 | 2018-03-30 | 北京林业大学 | A kind of landscape pattern and the quantitative correlation method and system of ecosystem service function |
| CN113256101A (en) * | 2021-05-20 | 2021-08-13 | 东南大学 | Key driving force analysis method for water storage capacity change of urban and rural lakes |
| CN113379198A (en) * | 2021-05-20 | 2021-09-10 | 上海勘测设计研究院有限公司 | Method, system, medium and device for evaluating physical habitat of river at watershed scale |
| CN113553551A (en) * | 2021-07-28 | 2021-10-26 | 生态环境部华南环境科学研究所 | Ozone concentration prediction model of coupling view pattern |
Non-Patent Citations (4)
| Title |
|---|
| 地貌格局与流域侵蚀产沙过程关系定量分析――以黄河中游河龙区间为例;王计平;黄志霖;刘洋;许申来;卫伟;陈利顶;;地理研究(第02期);全文 * |
| 张璐 ; 朱仲元 ; 张圣微 ; 王慧敏 ; .半干旱草原型流域土壤水分变异及其影响因素分析.农业工程学报.2020,(第13期),全文. * |
| 杜国明.长时间尺度珠江口河网水下地形演变 过程三维可视化实现及分析.水 科 学 进 展.2005,第16卷(第2期),全文. * |
| 黄河中游流域地貌形态特征研究;李晨瑞;李发源;马锦;刘玮;;地理与地理信息科学(第04期);全文 * |
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