CN108009562A - A kind of hydrographic water resource feature space variability knows method for distinguishing - Google Patents
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Abstract
The present invention relates to hydrographic water resource field, knows method for distinguishing more particularly, to a kind of hydrographic water resource feature space variability.Comprise the following steps：S1. the synchronous measured water level data of river network hydrology website are gathered and grid laying is carried out to mesh self adaptability；S2. gathered data are combined and grid is laid, calculate the water level semivariable function of all directions, variability of the research water level in all directions；S3. the method for performance model superposition, the semivariable function of different change journeys is overlapped and forms a new shrink-fit structure semivariable function model, semivariable function value in region between any two points can conveniently be obtained according to the shrink-fit structure semivariable function model, and the water level for waiting to estimate a little to survey region is estimated.Computation complexity of the present invention is relatively low, computational efficiency is high, prediction accuracy is high, easy to operate, Analysis on spatial variability can be carried out to river network water level feature, and carry out space point estimation simulation in mesh self adaptability to water level.
Description
Technical field
The present invention relates to hydrographic water resource field, knows more particularly, to a kind of hydrographic water resource feature space variability
Method for distinguishing.
Background technology
Due to the overlaying influence of the Multiple Factors such as mankind's activity and climate change, there occurs huge for river network natural environment
Change, be mainly manifested in the hydrology and river morphology character morphs.Mesh self adaptability water level is a kind of area with spatial position change
Domain variable, existing randomness, and have certain structural.In flood control and disaster reduction, management of water resources development and utilization, water level is most
One of important hydrological characteristics.But the waterlevel data of existing hydrographic(al) network actual measurement is far from the requirement for meeting production division, because
It is always limited for network density, and the water level numerical value that actual needs is known may not be just on survey station, not even in station net
It is interior.
The content of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of knowledge of hydrographic water resource feature space variability
Method for distinguishing, can carry out river network water level feature Analysis on spatial variability, and carry out space point estimation in mesh self adaptability to water level
Simulation.
To solve the above problems, technical solution provided by the invention is：A kind of hydrographic water resource feature space variability is known
Method for distinguishing, wherein, comprise the following steps：
S1. the synchronous measured water level data of river network hydrology website are gathered and grid laying is carried out to mesh self adaptability；
S2. gathered data are combined and grid is laid, calculate the water level semivariable function of all directions, research water level is each
The variability in direction；
S3. the method for performance model superposition, the semivariable function of different change journeys is overlapped and forms a new fitting
Structure semivariable function model, half between any two points can be conveniently obtained in region according to the shrink-fit structure semivariable function model
Variation function value, and the water level for waiting to estimate a little to survey region is estimated.
Further, the S2 steps include：
S21. experiment semivariable function γ is calculated*(h)：Using seeking [Z
(xi)-Z(xi+h)]2The method of arithmetic mean of instantaneous value calculate γ*(h)；Wherein, h is distance vector, and N (h) is by vectorial h phases
Every experimental data pair number, Z (xi) and Z (xi+ h) it is the point x that h is mutually divided on x directionsi(xi+ h) place observation；
S22. it is calculated all to h, γ*(h) value, makes h- γ*(h) semivariation figure is tested；
S23. on the basis of semivariable function is tested, it is intended using suitable theoretical semivariable function model
Close, water level feature space Variability Analysis is carried out using the model of fitting.
Further, the S23 steps include：
S231. spherical model is selected, the general formulae of spherical model is：
Wherein, C0For block gold constant；(C0+ C) it is base station value；C is known as sagitta；A is change journey；
S232. as 0 ＜ h≤a, weighted polynomial regression method fitting spherical model semi-variogram is selected.
Further, the S3 steps include：
S31. the theoretical semivariable function in x directions and y directions is built, wherein,
X directions theory semivariable function is：
Y directions theory semivariable function is：
S32. to hyCoordinate carries out linear transformation, is allowed to be changed into h'y, then, γy(hy) the original base station value of holding is can be changed to,
But become journey and γy(hy) one of change Cheng Xiangtong among semivariable function γ 'y(h'y)；
S33. γ 'y(h'y) regard as in all directions same sex structure γx(hx) on the basis of again in h'yDirection is superimposed with separately
The shrink-fit structure of one spherical model semivariable function, you can obtain fitting results model：
γ (h')=γ0(h'0)+γ1(h'x)+γ2(h'y)；
Semivariable function value in region between any two points can be obtained according to shrink-fit structure semivariable function model, with this mould
Type is to wait to estimate system valuation a little according to being extended to survey region.
Further, the grid is laid sets according to river network size and site density and distribution situation.
Compared with prior art, beneficial effect is：A kind of hydrographic water resource feature space variability provided by the invention is known
Method for distinguishing, computation complexity is relatively low, computational efficiency is high, prediction accuracy is high, easy to operate, can to river network water level feature into
Row Analysis on spatial variability, and space point estimation simulation is carried out in mesh self adaptability to water level.
Brief description of the drawings
Fig. 1 is the method for the present invention flow chart.
Fig. 2 is Delta of the Pearl River river network water level isogram during 16 days 13 July in 1999 in the present invention, digital table in figure
Show water level, unit m.
Fig. 3 is Delta of the Pearl River river network water level isogram during 22 days 6 July in 1999 in the present invention, digital table in figure
Show water level, unit m.
Fig. 4 is nested model parameter of the embodiment of the present invention.
Fig. 5 is Delta of the Pearl River mesh self adaptability water level nested model semivariable function schematic diagram of the embodiment of the present invention.
Fig. 6 for the present invention to 16 days 13 July in 1999 when Delta of the Pearl River river network semivariable function theoretical model curves
Estimated result.
Fig. 7 for the present invention to 22 days 6 July in 1999 when Delta of the Pearl River river network semivariable function theoretical model curves estimate
Count result.
Embodiment
Attached drawing is only for illustration, it is impossible to is interpreted as the limitation to this patent；It is attached in order to more preferably illustrate the present embodiment
Scheme some components to have omission, zoom in or out, do not represent the size of actual product；To those skilled in the art,
Some known features and its explanation may be omitted and will be understood by attached drawing.Being given for example only property of position relationship described in attached drawing
Explanation, it is impossible to be interpreted as the limitation to this patent.
As shown in Figure 1, a kind of hydrographic water resource feature space variability knows method for distinguishing, wherein, comprise the following steps：
The synchronous measured water level data of step 1 collection river network hydrology websites simultaneously carry out grid laying to mesh self adaptability；Net
Lattice are laid to be set according to river network size and site density and distribution situation.
Step 2 combines gathered data and grid is laid, and calculates the water level semivariable function of all directions, studies water level
In the variability of all directions；
1. calculate experiment semivariable function γ*(h)：Using seeking [Z
(xi)-Z(xi+h)]2The method of arithmetic mean of instantaneous value calculate γ*(h)；Wherein, h is distance vector, and N (h) is by vectorial h phases
Every experimental data pair number, Z (xi) and Z (xi+ h) it is the point x that h is mutually divided on x directionsi(xi+ h) place observation；
2. it is calculated all to h, γ*(h) value, makes h- γ*(h) semivariation figure is tested；
3. on the basis of semivariable function is tested, it is fitted using suitable theoretical semivariable function model,
Water level feature space Variability Analysis is carried out using the model of fitting；Select spherical model in the present invention, the one of spherical model
As formula be：
Wherein, C0For block gold constant；(C0+ C) it is base station value；C is known as sagitta；A is change journey；
During due to h=0, γ (h)=0；During h ＞ a, γ (h)=C0+ C (for a constant), situation is all very simple, the present invention
Fitting problems during 0 ＜ h≤a are only discussed.Have at this time：
This is an incomplete cubic polynomial of h, and available weights polynomial regression is fitted spherical model semivariation
Functional arrangement.
The method of step 3 performance models superposition, the semivariable functions of different change journeys are overlapped form one it is new
Shrink-fit structure semivariable function model, can conveniently obtain in region between any two points according to the shrink-fit structure semivariable function model
Semivariable function value, and the water level for waiting to estimate a little to survey region is estimated.
1. the theoretical semivariable function in x directions and y directions is built, wherein,
X directions theory semivariable function is：
Y directions theory semivariable function is：
2. couple hyCoordinate carries out linear transformation, is allowed to be changed into h'y, then, γy(hy) the original base station value of holding is can be changed to, but
Become journey and γy(hy) one of change Cheng Xiangtong among semivariable function γ 'y(h'y)；
A 3. γ 'y(h'y) regard as in all directions same sex structure γx(hx) on the basis of again in h'yDirection is superimposed with another
The shrink-fit structure of a spherical model semivariable function, you can obtain fitting results model：
γ (h')=γ0(h'0)+γ1(h'x)+γ2(h'y)；
Semivariable function value in region between any two points can be obtained according to shrink-fit structure semivariable function model, with this mould
Type is to wait to estimate system valuation a little according to being extended to survey region.
Embodiment 1
With reference to attached drawing and using Delta of the Pearl River mesh self adaptability as embodiment, the present invention is described in further detail, but the reality
Example is applied to should not be construed as limiting the invention.
The synchronous measured water level data of step 1. collection river network hydrology website simultaneously carry out grid laying to mesh self adaptability.This hair
Bright to lay 51 × 51 grids altogether to Delta of the Pearl River river network, grid is away from 2km；Mesh self adaptability shares 66 hydrometric stations.
Step 2. combines gathered data and grid is laid, and calculates the water level semivariable function of all directions, you can research
Variability of the water level in all directions.When respectively to 16 days 13 July in 1999 with 22 days 6 July in 1999 when Delta of the Pearl River river
Barrier water level is calculated in North and South direction, east-west direction, 45 ° of southeast direction and southwestern 45 ° of direction semivariable functions.Fig. 2, Fig. 3's
Water level space interpolation isogram reflects the variation property of each variation function in different directions, it can be seen that water level exists
Consecutive variations are spatially presented；But 45 ° of southeast direction, the change journey of North and South direction are more than the change of east-west direction, southwestern 45 ° of directions
Journey, it illustrates 45 ° of southeast direction, the water level variability of North and South direction is less than the variation of east-west direction, southwestern 45 ° of direction water levels
Property, on four direction, base station value is different, it reflects water level in Delta of the Pearl River river network in zonal distribution.
The method of step 3. performance model superposition, the semivariable function of different change journeys is overlapped and forms a new set
Structure semivariable function model is closed, can conveniently be obtained in region between any two points according to the shrink-fit structure semivariable function model
Semivariable function value, and the water level for waiting to estimate a little to survey region is estimated.
As shown in figure 4, it is nested model semi-variogram, as shown in figure 5, joining for nested model of the present invention
Number (1999.07.16.13).Half change in region between any two points can conveniently be obtained according to shrink-fit structure semivariable function model
Different functional value, using this model to wait to estimate system valuation a little according to being extended to survey region.
It can be seen that from Fig. 4 and Fig. 5, the change journey of wet season Delta of the Pearl River net stream stage shows apart into 13.02km
Outside 2 points of 13.02km, the direct correlation of water level spatial variations disappears substantially, this is left with Delta of the Pearl River mesh self adaptability 10km
The right side has water system to divide the branch of a river, and really coincideing of dividing that water level relevance before and after the branch of a river substantially reduces.It is corresponding with becoming journey 13.02km
Water level base station value is 6.18m2, i.e., maximum variation function value, variability is strong and weak between it reflects water level；Block gold coefficient is 0.045,
It is caused by level measuring error, the consecutive variations of water level etc..
The theoretical semivariable function model of present case structure is used for kriging estimate value, therefore, for each known
The water level of point, is estimated these points using some known points of surrounding with Kriging method, assumes the point at this time to be unknown, so
The error between each point estimate and given value is calculated afterwards, with the minimum object function of evaluated error, preferably goes out the ginseng of model
Number.The present invention carries out 15 stations in the hydrometric station of Delta of the Pearl River river network 66 using semivariable function theoretical model curves
Estimation, its synthesis result is as shown in Figure 6 and Figure 7.
The result of calculation of Fig. 6, Fig. 7 show, if it is known that point is more around estimation point, with the effect of Kriging method estimation
Fruit is preferable, its relative error is below 1%.It can be seen that according to spatial variability principle, estimate that point water level is not only with Kriging method
Feasible, and adopt a rigorous approach, there is higher precision, and evaluated error can be provided.
Obviously, the above embodiment of the present invention is only intended to clearly illustrate example of the present invention, and is not pair
The restriction of embodiments of the present invention.For those of ordinary skill in the field, may be used also on the basis of the above description
To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this
All any modification, equivalent and improvement made within the spirit and principle of invention etc., should be included in the claims in the present invention
Protection domain within.
Claims (5)
1. a kind of hydrographic water resource feature space variability knows method for distinguishing, it is characterised in that comprises the following steps：
S1. the synchronous measured water level data of river network hydrology website are gathered and grid laying is carried out to mesh self adaptability；
S2. gathered data are combined and grid is laid, calculate the water level semivariable function of all directions, research water level is in all directions
Variability；
S3. the method for performance model superposition, the semivariable function of different change journeys is overlapped and forms a new shrink-fit structure
Semivariable function model, the semivariation in region between any two points can be conveniently obtained according to the shrink-fit structure semivariable function model
Functional value, and the water level for waiting to estimate a little to survey region is estimated.
2. a kind of hydrographic water resource feature space variability according to claim 1 knows method for distinguishing, it is characterised in that institute
The S2 steps stated include：
S21. experiment semivariable function γ is calculated*(h)：Using seeking [Z (xi)-Z
(xi+h)]2The method of arithmetic mean of instantaneous value calculate γ*(h)；Wherein, h is distance vector, and N (h) is the reality being separated by by vectorial h
Test the number of data pair, Z (xi) and Z (xi+ h) it is the point x that h is mutually divided on x directionsi(xi+ h) place observation；
S22. it is calculated all to h, γ*(h) value, makes h- γ*(h) semivariation figure is tested；
S23. on the basis of semivariable function is tested, it is fitted using suitable theoretical semivariable function model, profit
Water level feature space Variability Analysis is carried out with the model of fitting.
3. a kind of hydrographic water resource feature space variability according to claim 2 knows method for distinguishing, it is characterised in that institute
The S23 steps stated include：
S231. spherical model is selected, the general formulae of spherical model is：
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Wherein, C0For block gold constant；(C0+ C) it is base station value；C is known as sagitta；A is change journey；
S232. as 0 ＜ h≤a, weighted polynomial regression method fitting spherical model semi-variogram is selected.
4. a kind of hydrographic water resource feature space variability according to claim 3 knows method for distinguishing, it is characterised in that institute
The S3 steps stated include：
S31. the theoretical semivariable function in x directions and y directions is built, wherein,
X directions theory semivariable function is：
Y directions theory semivariable function is：
S32. to hyCoordinate carries out linear transformation, is allowed to be changed into h'y, then, γy(hy) the original base station value of holding is can be changed to, but become
Journey and γy(hy) one of change Cheng Xiangtong among semivariable function γ 'y(h'y)；
S33. γ 'y(h'y) regard as in all directions same sex structure γx(hx) on the basis of again in h'yDirection is superimposed with another
The shrink-fit structure of spherical model semivariable function, you can obtain fitting results model：
γ (h')=γ0(h'0)+γ1(h'x)+γ2(h'y)；
Semivariable function value in region between any two points can be obtained according to shrink-fit structure semivariable function model, using this model as
Wait to estimate system valuation a little according to survey region is extended to.
5. a kind of hydrographic water resource feature space variability according to any one of claims 1 to 4 knows method for distinguishing, it is special
Sign is that the grid is laid to be set according to river network size and site density and distribution situation.
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| CN102938148A (en) * | 2012-10-09 | 2013-02-20 | 河海大学 | High-spectrum image texture analysis method based on V-GLCM (Gray Level Co-occurrence Matrix) |
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| CN102938148A (en) * | 2012-10-09 | 2013-02-20 | 河海大学 | High-spectrum image texture analysis method based on V-GLCM (Gray Level Co-occurrence Matrix) |
| CN103617563A (en) * | 2013-12-05 | 2014-03-05 | 重庆大学 | Water supply pipeline non-monitoring node pressure determining method based on ground statistics space analysis theory |
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| 袁艳艳: "汉江流域降水量的空间插值研究", 《中国优秀硕士学位论文全文数据库 基础科学辑》 * |
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| CN113221320A (en) * | 2021-03-31 | 2021-08-06 | 国网江苏省电力有限公司电力科学研究院 | Method for rapidly calculating geomagnetic storm induction ground electric field based on apparent resistivity |
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