CN111859820A - Roughness partition calibration method and system based on posterior distribution - Google Patents
Roughness partition calibration method and system based on posterior distribution Download PDFInfo
- Publication number
- CN111859820A CN111859820A CN202010578498.5A CN202010578498A CN111859820A CN 111859820 A CN111859820 A CN 111859820A CN 202010578498 A CN202010578498 A CN 202010578498A CN 111859820 A CN111859820 A CN 111859820A
- Authority
- CN
- China
- Prior art keywords
- roughness
- sub
- value
- region
- roughness value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 87
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000005192 partition Methods 0.000 title claims abstract description 26
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 52
- 238000004364 calculation method Methods 0.000 claims abstract description 41
- 238000010606 normalization Methods 0.000 claims abstract description 24
- 238000009827 uniform distribution Methods 0.000 claims abstract description 17
- 238000007476 Maximum Likelihood Methods 0.000 claims abstract description 15
- 238000004088 simulation Methods 0.000 claims description 23
- 238000005070 sampling Methods 0.000 claims description 17
- 238000013178 mathematical model Methods 0.000 claims description 14
- 238000005259 measurement Methods 0.000 claims description 12
- 238000003860 storage Methods 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 11
- 239000000126 substance Substances 0.000 claims description 4
- 230000001419 dependent effect Effects 0.000 claims description 3
- 238000005315 distribution function Methods 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims description 3
- 239000012530 fluid Substances 0.000 claims description 3
- 238000013316 zoning Methods 0.000 claims description 2
- 230000006870 function Effects 0.000 description 16
- 238000010586 diagram Methods 0.000 description 11
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
Abstract
The invention discloses a roughness partition calibration method and a roughness partition calibration system based on posterior distribution, wherein the method comprises the following steps: step 1: dividing a target water body into k sub-regions; step 2: determining a roughness value range of a target water body; and step 3: dividing the determined roughness value range into n intervals, and simultaneously extracting a plurality of roughness combinations in each sub-area; and 4, step 4: selecting a target likelihood function to carry out normalization calculation to obtain likelihood values of all the subregions; and 5: respectively calculating the probability of the likelihood value of each subregion on each subinterval to obtain the roughness posterior distribution of each subregion; step 6: repeating the steps 3 to 5 for one iteration on the premise that the roughness posterior distribution of each sub-region is used for replacing the uniform distribution of the roughness of each sub-region; and 7: and obtaining the maximum likelihood value probability of each subregion and the optimal roughness value of each subregion corresponding to the maximum likelihood value probability. The invention can improve the roughness partition calibration precision.
Description
Technical Field
The invention relates to a roughness partition calibration method and system based on posterior distribution, and belongs to the technical field of water environment mathematical model parameter estimation and analysis.
Background
In the modeling process of large water bodies (such as rivers, lakes, reservoirs and the like), due to the fact that the physical and chemical properties of the water bodies have large space differences, a set of parameters are applied to the whole water body space, so that the situation that simulation results cannot give consideration to the current situations of water environments of all regions is inevitable, namely the situation that the simulation error of one region is small and the simulation error of the other region is large occurs, and parameter partition setting is particularly important for improving the model accuracy.
At present, the zoning setting parameters are known in the hydrodynamic simulation of large water bodies, and the most common parameter is the roughness of the bottom of the water body, namely roughness, which has great influence on the hydrodynamic simulation result, particularly on the water level. In the prior art, the setting of the roughness partition of the large river channel is common, but the value range of the roughness is reduced step by step according to simulation errors in different river reach, the trial and error time cost and the calculation cost cannot be ignored, the influence of human subjectivity is large, and a systematic calibration method is absent in the aspect of the setting of the roughness partition of the large lake. Meanwhile, the value range and prior distribution of the roughness are difficult to obtain, and particularly, the prior distribution of the roughness is generally uniformly distributed, so that the method is not scientific or reasonable when being applied to a large water body full of uncertainty and randomness.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a roughness partition calibration method and system based on posterior distribution, which can improve the roughness partition calibration precision.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
in a first aspect, the present invention provides a roughness partition calibration method based on posterior distribution, the method comprising the steps of:
step 1: dividing the target water body into k subregions (k >1) of A1, A2, k and Ak according to the water quality category and vegetation distribution;
step 2: determining the roughness value range of the target water body, and enabling the roughness prior distribution of each subregion to adopt uniform roughness distribution;
and step 3: equally dividing the roughness value range determined in the step 2 into n intervals, and respectively taking 1 roughness value from each subarea according to uniform distribution of the roughness in each interval, namely respectively extracting each subarea and obtaining n roughness values;
randomly selecting 1 roughness value from the A2, ·, Ak subareas to be combined with the 1 st roughness value of the A1 subarea, randomly selecting 1 roughness value from the A2, ·, Ak subareas to be combined with the 2 nd roughness value of the A1 subarea, and so on until randomly selecting 1 roughness value from the A2, ·, Ak subareas to be combined with the n-th roughness value of the A1 subarea, so that the A1 subarea has n roughness combinations;
Randomly selecting 1 roughness value from the sub-areas A1, A3,. cndot.. Ak to be combined with the 1 st roughness value of the sub-area A2, randomly selecting 1 roughness value from the sub-areas A1, A3,. cndot.. Ak to be combined with the 2 nd roughness value of the sub-area A2, and so on until randomly selecting 1 roughness value from the sub-areas A1, A3,. cndot.. Ak to be combined with the n th roughness value of the sub-area A2, so that the sub-area A2 has n roughness combinations;
and so on until 1 roughness value is randomly selected from the sub-areas A1, A2,. cndot.. cndot.and Ak-1 to be combined with the 1 st roughness value of the sub-area Ak, and 1 roughness value is randomly selected from the sub-areas A1, A2,. cndot.. cndot.and Ak-1 to be combined with the 2 nd roughness value of the sub-area Ak, and so on until 1 roughness value is randomly selected from the sub-areas A1, A2,. cndot.. cndot.and Ak-1 to be combined with the n th roughness value of the sub-area Ak, so that the sub-area Ak has n roughness combinations; each subarea has n roughness combinations, so that the roughness spatial distribution of the target water body is formed;
simultaneously extracting a plurality of roughness combinations in each sub-area;
and 4, step 4: inputting the corresponding groups of roughness combinations obtained by sampling in each sub-region in the step 3 into a water environment mathematical model, calculating to obtain a simulation result of the hydrodynamic index corresponding to each group of roughness combinations, and selecting a target likelihood function to carry out normalization calculation according to the actual measurement result of the hydrodynamic index to obtain a likelihood value of each sub-region;
And 5: equally dividing the roughness value range determined in the step (2) into a plurality of subintervals, and respectively calculating the probability of the likelihood value of each subregion on each subinterval to obtain the roughness posterior distribution of each subregion;
step 6: on the premise that the roughness posterior distribution of each sub-region obtained in the step 5 is used for replacing the uniform distribution of the roughness of each sub-region in the step 3, repeating the step 3 to the step 5, and performing iterative calculation for once;
and 7: and (4) respectively comparing the probability of the likelihood value of each subregion on each subinterval based on the calculation result of the step (6) to obtain the maximum likelihood value probability of each subregion and the optimal roughness value of each subregion corresponding to the maximum likelihood value probability.
With reference to the first aspect, further, the relationship between the number n of the regions in step 3 and the number k of the sub-regions in step 1 satisfies that n is greater than or equal to 10 k.
In combination with the first aspect, further, the mathematical model of water environment in step 4 adopts an EFDC (environmental fluid Dynamics Code) model.
With reference to the first aspect, further, in step 4, the hydrodynamic indicators include a water level, a flow rate, and a flow direction.
With reference to the first aspect, further, the target likelihood function in step 4 is a Nash-sutchelfe certainty coefficient of each sub-region, and a calculation formula thereof is:
Wherein L isiIs the Nash-sutfilffe certainty coefficient (i ═ 1, 2.., k) of the ith sub-region,
means for indicating hydrodynamic forceThe error variance of the target simulation result,
a variance representing an actual measurement of the hydrodynamic indicator;
error variance of simulation result of hydrodynamic index
The calculation formula of (2) is as follows:
variance of measured results of hydrodynamic indicators
The calculation formula of (2) is as follows:
wherein the content of the first and second substances,
refers to the actual measurement result value of the hydrodynamic index at the t-th moment,
referring to the simulation result value of the hydrodynamic index at the t-th moment,
the average value of the measured result values of the hydrodynamic index is referred to, T is T and T is 1, and T is the 1 st time.
With reference to the first aspect, further, the method for selecting the target likelihood function in step 4 to perform normalization calculation to obtain the likelihood value of each sub-region specifically includes:
setting 0.5 as a threshold, reserving Nash-Sutcilffe certainty coefficients of all sub-regions higher than the threshold, and reserving Nash-Sutcilffe certainty coefficients L of the ith sub-regioniPerforming a normalization process whichThe calculation formula is as follows:
wherein liThe likelihood value of the ith sub-region obtained after normalization for the target likelihood function,
Lminis the minimum of Nash-sutfilffe certainty coefficients for each sub-region,
LmaxIs the maximum of Nash-sutfilffe certainty coefficients for each sub-region.
With reference to the first aspect, further, in step 5, the posterior distribution of the roughness of the sub-regions is calculated by the following formula:
wherein f is a posterior distribution function which takes the roughness value X extracted by the subarea as an independent variable and the likelihood value probability of the subarea as a dependent variable,
fmis that the extracted roughness value X of the sub-region is located in the sub-region [ X ] of step 5m，Xk) The likelihood probability of the sub-region corresponding to the upper time,
lmextracting the mth roughness value X for the sub-region in the step 3mThe likelihood values for that sub-region obtained at the time,
lkextracting the kth roughness value X for the sub-region in step 3kThe likelihood values for that sub-region obtained at the time,
n is the number of roughness values extracted from the sub-region in the step 3, ljExtracting the jth roughness value X for the sub-region in step 3jThe likelihood value of the sub-region, j, is 1,2, …, n.
In a second aspect, the present invention provides a roughness partition rating system based on posterior distribution, the system comprising:
water body subregion module: the method is used for dividing a target water body into k subregions (k >1) of A1, A2, k and Ak according to water quality categories and vegetation distribution;
a determination module: the method is used for determining the roughness value range of the target water body and enabling the roughness prior distribution of each subarea to adopt uniform roughness distribution;
A sampling module: the method is used for equally dividing the determined roughness value range into n intervals, and each sub-area respectively takes 1 roughness value from each interval according to the uniform distribution of the roughness, namely each sub-area respectively extracts and obtains n roughness values; randomly selecting 1 roughness value from the A2, A, Ak subareas to be combined with the 1 st roughness value of the A1 subarea, randomly selecting 1 roughness value from the A2, A, Ak subareas to be combined with the 2 nd roughness value of the A1 subarea, and so on until randomly selecting 1 roughness value from the A2, A, Ak subareas to be combined with the n-th roughness value of the A1 subarea, so that the A1 subarea has n roughness combinations; randomly selecting 1 roughness value from the sub-areas A1, A3,. cndot.. Ak to be combined with the 1 st roughness value of the sub-area A2, randomly selecting 1 roughness value from the sub-areas A1, A3,. cndot.. Ak to be combined with the 2 nd roughness value of the sub-area A2, and so on until randomly selecting 1 roughness value from the sub-areas A1, A3,. cndot.. Ak to be combined with the n th roughness value of the sub-area A2, so that the sub-area A2 has n roughness combinations; and so on until 1 roughness value is randomly selected from the sub-areas A1, A2,. cndot.. cndot.and Ak-1 to be combined with the 1 st roughness value of the sub-area Ak, and 1 roughness value is randomly selected from the sub-areas A1, A2,. cndot.. cndot.and Ak-1 to be combined with the 2 nd roughness value of the sub-area Ak, and so on until 1 roughness value is randomly selected from the sub-areas A1, A2,. cndot.. cndot.and Ak-1 to be combined with the n th roughness value of the sub-area Ak, so that the sub-area Ak has n roughness combinations; each subarea has n roughness combinations, so that the roughness spatial distribution of the target water body is formed; then, the method is also used for simultaneously extracting a plurality of roughness combinations in each sub-area;
A normalization module: the device is used for inputting corresponding groups of roughness combinations obtained by sampling in each sub-region into a water environment mathematical model, calculating to obtain a simulation result of hydrodynamic indexes corresponding to each group of roughness combinations, and selecting a target likelihood function to carry out normalization calculation according to an actual measurement result of the hydrodynamic indexes to obtain a likelihood value of each sub-region;
roughness posterior distribution module: the probability of the likelihood value of each sub-region on each sub-region is respectively calculated to obtain the posterior distribution of the roughness of each sub-region;
and repeating the iteration module: the method is used for repeatedly operating the sampling module, the normalization module and the roughness posterior distribution module to carry out iterative one-time calculation on the premise that the roughness posterior distribution of each sub-region replaces the uniform distribution of the roughness of each sub-region;
a roughness optimal value-taking module: and the probability of the likelihood value of each sub-region on each sub-interval is respectively compared based on the calculation result of the repeated iteration module, so that the maximum likelihood value probability of each sub-region and the optimal roughness value of each corresponding sub-region are obtained.
In a third aspect, the present invention provides a coarseness partition rating system based on posterior distribution, including a processor and a storage medium;
The storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of any of the preceding methods.
In a fourth aspect, a computer-readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of the method of any one of the preceding claims.
Compared with the prior art, the invention has the following beneficial effects:
dividing a target water body into a plurality of sub-regions, under the assumption that the roughness obeys uniform distribution, firstly sampling the roughness combination in each sub-region and inputting the sampled combination into a water environment mathematical model, calculating to obtain the likelihood value of each sub-region, obtaining the roughness posterior distribution of each sub-region, then replacing the previous roughness uniform distribution of each sub-region with the roughness posterior distribution of each sub-region, carrying out secondary sampling on the roughness combination in each sub-region and inputting the sampled combination into the water environment mathematical model to obtain new roughness posterior distribution, and finally respectively comparing the probability of the likelihood value of each sub-region on each sub-region based on the new roughness posterior distribution to obtain the maximum likelihood value probability of each sub-region and the optimal roughness value of each sub-region corresponding to the maximum likelihood value probability;
The method and the device have the advantages that errors between simulation results and actual measurement results of hydrodynamic indexes are utilized, iteration is carried out once to obtain new roughness posterior distribution, so that the actual distribution is approximated, then the optimum roughness value is selected according to the new roughness posterior distribution, the value range of the roughness is reduced, and the roughness partition calibration precision is improved.
Drawings
FIG. 1 is a schematic block diagram of a roughness partition calibration method based on posterior distribution according to an embodiment of the present invention;
FIG. 2 is a first calculated posterior distribution diagram of the roughness of each sub-region according to an embodiment of the present invention;
fig. 3 is a roughness posterior distribution diagram of each sub-region obtained by the second calculation according to the embodiment of 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.
As shown in fig. 1, an embodiment of the present invention provides a roughness partition calibration method based on posterior distribution based on a water environment mathematical model of a large lake a, where the water environment mathematical model adopts an EFDC (Environmental fluid dynamics) model, and the method includes the following steps:
Step 1: the large lake A is divided into 4 sub-areas of A1, A2, A3 and A4 according to the water quality category and vegetation distribution.
Step 2: the roughness value range of the large lake A is determined to be 0-0.05 by reference documents and empirical formulas, and the roughness prior distribution of each subarea is uniformly distributed by the roughness with the roughness value of 0.02.
And step 3: adopting Latin hypercube sampling, equally dividing the roughness value range determined in the step 2 into 40 intervals, and respectively taking 1 roughness value from each sub-area according to uniform distribution of the roughness, namely respectively extracting 40 roughness values from each sub-area, wherein the roughness sampling result of each sub-area is shown in a table 1;
TABLE 1 roughness sampling result units (m) for each sub-region
Randomly selecting 1 roughness value from the sub-areas A2, A3 and A4 to be combined with the 1 st roughness value of the sub-area A1, randomly selecting 1 roughness value from the sub-areas A2, A3 and A4 to be combined with the 2 nd roughness value of the sub-area A1, and so on until randomly selecting 1 roughness value from the sub-areas A2, A3 and A4 to be combined with the 40 th roughness value of the sub-area A1, so that the sub-area A1 has 40 roughness combinations;
randomly selecting 1 roughness value from the sub-areas A1, A3 and A4 to be combined with the 1 st roughness value of the sub-area A2, randomly selecting 1 roughness value from the sub-areas A1, A3 and A4 to be combined with the 2 nd roughness value of the sub-area A2, and so on until randomly selecting 1 roughness value from the sub-areas A1, A3 and A4 to be combined with the 40 th roughness value of the sub-area A2, so that the sub-area A2 has 40 roughness combinations;
Randomly selecting 1 roughness value from the sub-areas A1, A2 and A4 to be combined with the 1 st roughness value of the sub-area A3, randomly selecting 1 roughness value from the sub-areas A1, A2 and A4 to be combined with the 2 nd roughness value of the sub-area A3, and so on until randomly selecting 1 roughness value from the sub-areas A1, A2 and A4 to be combined with the 40 th roughness value of the sub-area A3, so that the sub-area A3 has 40 roughness combinations;
randomly selecting 1 roughness value from the sub-areas A1, A2 and A3 to be combined with the 1 st roughness value of the sub-area A4, randomly selecting 1 roughness value from the sub-areas A1, A2 and A3 to be combined with the 2 nd roughness value of the sub-area A4, and so on until randomly selecting 1 roughness value from the sub-areas A1, A2 and A3 to be combined with the 40 th roughness value of the sub-area A4, so that the sub-area A4 has 40 roughness combinations;
each sub-area has 40 roughness combinations, thus constituting the spatial distribution of roughness of the whole lake A;
simultaneously extracting a plurality of roughness combinations in each sub-area;
the number of the intervals in the step 3 is just 10 times of the number of the sub-regions.
And 4, step 4: in this embodiment, the flow velocity is used as a hydrodynamic index, corresponding groups of roughness combinations obtained by sampling in each sub-region in step 3 are input into the EFDC model, a flow velocity simulation result corresponding to each group of roughness combinations is obtained by calculation, and a target likelihood function is selected according to a flow velocity actual measurement result to perform normalization calculation, so as to obtain a likelihood value of each sub-region;
The target likelihood function is a Nash-Sutcilffe certainty coefficient of each sub-region, and the calculation formula is as follows:
wherein L isiIs the Nash-sutfilffe certainty coefficient (i ═ 1, 2.., k) of the ith sub-region,
the error variance of the flow rate simulation results is represented,
representing the variance of the measured flow rate;
error variance of flow velocity simulation results
The calculation formula of (2) is as follows:
variance of measured flow rate results
The calculation formula of (2) is as follows:
wherein the content of the first and second substances,
refers to the measured flow rate value at the t-th moment,
refers to the flow velocity simulation result value at the t-th time,
the average value of the measured flow speed result values is indicated, T is T and 1 is the No. 1 time;
the method for selecting the target likelihood function to perform normalization calculation to obtain the likelihood value of each sub-region specifically comprises the following steps:
setting 0.5 as a threshold, reserving Nash-Sutcilffe certainty coefficients of all sub-regions higher than the threshold, and reserving Nash-Sutcilffe certainty coefficients L of the ith sub-regioniNormalization processing is carried out, and the calculation formula is as follows:
wherein liThe likelihood value of the ith sub-region obtained after normalization for the target likelihood function,
Lminis the minimum of Nash-sutfilffe certainty coefficients for each sub-region,
Lmaxis the maximum of Nash-sutfilffe certainty coefficients for each sub-region.
The results of the likelihood values of the various sub-regions after the normalization of the target likelihood function are shown in table 2.
TABLE 2 results of likelihood values for each subregion after normalization of the target likelihood function
And 5: dividing the roughness value range determined in the step 2 into 10 sub-intervals, respectively calculating the probability of the likelihood value of each sub-area on each sub-interval to obtain the roughness posterior distribution of each sub-area, wherein the calculation formula of the roughness posterior distribution of the sub-areas is as follows:
wherein f is a posterior distribution function which takes the roughness value X extracted by the subarea as an independent variable and the likelihood value probability of the subarea as a dependent variable,
fmis that the extracted roughness value X of the sub-region is located in the sub-region [ X ] of step 5m，Xk) The likelihood probability of the sub-region corresponding to the upper time,
lmextracting the mth roughness value X for the sub-region in the step 3mThe likelihood values for that sub-region obtained at the time,
lkextracting the kth roughness value X for the sub-region in step 3kThe likelihood values for that sub-region obtained at the time,
n is the number of roughness values extracted from the sub-region in step 3, i.e. n is 40, ljExtracting the jth roughness value X for the sub-region in step 3jThe likelihood value of the sub-region, j, is 1,2, …, n.
The roughness posterior distribution of each sub-region obtained by the first calculation is shown in fig. 2.
Step 6: on the premise that the roughness posterior distribution of each sub-region obtained in the step 5 is used for replacing the uniform distribution of the roughness of each sub-region in the step 3, repeating the step 3 to the step 5, and performing iterative calculation for once;
the roughness posterior distribution of each sub-region obtained by the second calculation is shown in fig. 3.
And 7: based on the calculation result in step 6, the probabilities of the likelihood values of each sub-region on each sub-region are respectively compared to obtain the maximum likelihood value probability of each sub-region and the roughness optimal value of each sub-region corresponding to the maximum likelihood value probability, and as shown in table 3, it can be found that the roughness optimal value of each sub-region deviates from the initial value of 0.02.
TABLE 3 roughness optimum value units (m) for each sub-region
The embodiment of the invention divides a target water body into a plurality of sub-regions, under the assumption that the roughness obeys uniform distribution, firstly, roughness combinations in each sub-region are sampled in a first step and input into a water environment mathematical model, the likelihood value of each sub-region is obtained through calculation, the roughness posterior distribution of each sub-region is obtained, then, the roughness posterior distribution of each sub-region is used for replacing the previous roughness uniform distribution of each sub-region, the roughness combinations in each sub-region are sampled for the second time and input into the water environment mathematical model, new roughness posterior distribution is obtained, and finally, the probability of the likelihood value of each sub-region on each sub-region is compared respectively based on the new roughness posterior distribution, so that the maximum likelihood value probability of each sub-region and the optimal value of the roughness of each sub-region corresponding to the maximum likelihood value probability are obtained.
According to the embodiment of the invention, the error between the simulation result and the actual measurement result of the hydrodynamic index is utilized, iteration is carried out once to obtain new roughness posterior distribution, so that the actual distribution is approximated, and then the optimum roughness value is selected according to the new roughness posterior distribution, so that the value range of the roughness is reduced, and the partition calibration precision of the roughness is improved.
The embodiment of the invention also provides a roughness partition calibration system based on posterior distribution, which comprises:
water body subregion module: the method is used for dividing a target water body into k subregions (k >1) of A1, A2, k and Ak according to water quality categories and vegetation distribution;
a determination module: the method is used for determining the roughness value range of the target water body and enabling the roughness prior distribution of each subarea to adopt uniform roughness distribution;
a sampling module: the method is used for equally dividing the determined roughness value range into n intervals, and each sub-area respectively takes 1 roughness value from each interval according to the uniform distribution of the roughness, namely each sub-area respectively extracts and obtains n roughness values; randomly selecting 1 roughness value from the A2, A, Ak subareas to be combined with the 1 st roughness value of the A1 subarea, randomly selecting 1 roughness value from the A2, A, Ak subareas to be combined with the 2 nd roughness value of the A1 subarea, and so on until randomly selecting 1 roughness value from the A2, A, Ak subareas to be combined with the n-th roughness value of the A1 subarea, so that the A1 subarea has n roughness combinations; randomly selecting 1 roughness value from the sub-areas A1, A3,. cndot.. Ak to be combined with the 1 st roughness value of the sub-area A2, randomly selecting 1 roughness value from the sub-areas A1, A3,. cndot.. Ak to be combined with the 2 nd roughness value of the sub-area A2, and so on until randomly selecting 1 roughness value from the sub-areas A1, A3,. cndot.. Ak to be combined with the n th roughness value of the sub-area A2, so that the sub-area A2 has n roughness combinations; and so on until 1 roughness value is randomly selected from the sub-areas A1, A2,. cndot.. cndot.and Ak-1 to be combined with the 1 st roughness value of the sub-area Ak, and 1 roughness value is randomly selected from the sub-areas A1, A2,. cndot.. cndot.and Ak-1 to be combined with the 2 nd roughness value of the sub-area Ak, and so on until 1 roughness value is randomly selected from the sub-areas A1, A2,. cndot.. cndot.and Ak-1 to be combined with the n th roughness value of the sub-area Ak, so that the sub-area Ak has n roughness combinations; each subarea has n roughness combinations, so that the roughness spatial distribution of the target water body is formed; then, the method is also used for simultaneously extracting a plurality of roughness combinations in each sub-area;
A normalization module: the device is used for inputting corresponding groups of roughness combinations obtained by sampling in each sub-region into a water environment mathematical model, calculating to obtain a simulation result of hydrodynamic indexes corresponding to each group of roughness combinations, and selecting a target likelihood function to carry out normalization calculation according to an actual measurement result of the hydrodynamic indexes to obtain a likelihood value of each sub-region;
roughness posterior distribution module: the probability of the likelihood value of each sub-region on each sub-region is respectively calculated to obtain the posterior distribution of the roughness of each sub-region;
and repeating the iteration module: the method is used for repeatedly operating the sampling module, the normalization module and the roughness posterior distribution module to carry out iterative one-time calculation on the premise that the roughness posterior distribution of each sub-region replaces the uniform distribution of the roughness of each sub-region;
a roughness optimal value-taking module: and the probability of the likelihood value of each sub-region on each sub-interval is respectively compared based on the calculation result of the repeated iteration module, so that the maximum likelihood value probability of each sub-region and the optimal roughness value of each corresponding sub-region are obtained.
The embodiment of the invention also provides a roughness partition rating system based on posterior distribution, which comprises a processor and a storage medium;
The storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the foregoing calibration method.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the calibration method.
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 a system 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 an instruction system 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 roughness partition calibration method based on posterior distribution is characterized by comprising the following steps:
step 1: dividing the target water body into k subregions (k >1) of A1, A2, k and Ak according to the water quality category and vegetation distribution;
step 2: determining the roughness value range of the target water body, and enabling the roughness prior distribution of each subregion to adopt uniform roughness distribution;
and step 3: equally dividing the roughness value range determined in the step 2 into n intervals, and respectively taking 1 roughness value from each subarea according to uniform distribution of the roughness in each interval, namely respectively extracting each subarea and obtaining n roughness values;
randomly selecting 1 roughness value from the A2, ·, Ak subareas to be combined with the 1 st roughness value of the A1 subarea, randomly selecting 1 roughness value from the A2, ·, Ak subareas to be combined with the 2 nd roughness value of the A1 subarea, and so on until randomly selecting 1 roughness value from the A2, ·, Ak subareas to be combined with the n-th roughness value of the A1 subarea, so that the A1 subarea has n roughness combinations;
randomly selecting 1 roughness value from the sub-areas A1, A3,. cndot.. Ak to be combined with the 1 st roughness value of the sub-area A2, randomly selecting 1 roughness value from the sub-areas A1, A3,. cndot.. Ak to be combined with the 2 nd roughness value of the sub-area A2, and so on until randomly selecting 1 roughness value from the sub-areas A1, A3,. cndot.. Ak to be combined with the n th roughness value of the sub-area A2, so that the sub-area A2 has n roughness combinations;
And so on until 1 roughness value is randomly selected from the sub-areas A1, A2,. cndot.. cndot.and Ak-1 to be combined with the 1 st roughness value of the sub-area Ak, and 1 roughness value is randomly selected from the sub-areas A1, A2,. cndot.. cndot.and Ak-1 to be combined with the 2 nd roughness value of the sub-area Ak, and so on until 1 roughness value is randomly selected from the sub-areas A1, A2,. cndot.. cndot.and Ak-1 to be combined with the n th roughness value of the sub-area Ak, so that the sub-area Ak has n roughness combinations; each subarea has n roughness combinations, so that the roughness spatial distribution of the target water body is formed;
simultaneously extracting a plurality of roughness combinations in each sub-area;
and 4, step 4: inputting the corresponding groups of roughness combinations obtained by sampling in each sub-region in the step 3 into a water environment mathematical model, calculating to obtain a simulation result of the hydrodynamic index corresponding to each group of roughness combinations, and selecting a target likelihood function to carry out normalization calculation according to the actual measurement result of the hydrodynamic index to obtain a likelihood value of each sub-region;
and 5: equally dividing the roughness value range determined in the step (2) into a plurality of subintervals, and respectively calculating the probability of the likelihood value of each subregion on each subinterval to obtain the roughness posterior distribution of each subregion;
Step 6: on the premise that the roughness posterior distribution of each sub-region obtained in the step 5 is used for replacing the uniform distribution of the roughness of each sub-region in the step 3, repeating the step 3 to the step 5, and performing iterative calculation for once;
and 7: and (4) respectively comparing the probability of the likelihood value of each subregion on each subinterval based on the calculation result of the step (6) to obtain the maximum likelihood value probability of each subregion and the optimal roughness value of each subregion corresponding to the maximum likelihood value probability.
2. The roughness partition rating method based on posterior distribution according to claim 1, wherein the relationship between the number n of regions in step 3 and the number k of subregions in step 1 satisfies n ≧ 10 k.
3. The posterior distribution-based coarseness zoning rating method according to claim 1, wherein the mathematical model of the water environment in the step 4 adopts an EFDC (Environmental Fluid Dynamics Code) model.
4. The roughness zonal calibration method based on posterior distribution as claimed in claim 1, wherein the hydrodynamic indicators in step 4 include water level, flow rate and flow direction.
5. The roughness partition rating method based on posterior distribution according to claim 1, wherein the target likelihood function in step 4 is Nash-sutciliffe certainty coefficient of each sub-region, and the calculation formula is:
Wherein L isiIs the Nash-sutfilffe certainty coefficient (i ═ 1, 2.., k) of the ith sub-region,
error variance of the simulation result representing the hydrodynamic index,
a variance representing an actual measurement of the hydrodynamic indicator;
error variance of simulation result of hydrodynamic index
The calculation formula of (2) is as follows:
variance of measured results of hydrodynamic indicators
The calculation formula of (2) is as follows:
wherein the content of the first and second substances,
refers to the actual measurement result value of the hydrodynamic index at the t-th moment,
referring to the simulation result value of the hydrodynamic index at the t-th moment,
the average value of the measured result values of the hydrodynamic index is referred to, T is T and T is 1, and T is the 1 st time.
6. The roughness partition rating method based on posterior distribution according to claim 5, wherein the method of selecting the target likelihood function for normalization calculation in step 4 to obtain the likelihood value of each sub-region specifically comprises:
setting 0.5 as a threshold, reserving Nash-Sutcilffe certainty coefficients of all sub-regions higher than the threshold, and reserving Nash-Sutcilffe certainty coefficients L of the ith sub-regioniNormalization processing is carried out, and the calculation formula is as follows:
wherein liThe likelihood value of the ith sub-region obtained after normalization for the target likelihood function,
LminIs the minimum of Nash-sutfilffe certainty coefficients for each sub-region,
Lmaxis the maximum of Nash-sutfilffe certainty coefficients for each sub-region.
7. The roughness zonal calibration method based on posterior distribution as claimed in claim 1, wherein the posterior distribution of roughness of sub-zone in step 5 is calculated by the formula:
wherein f is a posterior distribution function which takes the roughness value X extracted by the subarea as an independent variable and the likelihood value probability of the subarea as a dependent variable,
fmis that the extracted roughness value X of the sub-region is located in the sub-region [ X ] of step 5m，Xk) The child corresponding to the previous timeThe probability of the likelihood value of the region,
lmextracting the mth roughness value X for the sub-region in the step 3mThe likelihood values for that sub-region obtained at the time,
lkextracting the kth roughness value X for the sub-region in step 3kThe likelihood values for that sub-region obtained at the time,
n is the number of roughness values extracted from the sub-region in the step 3, ljExtracting the jth roughness value X for the sub-region in step 3jThe likelihood value of the sub-region, j, is 1,2, …, n.
8. A roughness partition rating system based on a posterior distribution, the system comprising:
water body subregion module: the method is used for dividing a target water body into k subregions (k >1) of A1, A2, k and Ak according to water quality categories and vegetation distribution;
A determination module: the method is used for determining the roughness value range of the target water body and enabling the roughness prior distribution of each subarea to adopt uniform roughness distribution;
a sampling module: the method is used for equally dividing the determined roughness value range into n intervals, and each sub-area respectively takes 1 roughness value from each interval according to the uniform distribution of the roughness, namely each sub-area respectively extracts and obtains n roughness values; randomly selecting 1 roughness value from the A2, A, Ak subareas to be combined with the 1 st roughness value of the A1 subarea, randomly selecting 1 roughness value from the A2, A, Ak subareas to be combined with the 2 nd roughness value of the A1 subarea, and so on until randomly selecting 1 roughness value from the A2, A, Ak subareas to be combined with the n-th roughness value of the A1 subarea, so that the A1 subarea has n roughness combinations; randomly selecting 1 roughness value from the sub-areas A1, A3,. cndot.. Ak to be combined with the 1 st roughness value of the sub-area A2, randomly selecting 1 roughness value from the sub-areas A1, A3,. cndot.. Ak to be combined with the 2 nd roughness value of the sub-area A2, and so on until randomly selecting 1 roughness value from the sub-areas A1, A3,. cndot.. Ak to be combined with the n th roughness value of the sub-area A2, so that the sub-area A2 has n roughness combinations; and so on until 1 roughness value is randomly selected from the sub-areas A1, A2,. cndot.. cndot.and Ak-1 to be combined with the 1 st roughness value of the sub-area Ak, and 1 roughness value is randomly selected from the sub-areas A1, A2,. cndot.. cndot.and Ak-1 to be combined with the 2 nd roughness value of the sub-area Ak, and so on until 1 roughness value is randomly selected from the sub-areas A1, A2,. cndot.. cndot.and Ak-1 to be combined with the n th roughness value of the sub-area Ak, so that the sub-area Ak has n roughness combinations; each subarea has n roughness combinations, so that the roughness spatial distribution of the target water body is formed; then, the method is also used for simultaneously extracting a plurality of roughness combinations in each sub-area;
A normalization module: the device is used for inputting corresponding groups of roughness combinations obtained by sampling in each sub-region into a water environment mathematical model, calculating to obtain a simulation result of hydrodynamic indexes corresponding to each group of roughness combinations, and selecting a target likelihood function to carry out normalization calculation according to an actual measurement result of the hydrodynamic indexes to obtain a likelihood value of each sub-region;
roughness posterior distribution module: the probability of the likelihood value of each sub-region on each sub-region is respectively calculated to obtain the posterior distribution of the roughness of each sub-region;
and repeating the iteration module: the method is used for repeatedly operating the sampling module, the normalization module and the roughness posterior distribution module to carry out iterative one-time calculation on the premise that the roughness posterior distribution of each sub-region replaces the uniform distribution of the roughness of each sub-region;
a roughness optimal value-taking module: and the probability of the likelihood value of each sub-region on each sub-interval is respectively compared based on the calculation result of the repeated iteration module, so that the maximum likelihood value probability of each sub-region and the optimal roughness value of each corresponding sub-region are obtained.
9. A coarseness partition rating system based on posterior distribution comprises a processor and a storage medium;
The storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 7.
10. Computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010578498.5A CN111859820A (en) | 2020-06-23 | 2020-06-23 | Roughness partition calibration method and system based on posterior distribution |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010578498.5A CN111859820A (en) | 2020-06-23 | 2020-06-23 | Roughness partition calibration method and system based on posterior distribution |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111859820A true CN111859820A (en) | 2020-10-30 |
Family
ID=72988277
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010578498.5A Pending CN111859820A (en) | 2020-06-23 | 2020-06-23 | Roughness partition calibration method and system based on posterior distribution |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111859820A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114818320A (en) * | 2022-04-25 | 2022-07-29 | 珠江水利委员会珠江水利科学研究院 | Giant river network hydrodynamic force mathematical model calibration method, system and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107145678A (en) * | 2017-05-22 | 2017-09-08 | 中国水利水电科学研究院 | A kind of rating method of Two Dimensional Plane Flow in Rivers model roughness |
| CN108920737A (en) * | 2018-04-24 | 2018-11-30 | 中国水利水电科学研究院 | A kind of particle filter assimilation method of hydrodynamic model, device and calculate equipment |
| CN109858779A (en) * | 2019-01-14 | 2019-06-07 | 河海大学 | A kind of Water Environment Mathematical Model water quality parameter is uncertain and Sensitivity Analysis |
-
2020
- 2020-06-23 CN CN202010578498.5A patent/CN111859820A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107145678A (en) * | 2017-05-22 | 2017-09-08 | 中国水利水电科学研究院 | A kind of rating method of Two Dimensional Plane Flow in Rivers model roughness |
| CN108920737A (en) * | 2018-04-24 | 2018-11-30 | 中国水利水电科学研究院 | A kind of particle filter assimilation method of hydrodynamic model, device and calculate equipment |
| CN109858779A (en) * | 2019-01-14 | 2019-06-07 | 河海大学 | A kind of Water Environment Mathematical Model water quality parameter is uncertain and Sensitivity Analysis |
Non-Patent Citations (1)
| Title |
|---|
| 张潮: "基于机器学习的河网糙率反演", 《中国优秀博硕士学位论文全文数据库(博士)工程科技Ⅱ辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114818320A (en) * | 2022-04-25 | 2022-07-29 | 珠江水利委员会珠江水利科学研究院 | Giant river network hydrodynamic force mathematical model calibration method, system and storage medium |
| CN114818320B (en) * | 2022-04-25 | 2022-11-08 | 珠江水利委员会珠江水利科学研究院 | Giant river network hydrodynamic force mathematical model calibration method, system and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108469507B (en) | Effluent BOD soft measurement method based on self-organizing RBF neural network | |
| CN112508243B (en) | Training method and device for multi-fault prediction network model of power information system | |
| JP2019513265A (en) | Method and apparatus for automatic multi-threshold feature filtering | |
| CN109543912B (en) | Reservoir optimal scheduling decision model generation method based on deep learning | |
| CN108647272A (en) | A kind of small sample extending method based on data distribution | |
| CN112733904B (en) | Water quality abnormity detection method and electronic equipment | |
| CN110442911B (en) | High-dimensional complex system uncertainty analysis method based on statistical machine learning | |
| CN112733273A (en) | Method for determining Bayesian network parameters based on genetic algorithm and maximum likelihood estimation | |
| CN109284477A (en) | A kind of rich withered combined probability calculation method and device of Hydrologic Series | |
| CN112836435A (en) | Coarse grid numerical simulation result correction method and device and electronic equipment | |
| CN111859820A (en) | Roughness partition calibration method and system based on posterior distribution | |
| CN108470214B (en) | Bounded error parameter estimation method based on interval optimization algorithm | |
| CN112885415B (en) | Quick screening method for estrogen activity based on molecular surface point cloud | |
| CN117113264B (en) | Method for detecting abnormality of dissolved oxygen meter of sewage plant on line in real time | |
| Cosby et al. | Identification of photosynthesis-light models for aquatic systems I. Theory and simulations | |
| CN116679026A (en) | Self-adaptive unbiased finite impulse response filtering sewage dissolved oxygen concentration estimation method | |
| CN109829000B (en) | Consistency processing method and device for river annual runoff data | |
| CN116503147A (en) | Financial risk prediction method based on deep learning neural network | |
| CN116822743A (en) | Wind power prediction method based on two-stage decomposition reconstruction and error correction | |
| CN114842921A (en) | Municipal sewage dephosphorization agent adding method based on linear regression and decision tree model | |
| CN113868939A (en) | Wind power probability density evaluation method, device, equipment and medium | |
| CN110427655B (en) | Landslide sensitive state extraction method | |
| CN112596702A (en) | Chaotization method based on disturbance and pseudo random sequence generation method | |
| Davis et al. | Statistical Methods for Data Analysis | |
| CN112801350A (en) | Deep learning ultra-short-time wind power prediction method based on uncertainty |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201030 |
|
| RJ01 | Rejection of invention patent application after publication |