CN109783884A - The Real-time Flood Forecasting error correcting method corrected simultaneously based on areal rainfall and model parameter - Google Patents
The Real-time Flood Forecasting error correcting method corrected simultaneously based on areal rainfall and model parameter Download PDFInfo
- Publication number
- CN109783884A CN109783884A CN201811586544.5A CN201811586544A CN109783884A CN 109783884 A CN109783884 A CN 109783884A CN 201811586544 A CN201811586544 A CN 201811586544A CN 109783884 A CN109783884 A CN 109783884A
- Authority
- CN
- China
- Prior art keywords
- error
- rainfall
- model
- model parameter
- basin
- 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.)
- Granted
Links
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Landscapes
- Feedback Control In General (AREA)
Abstract
The invention discloses the Real-time Flood Forecasting error correcting methods corrected simultaneously based on areal rainfall and model parameter, by the way that Real-time Flood Forecasting overall error is divided into rainfall error and model error by a certain percentage, receptance function correction model is reused, is corrected while realizing the input of face rainfall and model parameter.The flood forcast modification method provided by the invention that face mean rainfall and model parameter can be corrected simultaneously, overcoming can only assume that one does not have error in face rainfall and model parameter currently based on the Real-time Flood Forecasting error correcting technology of system response theory, and overall error is all used as to another error to correct this deficiency, it has great significance for improving Precision of Flood Forecast.
Description
Technical field
The present invention relates to the Real-time Flood Forecasting error correcting methods corrected simultaneously based on areal rainfall and model parameter.
Background technique
Hydrologic forecast is an ancient problem, and people are long-standing to the exploration of flood forecasting, flood forecasting it is accurate
Property and timeliness important in inhibiting in flood control and disaster reduction.However when carrying out flood forecasting with hydrological model, error is always
In the presence of forecast precision is always difficult to meet.Forecasting model initial state value error, mode input error and model parameter are non-
Optimization can all influence the precision of forecast result, so being often used error correcting technology to reduce model in the mistake for generation of giving the correct time in advance
Difference.
Traditional error correcting method has very much, such as autoregression modification method, Kalman Filter Technology, but these
Method basically there exist physical basis it is not strong, correction effect is undesirable the defects of.Therefore, at present with it is more be based on being
Dynamical system response curve is creatively introduced into error by the Real-time Flood Forecasting modification method for response theory of uniting, this method
In amendment, structure is simple, clear physics conception, can opposite mean rainfall error be modified or runoff yield error repaired
Just, there is certain effect to raising Precision of Flood Forecast.It would be appreciated that both modification methods can only assume face
One does not have error in rainfall and model parameter, and overall error is all used as to another error to correct.And there is research
Show be modified great flood in application, the correction effect of face mean rainfall modification method is preferable；For medium and small flood
Water, the effect of runoff yield modification method are better than the effect of face mean rainfall method.
Summary of the invention
It is an object of the invention to overcome deficiency in the prior art, provide a kind of same based on areal rainfall and model parameter
The Real-time Flood Forecasting error correcting method of Shi Jiaozheng, solution can only assume in face rainfall and model parameter in the prior art
One does not have error, and overall error is all used as another error carry out modified deficiency.
In order to solve the above technical problems, the technical scheme adopted by the invention is that: simultaneously based on areal rainfall and model parameter
The Real-time Flood Forecasting error correcting method of correction, which comprises the steps of:
(1) the target basin A not high to precipitation station density, finds that precipitation station density is higher closes on basin or hydrology phase
Like basin B, and collect the rainfall data and basin Outlet Section discharge process of basin B；
(2) Real-time Flood Forecasting is carried out based on Xinanjiang model watershed B, obtains the flood under different precipitation station density ps
Prediction error, and precipitation station density p and rainfall error rate η are established accordinglyPWith model error ratio ηθRelationship；
(3) relationship obtained according to step (2) obtains the drop of target basin A by the precipitation station density p of target basin A
Rain error rate ηPWith model error ratio ηθ；
(4) to each period of target basin A Real-time Flood Forecasting all in accordance with this ratio, by flood forecasting overall error
ETIt is divided into rainfall error EPWith model error Eθ；
(5) it is based on Real-time Flood Forecasting receptance function correction model, to rainfall error EPWith model error EθIt carries out respectively
Amendment is realized and is corrected simultaneously to the face mean rainfall error correction of target basin A and to Errors.
Further, flood forcast in the step (2) and (4) is weighed with deterministic coefficient 1-DC index
Amount, meanwhile, which is the mean error of more floods.
Further, precipitation station density p and rainfall error rate η are established in the step (2)PWith model error ratio ηθ
Relationship, include the following steps:
(1) under different precipitation station density ps or precipitation station number n, flood forecasting overall error ET,nConsistently equal to face is averaged rain
Measure error EP,nWith Errors EθThe sum of, i.e. ET,n=EP,n+Eθ；
(2) according to the rainfall data of N number of precipitation station of basin B, one group of optimal model parameters is determined, because model is joined
Number is not change with precipitation station number and become, so Errors EθIt is also constant；
(3) when precipitation station number n=N, precipitation station density p reaches maximum, then B rainfall in basin does not have error at this time, i.e.,
EP,N=0, then Errors E at this timeθ=ET,N；
(4) and so on, it obtains under different precipitation station density ps or precipitation station number n, face mean rainfall error EP,nAnd mould
Shape parameter error Eθ；
(5) face mean rainfall error E is usedP,nDivided by flood forecasting overall error ET,n, as rainfall error rate ηP,n；
(6) Errors E is usedθDivided by flood forecasting overall error ET,n, as model error ratio ηθ,n；
(7) and so on, the flood forcast under different precipitation station density is obtained, precipitation station density p and rainfall are established
Error rate ηPWith model error ratio ηθRelationship.
Further, the face mean rainfall error correction of target basin A is included the following steps: in the step (5)
In (1) face mean rainfall Series P for needing to be corrected, each period face mean rainfall pi(i=1~n) exists
Remaining period face mean rainfall pjThe face mean rainfall for increasing by 1 unit on the basis of (j ≠ i) is constant, it is flat to obtain new face
Equal rainfall series PiIt indicates；
(2) with new face mean rainfall Series PiDischarge process is obtained after calculating by model；
(3) flow being calculated with former face mean rainfall Series P is subtracted with the discharge process that step (2) is calculated
The obtained graph of process, as face mean rainfall piSystem response curve, be expressed as Ui(t), each column in U matrix
Acquired with same method；
(4) by formula Q (P, θ, t)=Q (PC, θ, t) and the calculating formula of the available face mean rainfall correction amount of+U Δ P+ ε is
Δ P=(UTU)-1UT(Q(P,θ,t)-Q(PC, θ, t)), wherein Q (P, θ, t) is measured discharge process, Q (PC, θ, t) and it is new peace
River model prediction discharge process, PCFor effective surface mean rainfall series, θ is optimal model parameters, and t is the time, and U rings for system
Curve is answered, Δ P is the face mean rainfall correction amount to be solved, and ε is effective surface mean rainfall random error, Q (P, θ, t)-Q
(PC, θ, t) and it is rainfall error EP, then revised face mean rainfall series is P 'C=PC+ΔP；
(5) by revised face mean rainfall Series P 'CAgain it is calculated with Xinanjiang model modified to get arriving
The calculating discharge process Q ' of basin Outlet Sectionu。
Further, the Errors of target basin A are corrected in the step (5), is included the following steps:
In (1) model parameter series θ for needing to be corrected, each model parameterIn remaining model
ParameterIncrease by 1 unit on the basis of constant, obtains new model parameter series θiIt indicates；
(2) with new model parameter series θiDischarge process is obtained after calculating by model；
(3) the flow mistake being calculated with master mould parameter series θ is subtracted with the discharge process that step (2) is calculated
The obtained graph of journey, as model parameterSystem response curve, be expressed as Vi(t), each column in V matrix are equal
It is acquired with same method；
(4) by formula Q (P, θ, t)=Q (P, θC, t)+V Δ θ+ε obtain model parameter correction amount calculating formula be Δ θ=
(VTV)-1VT(Q(P,θ,t)-Q(P,θC, t)), wherein Q (P, θ, t) is measured discharge process, Q (P, θC, t) and it is Xinanjiang model
Forecasting runoff process, P are effective surface mean rainfall series, θCFor calibration optimal model parameters, t is the time, and V is system response
Curve, Δ θ are the optimal model parameters correction amount to be solved, and ε is calibration optimal model parameters random error, Q (P, θ, t)-Q
(P,θC, t) and it is model error Eθ, then revised model parameter series is θ 'C=θC+Δθ；
(5) by revised model parameter series R 'cAgain it is calculated with Xinanjiang model to get modified stream is arrived
The calculating discharge process Q " of domain Outlet Sectionu。
Compared with prior art, the beneficial effects obtained by the present invention are as follows being: the present invention overcomes respond currently based on system
Theoretical Real-time Flood Forecasting error correcting technology can only assume in face rainfall and model parameter that does not have an error, and incite somebody to action
Overall error is used as another error all to correct this deficiency, is suitable for data-deficiency region, for improving flood forecasting
Precision has great significance.
Specific embodiment
The invention will be further described below.Following embodiment is only used for clearly illustrating technology of the invention
Scheme, and not intended to limit the protection scope of the present invention.
Below with reference to example, the present invention will be further explained.
Existing a certain basin A only has precipitation station 5, i.e., (precipitation station density is equal to the precipitation station density of target basin A
Valley rainfall station number is divided by drainage area) it is lower；
The hydrology analogy basin B, basin B for searching out basin A have precipitation station 59, and the precipitation station density of basin B is higher,
Collect each station rainfall data and basin Outlet Section discharge process of basin B；
Method according to the present invention, using the complete website data of hydrology analogy basin B, to only 5 precipitation station data
The process that the Real-time Flood Forecasting error of basin A is modified are as follows:
(1) different precipitation station density psn(ρn=n/S；Wherein, n is precipitation station number, and S is drainage area.) under, flood is pre-
Report overall error ET,nConsistently equal to face mean rainfall error EP,nWith Errors EθThe sum of (i.e. ET,n=EP,n+Eθ), and flood
Water prediction error is measured with 1-DC (deterministic coefficient) index；
Because model parameter series θ is not change with precipitation station density p and become, i.e., model parameter should be unique,
So Errors EθIt is also not with precipitation station density p (or precipitation station number n) variation, and flood forecasting overall error
ET,nWith face mean rainfall error EP,nIt can change with precipitation station density p, i.e., precipitation station density is always missed with flood forecasting
Difference, the relationship of rainfall error and model error are as follows:
Precipitation station density overall error rainfall error model error
According to rainfall error rate ηP,nEqual to face mean rainfall error EP,nDivided by overall error ET,nAnd model error ratio
Example ηθ,nEqual to Errors EθDivided by overall error ET,n, obtain precipitation station density and rainfall error rate and model error
The relationship of ratio is；
Precipitation station density rainfall error rate model error ratio
(2) it is based on Xinanjiang model, Real-time Flood Forecasting is carried out to the basin B for there are 59 precipitation stations (i.e. N=59), is obtained
To different precipitation station density psn(n=1,2 ..., 59, ρ1=1/SB,ρ2=2/SB,…,ρ59=59/SB, wherein SBFor basin B's
Area) under flood forecasting overall error ET,n(n=1,2 ..., 59, ET,1,ET,2,…,ET,59)；
According to the rainfall data of the 59 of basin B precipitation stations, one group of optimal model parameters is determined, because model parameter is missed
Poor EθDo not change with precipitation station density p, it is believed that as precipitation station number n=59, precipitation station density p reaches maximum, then flows at this time
Domain B rainfall does not have error, i.e. EP,59=0, then Errors E at this timeθ=ET,59；
According to the pass of the precipitation station density and flood forecasting overall error, rainfall error and model error established in step (1)
System, obtains basin B difference precipitation station density pn(n=1,2 ..., 59, ρ1=1/SB,ρ2=2/SB,…,ρ59=59/SB) under
Face mean rainfall error EP,n(n=1,2 ..., 59, EP,1=ET,1-ET,59,EP,2=ET,2-ET,59,…,EP,59=0)；
The relationship of the different precipitation station density and flood forecasting overall error, rainfall error and model error of basin B is are as follows:
Precipitation station density overall error rainfall error model error
According to the relationship of the precipitation station density and rainfall error rate and model error ratio established in step (1), obtain
The different precipitation station density and rainfall error rate of basin B and the relationship of model error ratio are are as follows:
Precipitation station density rainfall error rate model error ratio
(3) the precipitation station density of the basin B obtained according to step (2) and rainfall error rate and model error ratio
Relationship, by precipitation station density p (ρ=5/S of target basin AB), inquiry obtains the rainfall error rate η of target basin APAnd mould
Type error rate ηθ；
(4) to each period of target basin A Real-time Flood Forecasting all in accordance with this ratio, by flood forecasting overall error
ETIt is divided into rainfall error EPWith model error Eθ；
(5) it is based on Real-time Flood Forecasting receptance function correction model, to rainfall error EPWith model error EθIt carries out respectively
Amendment is realized and is corrected simultaneously to the face mean rainfall error correction of target basin A and to Errors.
First to the face mean rainfall error E of target basin APAmendment:
In one face mean rainfall Series P for needing to be corrected, each period face mean rainfall pi(i=1~n) is at it
Remaining period face mean rainfall pjThe face mean rainfall for increasing by 1 unit on the basis of (j ≠ i) is constant, it is average to obtain new face
Rainfall series PiIt indicates；
With new face mean rainfall Series PiDischarge process is obtained after calculating by model；
The discharge process being calculated with former face mean rainfall Series P is subtracted with the discharge process that step (2) is calculated
Obtained graph, as face mean rainfall piSystem response curve, be expressed as Ui(t), each column in U matrix are used
Same method acquires；
By formula Q (P, θ, t)=Q (PC, θ, t) the available face mean rainfall correction amount of+U Δ P+ ε calculating formula be Δ P
=(UTU)-1UT(Q(P,θ,t)-Q(PC, θ, t)), wherein Q (P, θ, t) is measured discharge process, Q (PC, θ, t) and it is Xinanjiang River mould
Type forecasting runoff process, PCFor effective surface mean rainfall series, θ is optimal model parameters, and t is the time, and U is that system response is bent
Line, Δ P are the face mean rainfall correction amount to be solved, and ε is effective surface mean rainfall random error, Q (P, θ, t)-Q (PC,θ,
It t) is rainfall error EP, then revised face mean rainfall series is P 'C=PC+ΔP；
By revised face mean rainfall Series P 'CAgain it is calculated with Xinanjiang model, modified stream can be obtained
The calculating discharge process Q ' of domain Outlet Sectionu。
Again to the Errors E of target basin AθAmendment:
In one model parameter series θ for needing to be corrected, each model parameterIn remaining model parameterIncrease by 1 unit on the basis of constant, obtains new model parameter series θiIt indicates；
With new model parameter series θiDischarge process is obtained after calculating by model；
The discharge process institute being calculated with master mould parameter series θ is subtracted with the discharge process that step (2) is calculated
Obtained graph, as model parameterSystem response curve, be expressed as Vi(t).Each column in V matrix are used together
Quadrat method acquires；
By formula Q (P, θ, t)=Q (P, θC, t) the available model parameter correction amount of+V Δ θ+ε calculating formula be Δ θ=
(VTV)-1VT(Q(P,θ,t)-Q(P,θC, t)), wherein Q (P, θ, t) is measured discharge process, Q (P, θC, t) and it is Xinanjiang model
Forecasting runoff process, P are effective surface mean rainfall series, θCFor calibration optimal model parameters, t is the time, and V is system response
Curve, Δ θ are the optimal model parameters correction amount to be solved, and ε is calibration optimal model parameters random error, Q (P, θ, t)-Q
(P,θC, t) and it is model error Eθ, then revised model parameter series is θ 'C=θC+Δθ；
By revised model parameter series R 'cAgain it is calculated with Xinanjiang model, modified basin can be obtained
The calculating discharge process Q " of Outlet Sectionu。
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, several improvement and deformations can also be made, these improve and become
Shape also should be regarded as protection scope of the present invention.
Claims (5)
1. the Real-time Flood Forecasting error correcting method corrected simultaneously based on areal rainfall and model parameter, which is characterized in that including
Following steps:
(1) the target basin A not high to precipitation station density, finds that precipitation station density is higher closes on basin or hydrology analogy basin
B, and collect the rainfall data and basin Outlet Section discharge process of basin B；
(2) Real-time Flood Forecasting is carried out based on Xinanjiang model watershed B, obtains the flood forecasting under different precipitation station density ps
Error, and precipitation station density p and rainfall error rate η are established accordinglyPWith model error ratio ηθRelationship；
(3) relationship obtained according to step (2) obtains the rainfall error of target basin A by the precipitation station density p of target basin A
Ratio ηPWith model error ratio ηθ；
(4) to each period of target basin A Real-time Flood Forecasting all in accordance with this ratio, by flood forecasting overall error ETIt is divided into
Rainfall error EPWith model error Eθ；
(5) it is based on Real-time Flood Forecasting receptance function correction model, to rainfall error EPWith model error EθIt is modified respectively,
It realizes and is corrected simultaneously to the face mean rainfall error correction of target basin A and to Errors.
2. the Real-time Flood Forecasting error correction side according to claim 1 corrected simultaneously based on areal rainfall and model parameter
Method, which is characterized in that flood forcast in the step (2) and (4) is measured with deterministic coefficient 1-DC index, together
When, which is the mean error of more floods.
3. the Real-time Flood Forecasting error correction side according to claim 1 corrected simultaneously based on areal rainfall and model parameter
Method, which is characterized in that establish precipitation station density p and rainfall error rate η in the step (2)PWith model error ratio ηθPass
System, includes the following steps:
(1) under different precipitation station density ps or precipitation station number n, flood forecasting overall error ET,nConsistently equal to face mean rainfall error
EP,nWith Errors EθThe sum of, i.e. ET,n=EP,n+Eθ；
(2) according to the rainfall data of N number of precipitation station of basin B, one group of optimal model parameters is determined, because model parameter is not
Change with precipitation station number and become, so Errors EθIt is also constant；
(3) when precipitation station number n=N, precipitation station density p reaches maximum, then B rainfall in basin does not have error, i.e. E at this timeP,N=0,
Then Errors E at this timeθ=ET,N；
(4) and so on, it obtains under different precipitation station density ps or precipitation station number n, face mean rainfall error EP,nJoin with model
Number error Eθ；
(5) face mean rainfall error E is usedP,nDivided by flood forecasting overall error ET,n, as rainfall error rate ηP,n；
(6) Errors E is usedθDivided by flood forecasting overall error ET,n, as model error ratio ηθ,n；
(7) and so on, the flood forcast under different precipitation station density is obtained, precipitation station density p and rainfall error are established
Ratio ηPWith model error ratio ηθRelationship.
4. the Real-time Flood Forecasting error correction side according to claim 1 corrected simultaneously based on areal rainfall and model parameter
Method, which is characterized in that the face mean rainfall error correction of target basin A in the step (5), include the following steps:
In (1) face mean rainfall Series P for needing to be corrected, each period face mean rainfall pi(i=1~n) is at remaining
Section face mean rainfall pjThe face mean rainfall for increasing by 1 unit on the basis of (j ≠ i) is constant, obtains new face mean rainfall
Series PiIt indicates；
(2) with new face mean rainfall Series PiDischarge process is obtained after calculating by model；
(3) the discharge process institute being calculated with former face mean rainfall Series P is subtracted with the discharge process that step (2) is calculated
Obtained graph, as face mean rainfall piSystem response curve, be expressed as Ui(t), each column in U matrix are used together
Quadrat method acquires；
(4) by formula Q (P, θ, t)=Q (PC, θ, t) the available face mean rainfall correction amount of+U △ P+ ε calculating formula be △ P=
(UTU)-1UT(Q(P,θ,t)-Q(PC, θ, t)), wherein Q (P, θ, t) is measured discharge process, Q (PC, θ, t) and it is Xinanjiang model
Forecasting runoff process, PCFor effective surface mean rainfall series, θ is optimal model parameters, and t is the time, and U is system response curve,
△ P is the face mean rainfall correction amount to be solved, and ε is effective surface mean rainfall random error, Q (P, θ, t)-Q (PC, θ, t) i.e.
For rainfall error EP, then revised face mean rainfall series is P 'C=PC+△P；
(5) by revised face mean rainfall Series P 'CAgain it is calculated to go out to get to modified basin with Xinanjiang model
The calculating discharge process Q ' of mouth sectionu。
5. the Real-time Flood Forecasting error correction side according to claim 1 corrected simultaneously based on areal rainfall and model parameter
Method, which is characterized in that the Errors of target basin A are corrected in the step (5), are included the following steps:
In (1) model parameter series θ for needing to be corrected, each model parameterIn remaining model parameterIncrease by 1 unit on the basis of constant, obtains new model parameter series θiIt indicates；
(2) with new model parameter series θiDischarge process is obtained after calculating by model；
(3) it is subtracted with the discharge process that step (2) is calculated obtained by the discharge process being calculated with master mould parameter series θ
The graph arrived, as model parameterSystem response curve, be expressed as Vi(t), each column in V matrix use same sample prescription
Method acquires；
(4) by formula Q (P, θ, t)=Q (P, θC, t)+V △ θ+ε obtain model parameter correction amount calculating formula be △ θ=(VTV)-1VT
(Q(P,θ,t)-Q(P,θC, t)), wherein Q (P, θ, t) is measured discharge process, Q (P, θC, t) and it is Xinanjiang model forecasting runoff
Process, P are effective surface mean rainfall series, θCFor calibration optimal model parameters, t is the time, and V is system response curve, and △ θ is
The optimal model parameters correction amount to be solved, ε are calibration optimal model parameters random error, Q (P, θ, t)-Q (P, θC, t) be
Model error Eθ, then revised model parameter series is θ 'C=θC+△θ；
(5) by revised model parameter series Rc' calculated with Xinanjiang model again to get the outlet of modified basin is arrived
The calculating discharge process Q " of sectionu。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811586544.5A CN109783884B (en) | 2018-12-25 | 2018-12-25 | Real-time flood forecast error correction method based on simultaneous correction of surface rainfall and model parameters |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811586544.5A CN109783884B (en) | 2018-12-25 | 2018-12-25 | Real-time flood forecast error correction method based on simultaneous correction of surface rainfall and model parameters |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109783884A true CN109783884A (en) | 2019-05-21 |
| CN109783884B CN109783884B (en) | 2022-10-11 |
Family
ID=66498266
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811586544.5A Active CN109783884B (en) | 2018-12-25 | 2018-12-25 | Real-time flood forecast error correction method based on simultaneous correction of surface rainfall and model parameters |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109783884B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111008259A (en) * | 2019-12-06 | 2020-04-14 | 中国水利水电科学研究院 | River basin rainfall similarity searching method |
| CN111027764A (en) * | 2019-12-06 | 2020-04-17 | 中国水利水电科学研究院 | Flood forecasting method suitable for runoff data lack basin based on machine learning |
| CN112990330A (en) * | 2021-03-26 | 2021-06-18 | 国网河北省电力有限公司营销服务中心 | User energy abnormal data detection method and device |
| CN113158394A (en) * | 2020-01-22 | 2021-07-23 | 河海大学 | Basin hydrological model error correction method and device based on evaporation error compensation |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106971034A (en) * | 2017-03-22 | 2017-07-21 | 中国水利水电科学研究院 | It is a kind of based on Ensemble Kalman Filter to Cross Some Region Without Data run-off calculation method |
| CN106991278A (en) * | 2017-03-21 | 2017-07-28 | 武汉大学 | It is a kind of to gather precipitation forecast and the coupling process of real-time flood probability forecast |
| CN107609713A (en) * | 2017-10-03 | 2018-01-19 | 中国水利水电科学研究院 | A kind of Diabatic slow wave Real-time Forecasting Method corrected by rainfall and the double key elements of runoff |
| CN108491974A (en) * | 2018-03-23 | 2018-09-04 | 河海大学 | A kind of Flood Forecasting Method based on Ensemble Kalman Filter |
-
2018
- 2018-12-25 CN CN201811586544.5A patent/CN109783884B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106991278A (en) * | 2017-03-21 | 2017-07-28 | 武汉大学 | It is a kind of to gather precipitation forecast and the coupling process of real-time flood probability forecast |
| CN106971034A (en) * | 2017-03-22 | 2017-07-21 | 中国水利水电科学研究院 | It is a kind of based on Ensemble Kalman Filter to Cross Some Region Without Data run-off calculation method |
| CN107609713A (en) * | 2017-10-03 | 2018-01-19 | 中国水利水电科学研究院 | A kind of Diabatic slow wave Real-time Forecasting Method corrected by rainfall and the double key elements of runoff |
| CN108491974A (en) * | 2018-03-23 | 2018-09-04 | 河海大学 | A kind of Flood Forecasting Method based on Ensemble Kalman Filter |
Non-Patent Citations (1)
| Title |
|---|
| 司伟等: "基于面平均雨量误差修正的实时洪水预报修正方法", 《湖泊科学》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111008259A (en) * | 2019-12-06 | 2020-04-14 | 中国水利水电科学研究院 | River basin rainfall similarity searching method |
| CN111027764A (en) * | 2019-12-06 | 2020-04-17 | 中国水利水电科学研究院 | Flood forecasting method suitable for runoff data lack basin based on machine learning |
| CN111008259B (en) * | 2019-12-06 | 2020-08-11 | 中国水利水电科学研究院 | River basin rainfall similarity searching method |
| CN113158394A (en) * | 2020-01-22 | 2021-07-23 | 河海大学 | Basin hydrological model error correction method and device based on evaporation error compensation |
| CN113158394B (en) * | 2020-01-22 | 2022-08-19 | 河海大学 | Basin hydrological model error correction method and device based on evaporation error compensation |
| CN112990330A (en) * | 2021-03-26 | 2021-06-18 | 国网河北省电力有限公司营销服务中心 | User energy abnormal data detection method and device |
| CN112990330B (en) * | 2021-03-26 | 2022-09-20 | 国网河北省电力有限公司营销服务中心 | User energy abnormal data detection method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109783884B (en) | 2022-10-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109783884A (en) | The Real-time Flood Forecasting error correcting method corrected simultaneously based on areal rainfall and model parameter | |
| CN105512767B (en) | A kind of Flood Forecasting Method of more leading times | |
| CN107730151B (en) | Basin design flood calculation method based on conceptual hydrological model | |
| CN106168991B (en) | A kind of tidal river network tide prediction method based on hydrodynamic simulation | |
| CN107609335B (en) | A kind of Flood Forecasting Method based on resultant flow and form fit | |
| CN107423546A (en) | Multivariable hydrological uncertainty processing method based on Copula functions | |
| CN109635435A (en) | A kind of natural river course stage discharge relation based on bayesian theory determines method | |
| CN107679021B (en) | River lake-entering reservoir flow calculation method | |
| CN105260607A (en) | Serial connection and parallel connection coupling multi-model hydrological forecasting method | |
| CN106021854A (en) | A natural river course cross section stage-discharge relation determining method | |
| CN106951674B (en) | A kind of no historical summary Flood of small drainage area return period calculation method | |
| CN106971013A (en) | Mountain flood minor watershed Storm and flood calculation method | |
| CN107045568B (en) | River course roughness inversion method based on dynamic programming successive approximation method | |
| CN107085658B (en) | Method for determining disaster time of mountain torrent disasters | |
| CN108256780A (en) | A kind of step reservoir Flood Control Dispatch computational methods based on iteration reservoir area backwater calculation | |
| CN108664453B (en) | Method for calculating suspended load sand transportation amount of river lacking hydrologic data | |
| CN114548511A (en) | Prediction method for daily average warehousing flow of low-head hydropower station group downstream reservoir | |
| CN108491974B (en) | Flood forecasting method based on ensemble Kalman filtering | |
| CN114819322A (en) | Method for forecasting lake inflow flow of lake | |
| CN116681180B (en) | Lake water level similarity forecasting method based on two-layer similarity measurement | |
| CN109145396B (en) | River course roughness zoning calibration method based on vegetation distribution | |
| CN114969655A (en) | Simulation estimation method for sediment transport amount of drainage basin | |
| CN114048432A (en) | Ecological flow calculation method for area without actual measurement runoff data | |
| CN108896473B (en) | River basin erosion sand yield prediction method coupled with different space-time scale models | |
| CN111815043B (en) | Flood flow prediction method and system based on storm characteristics |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |