CN110110339A - A kind of hydrologic forecast error calibration method and system a few days ago - Google Patents
A kind of hydrologic forecast error calibration method and system a few days ago Download PDFInfo
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- CN110110339A CN110110339A CN201810085197.1A CN201810085197A CN110110339A CN 110110339 A CN110110339 A CN 110110339A CN 201810085197 A CN201810085197 A CN 201810085197A CN 110110339 A CN110110339 A CN 110110339A
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
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Abstract
The present invention relates to a kind of hydrologic forecast error calibration method and systems a few days ago, including calculate hydrologic forecast Process Forecasting value and prediction error matrix percentile section；K mean cluster is carried out to the predicted value and prediction error matrix percentile section；Establish the typical categories regression model of predicted value and prediction error matrix percentile section after K mean cluster；Establish the predicted value calibration model of typical categories regression model prediction error estimation.The present invention decomposes error dynamics process, is recognized, is positioned, then carried out characteristic matching in practical applications using meter and error time sequence mode decomposition and the error character discrimination method a few days ago of error character classification, and it is horizontal to improve hydrologic forecast a few days ago.
Description
Technical field
The present invention relates to the bearing calibrations and system of a kind of renewable energy power generation technology, and in particular to a kind of hydrology a few days ago
Prediction error correction method and system.
Background technique
The error of hydrologic forecast has certain uncertainty, and especially in hydrologic forecast a few days ago, used process is driven
Movable model method or data-driven model method can not effectively evade the deviation uncertain problem of traffic forecast a few days ago.And it is sharp
The flood peak information obtained with the real-time discharge site in basin, and the correction of the reservoir inflow, crest discharge carried out based on this, this
One mode depends on the upstream time of origin and crest discharge of flood peak, and that there are leading times is shorter, relies on the offices such as Watershed Runoff characteristic
Limit.For hydrologic forecast longer for leading time, the measured data being able to use is relatively fewer, preferably more in own sequence spy
Correcting scheme is found in sign and history forecast rule.
For application, how the risk in terms of extension leading time certainly will bring error robustness, information reliability, mention
The error rejection ability of high hydrologic forecast a few days ago then determines the practical level of forecasting model to a certain extent.In order to solve this
One problem generallys use different classes of real-time correction of error technology, intervenes the original forecast result of model output,
In, relatively conventional mode is the analysis of the time series based on error itself, for example, by using autoregression model or BP nerve net
The means such as network realize error correction, and this mode, which is disadvantageous in that, lacks effective error genetic analysis, calibration model
It is difficult to cover the influence factor for leading to all kinds of error characteristics in the input factor, thus is easy to cause that calibration result is bad, universality
The problems such as poor.
Secondly, the typically no each configuration that different error characteristics are regarded as to entire error dynamics process of the prior art,
Therefore it is easy to appear the same processing strategie of the different characteristics such as Systematic Errors, nonlinearity erron in terms of error correction, causes
The ability that error inhibits reduces, and weakens the overall performance of hydrologic forecast model.In hydrologic forecast a few days ago, since leading time drops
There may be biggish evaluated errors for water.
Summary of the invention
To solve typically no each group that different error characteristics are regarded as to entire error dynamics process in the prior art
State, therefore it is easy to appear in terms of error correction the same processing strategie of the different characteristics such as Systematic Errors, nonlinearity erron, it leads
The problem of ability for causing error to inhibit reduces, and weakens the overall performance of hydrologic forecast model, the object of the present invention is to provide one
It is longer and in longer leading time to be suitable for more traditional hydrologic forecast leading time for kind a few days ago hydrologic forecast error calibration method and system
The middle hydrologic forecast error correction for keeping higher forecast horizontal.
The purpose of the present invention is adopt the following technical solutions realization:
The present invention provides a kind of hydrologic forecast error calibration method a few days ago, thes improvement is that:
Obtain hydrologic forecast data；
It is corrected based on hydrologic forecast data of the calibration model pre-established to the acquisition；Wherein,
The calibration model pre-established includes: the percentile area based on history hydrologic forecast value Yu prediction error matrix
Between K mean cluster generate.
Further: the acquisition hydrologic forecast data include obtaining history hydrologic forecast data and history hydrology actual measurement number
According to.
Further: the calibration model pre-established includes:
Hydrologic forecast Process Forecasting value is calculated according to history hydrologic forecast data and history hydrology measured data and forecast misses
Poor matrix percentile section；
K mean cluster is carried out to the hydrologic forecast Process Forecasting value and prediction error matrix percentile section；
Establish the typical categories regression model of predicted value and prediction error matrix percentile section after K mean cluster；
Calibration model is formulated according to the typical categories regression model.
Further: it is pre- to calculate the hydrology in the history hydrologic forecast data according to acquisition and history hydrology measured data
It further include construction predicted value and prediction error matrix before report value and prediction error matrix percentile section；
The construction hydrology forecasting process predicted value and prediction error matrix, comprising:
Determine that hydrologic forecast Process Forecasting value and forecast are missed according to history hydrologic forecast data and history hydrology measured data
Difference；
According to hydrologic forecast Process Forecasting value and prediction error construction predicted value and prediction error matrix；
Hydrologic forecast Process Forecasting value P' is carried out according to Percentiles predicted value and prediction error matrixijBy stages collects.
Further: the hydrologic forecast Process Forecasting value and prediction error such as following formula:
Eij=Pij-P'ij；
Wherein, EijFor prediction error, PijFor measured value, P'ijFor hydrologic forecast Process Forecasting value, i is 1≤i≤setting threshold
Value, the temporal resolution between the sample of given threshold=24 hour/day by day, j is 1≤j≤n；N is the forecast number of days for being included in analysis.
Further: the hydrologic forecast Process Forecasting value is expressed as with prediction error matrix:
A=(P 'ij,Eij)
Wherein: X is predicted value and prediction error matrix, Ai1、Ai2...AijIndicate hydrologic forecast Process Forecasting value and pre-
Report the i-th row the 1st column, the i-th row the 2nd the i-th row jth column element of column ... of error matrix.
Further: described to carry out hydrologic forecast Process Forecasting value point according to Percentiles predicted value and prediction error matrix
Section, which collects, includes:
With hydrologic forecast Process Forecasting value P'ijPremised on ascending order, P' is carried out according to PercentilesijPercentile by stages is returned
Collection, P'ijPercentile by stages collects expression are as follows: X (Tt, Eij) indicate to work as P'ijSize is between the hydrologic forecast process randomly selected
Corresponding error sample when the Tt of predicted value section, the hydrologic forecast Process Forecasting value section is with P'ijEach percentile section
The classification of error sample is carried out for condition,T=[1,100,1].
Further: the K mean cluster in the predicted value and prediction error matrix percentile section indicates are as follows:
Wherein: f (Pk) indicate hydrologic forecast Process Forecasting value and prediction error matrix percentile section K mean cluster；Pk
To make f (Pk) it is the smallest one division, Pk={ C1..., Ck}；Indicate the center of n-th of cluster, n=
1 ..., k, d (xy,mn) indicate xyThe m arrivednDistance；xyFor X (Tt, Eij) in element；mnIndicate the center of n-th of cluster；Tt
Indicate the hydrologic forecast Process Forecasting value section randomly selected.
Further: the typical categories regression model indicates are as follows:
Eij=aP'ij+b
Wherein: a, b are preset regression coefficient, P'ijFor hydrologic forecast Process Forecasting value；Eij indicates prediction error.
Further: it is described to be corrected based on hydrologic forecast of the calibration model pre-established to the acquisition, including
It is corrected as the following formula:
P't=f (P't)
Wherein: the P' on the left sidetIndicate the particular forecast value in hydrologic forecast Process Forecasting value section；f(P't) indicate to meet K
The corrected value for the typical forecast sequential track subset that mean cluster provides.
The present invention also provides a kind of hydrologic forecast error correcting systems a few days ago, the improvement is that: including:
Module is obtained, for obtaining hydrologic forecast data；
Correction module, for being corrected based on hydrologic forecast data of the calibration model pre-established to the acquisition；
Wherein,
The calibration model pre-established includes: the percentile area based on history hydrologic forecast value Yu prediction error matrix
Between K mean cluster generate.
Further: further including formulating module, for pre-establishing calibration model.
Further: the formulation module includes:
Computational submodule, for calculating hydrologic forecast process according to history hydrologic forecast data and history hydrology measured data
Predicted value and prediction error matrix percentile section；
Submodule is clustered, for carrying out K to the hydrologic forecast Process Forecasting value and prediction error matrix percentile section
Mean cluster；
First setting up submodule, for establishing the allusion quotation of predicted value and prediction error matrix percentile section after K mean cluster
Type classification regression model；
Second setting up submodule, for formulating calibration model according to the typical categories regression model.
It further, further include construction submodule, for calculating hydrologic forecast mistake according to the hydrologic forecast of acquisition described
Before journey predicted value and prediction error matrix percentile section, construction hydrology forecasting process predicted value and prediction error matrix.
Further: the construction submodule, comprising:
First determination unit, for determining hydrologic forecast mistake according to history hydrologic forecast data and history hydrology measured data
Journey predicted value and prediction error；
First establishing unit, for value according to weather report and prediction error construction predicted value and prediction error matrix；
Unit is collected, for carrying out hydrologic forecast Process Forecasting value according to Percentiles predicted value and prediction error matrix
P'ijBy stages collects.
Further: the correction module is also used to pre- based on the hydrology of the calibration model pre-established to the acquisition
Report is corrected, including is corrected as the following formula:
P't=f (P't)
Wherein: the P' on the left sidetIndicate the particular forecast value in hydrologic forecast Process Forecasting value section；f(P't) indicate to meet K
The corrected value for the typical forecast sequential track subset that mean cluster provides.
Compared with the immediate prior art, technical solution provided by the invention is had the beneficial effect that
A kind of hydrologic forecast error calibration method a few days ago proposed by the present invention, obtains hydrologic forecast data；Based on preparatory system
Fixed calibration model is corrected the hydrologic forecast data of the acquisition；The calibration model pre-established includes: to be based on
The percentile section K mean cluster of history hydrologic forecast value and prediction error matrix solves typically no in the prior art incite somebody to action
Different error characteristics are regarded as each configuration of entire error dynamics process, therefore systematicness is easy to appear in terms of error correction
The same processing strategie of the different characteristics such as error, nonlinearity erron, the ability for causing error to inhibit reduce, and weaken hydrologic forecast
The problem of overall performance of model, the present invention decompose error dynamics process, are distinguished to error character discrimination method a few days ago
Know, positioning, then carry out characteristic matching in practical applications, it is horizontal to improve hydrologic forecast a few days ago.
On the basis of traditional hydrologic forecasting method, examined for the sequential track of the error character of hydrologic forecast a few days ago
Disconnected research, the error statistics correction for being different from "black box" formula and the machine learning algorithm for precision optimizing etc., using meter and accidentally
Poor time series mode decomposition and the error character discrimination method a few days ago of error character classification, by being carried out to error dynamics process
It decomposes, identification, positioning, then carries out characteristic matching in practical applications, to realize that hydrologic forecast error reduces purpose a few days ago.
Detailed description of the invention
Fig. 1 is the error calibration method process of the hydrologic forecast a few days ago simple frame provided by the invention based on temporal aspect cluster
Figure；
Fig. 2 is the hydrologic forecast a few days ago error calibration method process detailed frame provided by the invention based on temporal aspect cluster
Figure；
Fig. 3 is the hydrologic forecast a few days ago error correcting system structural block diagram provided by the invention based on temporal aspect cluster.
Specific embodiment
Specific embodiments of the present invention will be described in further detail with reference to the accompanying drawing.
The following description and drawings fully show specific embodiments of the present invention, to enable those skilled in the art to
Practice them.Other embodiments may include structure, logic, it is electrical, process and other change.Embodiment
Only represent possible variation.Unless explicitly requested, otherwise individual component and function are optional, and the sequence operated can be with
Variation.The part of some embodiments and feature can be included in or replace part and the feature of other embodiments.This hair
The range of bright embodiment includes equivalent obtained by the entire scope of claims and all of claims
Object.Herein, these embodiments of the invention can individually or generally be indicated that this is only with term " invention "
For convenience, and if in fact disclosing the invention more than one, the range for being not meant to automatically limit the application is to appoint
What single invention or inventive concept.
Embodiment one,
The present invention propose one kind premised on error sequential track signature analysis, meter and error time sequence mode decomposition and
The error character discrimination method a few days ago of error character classification, to realize the error correction of hydrologic forecast a few days ago.The present invention provides
It is a kind of based on temporal aspect cluster the error calibration method of hydrologic forecast a few days ago flow diagram, as illustrated in fig. 1 and 2, the party
Method sequentially includes the following steps:
Step 11 obtains hydrologic forecast data；
Step 12 is corrected the acquisition hydrologic forecast data based on the calibration model pre-established；Wherein,
The calibration model pre-established includes: the percentile area based on history hydrologic forecast value Yu prediction error matrix
Between K mean cluster generate.
It includes obtaining history hydrologic forecast data and history hydrology actual measurement number that hydrologic forecast data are obtained in the step 11
According to.
The calibration model pre-established in the step 12 is corrected the hydrologic forecast data of the acquisition
1. hydrologic forecast timing process data collects, construction predicted value and prediction error matrix, CALCULATING PREDICTION value percentile
Section.
2. predicted value and prediction error matrix K mean cluster.
3. the typical categories regression model of predicted value and prediction error matrix is established.
4. the predicted value calibration model based on prediction error estimation.
Particular content is as follows:
Accurately to illustrate implementation method and step of the invention, following example is provided:
(1) hydrologic forecast process data collects
The leading time of usual hydrologic forecast is the concentration time accumulation that statistical data provides, the case where rainfall is forecast in application
Under, the leading time of hydrologic forecast can achieve a few days ago or more.
This example constitutes forecast time series, definition forecast using hydrologic forecast process and its corresponding actual measurement reservoir inflow
Value is P'ijIt is P with measured valueij, prediction error Eij, Eij=Pij-P'ij
Wherein, 1≤i≤96,1≤j≤n
N is the forecast number of days for being included in analysis, that is, the number of days for being included in the sample of analysis is n, and the time between sample point day by day
Resolution is 15min, total sample number N=n96
Wherein: EijFor prediction error, PijFor measured value, P'ijFor hydrologic forecast Process Forecasting value, i is 1≤i≤setting threshold
Value, the temporal resolution between the sample of given threshold=24 hour/day by day, j is 1≤j≤n；N is the forecast number of days for being included in analysis,
Total sample number N=n given threshold.
Firstly, the historical sample that sum is N is carried out purpose data classifying.
Predicted value and prediction error form matrix, are defined as X (P'ij, Eij)
With P'ijPremised on ascending order, P' is carried out according to PercentilesijBy stages collects
Therefore, by X (P'ij, Eij) it is further rewritten as X (Tt, Eij),T=[1,100,1]
(2)X(Tt, Eij) K mean cluster
With the section T randomly selectedtRemaining sample is included into similarity most senior middle school as starting central point by middle K sample
Cluster where heart point, then establishing the mean value of sample coordinate in current cluster is new central point, circuits sequentially that iteration continues, until institute
There is sample generic no longer to change.
Original data set X (T as a result,t, Eij) by the use of K mean cluster method, it can be by X (Tt, Eij) it is regarded as band
Cluster data collection and a potential each cluster for meeting integer K.Output is one of to divide Pk={ C1..., Ck, if xy
It is X (Tt, Eij) in element, then the problem of K mean cluster described in this example is to find such to divide Pk=
{C1..., Ck, so that objective functionIt is minimum.
Wherein,Indicate the center of n-th of cluster, n=1 ..., k, d (xy, mn) indicate xyThe m arrivednDistance.
(3)X(P'ij, Eij) typical categories regression model establish
In section TtIn, X (Tt, Eij) by the application of K mean cluster algorithm, it is divided into each cluster similar in plant characteristic,
Object is different between different clusters.
By clustering algorithm, the original predicted value of characteristic close and some characteristic feature of prediction error value matrix are obtained
Collection establishes regression equation for the subset of this typical forecast sequential track:
Eij=aP'ij+ b formula (1.1)
Using least square method, linear relationship between the two is solved, P' is providedijMeet section TtAnd characteristic feature subset
Under conditions of a, b value, that is, provide PijWith EijMeet section TtIt is given there are one and under conditions of characteristic feature subset
, typical relation based on historical statistics.
(4) it is based on EijThe P' of estimationijCorrection value:
Applying equation (1.1), section TtAnd in characteristic feature subset, corresponding EijIt is by measured value in history sequential track
PijIt is P' with predicted valueijDifference be equivalent to the estimated value in linear relationship.
Therefore, in section TtAnd in characteristic feature subset, PijIt can be regarded as P'ijFunction, that is, correspond to section, typical special
Subset is levied, there are determining Pij=f (P'ij)。
When the following a certain moment, hydrologic forecast model provides specific predicted value P't
If P'tIt is subordinated to TtIn a certain specific sections, and P'tIn single is forecast a few days ago, affiliated section and shown
Error statistics, meet the typical forecast sequential track subset that K mean cluster provides, then
P't=f (P't) formula (1.2)
Formula 1.2 is the error correction model that this example provides.
Embodiment two,
Based on same inventive concept, the present invention also provides the hydrologic forecast error corrections a few days ago clustered based on temporal aspect
System, structure chart are as shown in Figure 3, comprising:
Module 201 is obtained, for obtaining hydrologic forecast data；
Correction module 202, for carrying out school based on hydrologic forecast data of the calibration model pre-established to the acquisition
Just；Wherein,
The calibration model pre-established includes: based on history hydrologic forecast Process Forecasting value and prediction error matrix
Percentile section K mean cluster generates.
Further: further including formulating module, for pre-establishing calibration model.
Further: the formulation module includes:
Computational submodule, for calculating hydrologic forecast process according to history hydrologic forecast data and history hydrology measured data
Predicted value and prediction error matrix percentile section；
Submodule is clustered, for carrying out K to the hydrologic forecast Process Forecasting value and prediction error matrix percentile section
Mean cluster；
First setting up submodule, for establishing the allusion quotation of predicted value and prediction error matrix percentile section after K mean cluster
Type classification regression model；
Second setting up submodule, for formulating calibration model according to the typical categories regression model.
It further, further include construction submodule, for calculating hydrologic forecast value according to the hydrologic forecast of acquisition described
Before prediction error matrix percentile section, construction hydrology forecasting process predicted value and prediction error matrix.
Further: the construction submodule, comprising:
First determination unit, for determining hydrologic forecast mistake according to history hydrologic forecast data and history hydrology measured data
Journey predicted value and prediction error；
First establishing unit, for value according to weather report and prediction error construction predicted value and prediction error matrix；
Unit is collected, for carrying out hydrologic forecast Process Forecasting value according to Percentiles predicted value and prediction error matrix
P'ijBy stages collects.
Further: the correction module is also used to pre- based on the hydrology of the calibration model pre-established to the acquisition
Report is corrected, including is corrected as the following formula:
P't=f (P't)
Wherein: the P' on the left sidetIndicate the particular forecast value in hydrologic forecast Process Forecasting value section；f(P't) indicate to meet K
The corrected value for the typical forecast sequential track subset that mean cluster provides.
Present invention application K mean cluster carries out characteristic feature subset division to predicted value and prediction error value matrix, passes through
The screening of the affiliated numerical intervals of predicted value and corresponding character subset is realized using step of the present invention and the formula equation provided
Predicted value correction.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
The above embodiments are merely illustrative of the technical scheme of the present invention and are not intended to be limiting thereof, although referring to above-described embodiment pair
The present invention is described in detail, those of ordinary skill in the art still can to a specific embodiment of the invention into
Row modification perhaps equivalent replacement these without departing from any modification of spirit and scope of the invention or equivalent replacement, applying
Within pending claims of the invention.
Claims (17)
1. a kind of hydrologic forecast error calibration method a few days ago, it is characterised in that:
Obtain hydrologic forecast data；
The acquisition hydrologic forecast data are corrected based on the calibration model pre-established；
Wherein, the calibration model pre-established includes: the percentile based on history hydrologic forecast value Yu prediction error matrix
Section K mean cluster generates.
2. hydrologic forecast error calibration method a few days ago as described in claim 1, it is characterised in that: the correction pre-established
The building process of model includes:
According to history hydrologic forecast data and history hydrology measured data, hydrologic forecast Process Forecasting value and prediction error square are calculated
Battle array percentile section；
K mean cluster is carried out to the hydrologic forecast Process Forecasting value and prediction error matrix percentile section；
Establish the typical categories regression model of predicted value and prediction error matrix percentile section after K mean cluster；
Calibration model is formulated according to the typical categories regression model.
3. hydrologic forecast error calibration method a few days ago as claimed in claim 2, it is characterised in that: in the going through according to acquisition
History hydrologic forecast data and history hydrology measured data calculate hydrologic forecast Process Forecasting value and prediction error matrix percentile area
Between before, further includes:
According to history hydrologic forecast data and history hydrology measured data, hydrologic forecast Process Forecasting value and prediction error are determined；
Value and prediction error construction predicted value and prediction error matrix according to weather report；
Hydrologic forecast Process Forecasting value by stages is carried out with prediction error matrix according to Percentiles predicted value to collect.
4. hydrologic forecast error calibration method a few days ago as claimed in claim 3, it is characterised in that: the hydrologic forecast error meter
Formula is as follows:
Eij=Pij-P'ij；
Wherein, EijFor prediction error, PijFor measured value, P'ijFor hydrologic forecast Process Forecasting value, i is 1≤i≤given threshold,
Temporal resolution between the sample of given threshold=24 hour/day by day, j are 1≤j≤n；N is the forecast number of days for being included in analysis.
5. hydrologic forecast error calibration method a few days ago as claimed in claim 4, it is characterised in that: the hydrologic forecast process is pre-
Report value is expressed as with prediction error matrix:
A=(Pij′,Eij)
Wherein: X is predicted value and prediction error matrix, Ai1、Ai2...AijIndicate that hydrologic forecast Process Forecasting value and forecast are missed
The i-th row the 1st column, the i-th row the 2nd the i-th row jth column element of column ... of poor matrix.
6. hydrologic forecast error calibration method a few days ago as claimed in claim 5, it is characterised in that: described pre- according to Percentiles
Report value is collected with prediction error matrix progress hydrologic forecast Process Forecasting value by stages
With hydrologic forecast Process Forecasting value P'ijPremised on ascending order, P' is carried out according to PercentilesijPercentile by stages collects,
P'ijPercentile by stages collects expression are as follows: X (Tt, Eij) indicate to work as P'ijSize is pre- between the hydrologic forecast process randomly selected
Corresponding error sample when report value section Tt, the hydrologic forecast Process Forecasting value section is with P'ijEach percentile section be
Condition carries out the classification of error sample,T=[1,100,1].
7. hydrologic forecast error calibration method a few days ago as claimed in claim 2, it is characterised in that: the hydrologic forecast process is pre-
Report value and the K mean cluster in prediction error matrix percentile section are generated and are indicated are as follows:
Wherein: f (Pk) indicate hydrologic forecast Process Forecasting value and prediction error matrix percentile section K mean cluster；PkTo make
f(Pk) it is the smallest one division, Pk={ C1..., Ck}；Indicate the center of n-th of cluster, n=1 ..., k,
d(xy,mn) indicate xyThe m arrivednDistance；xyFor X (Tt, Eij) in element；mnIndicate the center of n-th of cluster；TtIndicate with
The hydrologic forecast Process Forecasting value section that machine is chosen.
8. hydrologic forecast error calibration method a few days ago as claimed in claim 2, it is characterised in that: the typical categories return mould
Type indicates are as follows:
Eij=aP'ij+b
Wherein: a, b are preset regression coefficient, P'ijFor hydrologic forecast Process Forecasting value；EijMould is returned for typical categories
Type indicates prediction error.
9. hydrologic forecast error calibration method a few days ago as claimed in claim 2, it is characterised in that: described according to the typical class
Other regression model formulates calibration model, comprising:
Determine the calibration model between typical categories regression model hydrologic forecast Process Forecasting value and hydrologic forecast process measured value.
10. hydrologic forecast error calibration method a few days ago as claimed in claim 9, it is characterised in that: determine hydrologic forecast process
Calibration model between predicted value and hydrologic forecast process measured value is indicated with following formula:
Pij=f (P'ij)
Wherein: P'ijFor hydrologic forecast Process Forecasting value, PijFor hydrologic forecast process measured value, f (P'ij) it is with hydrologic forecast mistake
Journey predicted value is the function of independent variable.
11. hydrologic forecast error calibration method a few days ago as claimed in claim 10, it is characterised in that: described to be based on pre-establishing
Calibration model the hydrologic forecast of the acquisition is corrected, comprising: when hydrologic forecast Process Forecasting value be particular forecast value
When, it is corrected as the following formula:
P't=f (P't)
Wherein: the P' on the left sidetIndicate the correction result of hydrologic forecast process particular forecast value；f(P't) indicate with hydrologic forecast mistake
Journey particular forecast value is the function of independent variable.
12. a kind of hydrologic forecast error correcting system a few days ago, it is characterised in that: include:
Module is obtained, for obtaining hydrologic forecast data；
Correction module, for being corrected based on hydrologic forecast data of the calibration model pre-established to the acquisition；Wherein,
The calibration model pre-established includes: the percentile section K based on history hydrologic forecast value Yu prediction error matrix
Mean cluster generates.
13. hydrologic forecast error correcting system a few days ago as claimed in claim 12, it is characterised in that: it further include formulating module,
For pre-establishing calibration model.
14. hydrologic forecast error correcting system a few days ago as claimed in claim 13, it is characterised in that: the formulation module packet
It includes:
Computational submodule, for calculating hydrologic forecast Process Forecasting according to history hydrologic forecast data and history hydrology measured data
Value and prediction error matrix percentile section；
Submodule is clustered, for carrying out K mean value to the hydrologic forecast Process Forecasting value and prediction error matrix percentile section
Cluster；
First setting up submodule, for establishing the typical class of predicted value and prediction error matrix percentile section after K mean cluster
Other regression model；
Second setting up submodule, for formulating calibration model according to the typical categories regression model.
15. hydrologic forecast error correcting system a few days ago as claimed in claim 12, which is characterized in that further include construction submodule
Block, for calculating hydrologic forecast Process Forecasting value and prediction error matrix percentile section according to the hydrologic forecast of acquisition described
Before, construction hydrology forecasting process predicted value and prediction error matrix.
16. hydrologic forecast error calibration method a few days ago as claimed in claim 15, it is characterised in that: the construction submodule,
Include:
First determination unit, for determining that hydrologic forecast process is pre- according to history hydrologic forecast data and history hydrology measured data
Report value and prediction error；
First establishing unit, for according to hydrologic forecast Process Forecasting value and prediction error construction hydrology forecasting process predicted value with
Prediction error matrix；
Unit is collected, for carrying out hydrologic forecast Process Forecasting value P' according to Percentiles predicted value and prediction error matrixijPoint
Section collects.
17. hydrologic forecast error correcting system a few days ago as claimed in claim 12, it is characterised in that: the correction module, also
For being corrected as the following formula when hydrologic forecast Process Forecasting value is particular forecast value:
P't=f (P't)
Wherein: the P' on the left sidetIndicate the correction result of hydrologic forecast process particular forecast value；f(P't) indicate with hydrologic forecast mistake
Journey particular forecast value is the function of independent variable.
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| 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 |
| WO2021218457A1 (en) * | 2020-04-28 | 2021-11-04 | 中国长江三峡集团有限公司 | Method for performing runoff forecast under influence of upstream reservoir group by using forecast errors |
| WO2022104709A1 (en) * | 2020-11-19 | 2022-05-27 | 中山大学 | Monthly-scale rainfall forecast correction method coupled with gamma and gaussian distribution |
| CN116595945A (en) * | 2023-07-14 | 2023-08-15 | 浙江大华技术股份有限公司 | High-precision simulation scattering parameter extraction method, electronic equipment and storage medium |
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| CN106650767A (en) * | 2016-09-20 | 2017-05-10 | 河海大学 | Flood forecasting method based on cluster analysis and real time correction |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| WO2021218457A1 (en) * | 2020-04-28 | 2021-11-04 | 中国长江三峡集团有限公司 | Method for performing runoff forecast under influence of upstream reservoir group by using forecast errors |
| GB2599862A (en) * | 2020-04-28 | 2022-04-13 | China Three Gorges Corp | Method for performing runoff forecast under influence of upstream reservoir group by using forecast errors |
| GB2599862B (en) * | 2020-04-28 | 2022-09-07 | China Three Gorges Corp | Method for forecasting runoff under influence of upstream reservoir group by utilizing forecasting errors |
| WO2022104709A1 (en) * | 2020-11-19 | 2022-05-27 | 中山大学 | Monthly-scale rainfall forecast correction method coupled with gamma and gaussian distribution |
| CN116595945A (en) * | 2023-07-14 | 2023-08-15 | 浙江大华技术股份有限公司 | High-precision simulation scattering parameter extraction method, electronic equipment and storage medium |
| CN116595945B (en) * | 2023-07-14 | 2023-10-20 | 浙江大华技术股份有限公司 | High-precision simulation scattering parameter extraction method, electronic equipment and storage medium |
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