CN110186533B - High-precision river mouth short-term tide level forecasting method - Google Patents

High-precision river mouth short-term tide level forecasting method Download PDF

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CN110186533B
CN110186533B CN201910461891.3A CN201910461891A CN110186533B CN 110186533 B CN110186533 B CN 110186533B CN 201910461891 A CN201910461891 A CN 201910461891A CN 110186533 B CN110186533 B CN 110186533B
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陈永平
甘敏
潘毅
刘士诚
谭亚
周子骏
蒲金山
朱弦
林祥峰
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Hohai University HHU
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Abstract

The invention discloses a high-precision short-term forecasting method for estuary tide level, which introduces an autoregressive method on the basis of an unsteady state harmonic analysis method, establishes an autoregressive model for forecasting errors of the unsteady state harmonic analysis method through the autoregressive method, and estimates the forecasting errors of the unsteady state harmonic analysis method forecasting tide level by adopting the autoregressive model, thereby correcting the errors of the forecasting tides and achieving a higher-precision forecasting result of the estuary tide level compared with the traditional method.

Description

High-precision river mouth short-term tide level forecasting method
Technical Field
The invention relates to a high-precision river mouth short-term tide level forecasting method, and belongs to the technical field of river mouth area tide level forecasting.
Background
For the problem of tide level forecast, a harmonic analysis method is usually adopted, and the tide level of a station is forecasted by performing harmonic analysis on the past actually measured tide level data of the tide level station to obtain a harmonic constant of the tide level station. The harmonic analysis method can forecast the sea tide level with high precision, when the tide wave is transmitted to a river channel from the sea, the nonlinear interaction between the upstream runoff and the tide wave exists, and the harmonic analysis method cannot consider the nonlinear interaction between the runoff and the tide wave, so that the harmonic analysis method is not high in precision when applied to the tide level forecast in estuary areas (particularly upstream of tidal river reach). Therefore, how to consider the nonlinear interaction between runoff and tidal waves is an important problem in the forecast of the tidal level of the estuary region. To account for the effects of runoff on tidal waves, a non-steady state harmonic analysis method is proposed. The method considers the correction effect of the mean water level, the tide dividing amplitude and the lag angle in the parameters of the radial flow regulation and analysis method, and improves the precision relative to the regulation and analysis method when forecasting the future tide level.
Although the unsteady state harmonic analysis method can obtain higher precision than harmonic analysis when forecasting the tidal level of the estuary region, the applicability of the unsteady state harmonic analysis method to estuary with large runoff and tidal volume needs to be improved because the unsteady state harmonic analysis method is obtained by linearizing a nonlinear equation, and the precision still needs to be further improved when forecasting the tidal level of the estuary.
Disclosure of Invention
The invention aims to solve the technical problem of providing a short-term estuary tide level forecasting method with higher forecasting precision than an unsteady state harmonic analysis method.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a high-precision river mouth short-term tide level forecasting method is characterized by comprising the following steps:
s1, collecting actual measured runoff data of the runoff station and actual measured tide level data of the analysis station and the reference station within M + M time period before the beginning of the estuary region, calculating a corresponding tide difference sequence according to the collected tide level data of the reference station, wherein the runoff sequence measured or predicted by the runoff station is represented by Q (t), the tide level data sequence of the analysis station is represented by h (t), and the tide difference sequence calculated by the tide level sequence measured or predicted by the tide verification station is represented by R (t); the runoff station is defined as a hydrological station for observing runoff at the upstream of the estuary region, the analysis station is defined as a tide level station needing forecasting at the estuary region, and the reference station is defined as a tide station close to the open sea at the estuary;
s2, collecting runoff station forecast runoff data M hours before and K hours after the start time of the estuary area, wherein the M time period is a long time period, and the M time period is a short time period;
s3, adopting a harmonic analysis method, rolling and forecasting the tide level value in the previous M time interval by taking K hours as a forecasting step length based on the tide level data in the previous M time interval before the forecasting time of the reference station, and calculating a corresponding forecasting tide difference sequence; predicting the tide level value within K hours after the reference station is started and reporting the tide level data of the M time period before the time and calculating the corresponding tide difference;
s4, adopting an unsteady state harmonic analysis method, solving a corresponding unknown coefficient based on the measured tide level data of the analysis station in the previous M time period M time period before the forecast time, the measured runoff station runoff sequence in the same time period and the tide difference sequence of the reference station, combining the runoff station runoff forecast value in the previous M time period in the step S2 and the forecast tide difference sequence of the reference station in the same time period in the step S3, and adopting K hours as a forecast step length to roll and forecast the tide level value in the previous M time period before the start forecast time of the analysis station; analyzing the actual measurement runoff station runoff sequence, the analysis station tidal level sequence and the reference station tidal level sequence based on the M time period before the analysis station start reporting time, solving a corresponding unknown coefficient, and forecasting the tidal level value K hours after the analysis station start reporting time by combining the forecast value K hours after the step S2 and the forecast tidal level of the reference station K hours after the step S3;
s5, calculating the difference value between the forecast tide level value of the analysis station at the previous m time period in S4 and the actually measured tide level data at the time period corresponding to the analysis station in S1, and taking the difference value as a prediction error sequence of the analysis station at the previous m time period;
s6, constructing an autoregressive model for the error sequence calculated in the S5 by adopting an autoregressive method;
s7, predicting a prediction error value of the unsteady state harmonic analysis method within K hours after the start time according to the autoregressive model constructed in S6;
and S8, subtracting the forecast error value predicted in S7 from the tide level value K hours after the start-of-report time calculated in S4 to obtain the final forecast tide level value K hours after the start-of-report time of the analysis station.
Further, in S1, the period of time M is 8785 hours, and the value range of the period of time M is: and m is more than or equal to 360 and less than or equal to 1200, the unit is hour, an autoregressive model is constructed based on the prediction error sequences of different m time periods in the past, and the target value of the minimum prediction mean square error reached when K-hour rolling prediction is carried out is a m determined value. The value of M is not limited to the fixed value, the larger the value of M is, the more the number of partial tides that can be selected during analysis is, but the larger the difference between the calculated parameter and the parameter at the forecasting time is, so that the value of M is selected as a compromise between the time series length and the number of partial tides that can be selected corresponding to the time series length, and 8785 hours is only the preferred value after the comprehensive consideration; and the value of M is a range value obtained based on the value of M and based on experience value, and the specific value of M is determined based on the forecast errors of different values in the range.
Furthermore, the K hour value in S2 is that K is less than or equal to 48.
Further, the expression of the harmonic analysis method tide level is as follows:
Figure BDA0002078291990000031
Figure BDA0002078291990000032
wherein h (t) is the actually measured tide level of the analysis station η0Is the average sea level; t is time; k is the number of the tide separating; n is the number of the partial tides used for harmonic analysis; sigmak、
Figure BDA0002078291990000033
fk、uk、υ0,k、HkAnd gkRespectively the frequency, phase angle, crossing point factor, crossing point correcting angle, astronomical initial phase angle, amplitude and lag angle of the kth tide, wherein the harmonic constant HkAnd gkThe solution of (2) adopts a least square method.
Further, the expression of the unsteady state harmonic analysis method tide level is as follows:
Figure BDA0002078291990000034
wherein c isj(j＝0～2)，
Figure BDA0002078291990000035
Is an unknown coefficient; (p)s,qs,rs) And (p)f,qf,rf) Is an unknown index; qR(t) is a low passA filtered upstream runoff station runoff sequence; r (t) is a reference station tidal range sequence, which can be calculated from the reference station tidal level; t is tQAnd tRRespectively representing the propagation time of the runoff station and the tidal wave of the reference station to the analysis station by respectively calculating QR(t) and maximum cross-correlation coefficient determination of R (t) and h (t); the other parameters are consistent with the formula (2), and the runoff sequence Q of the runoff station is led inR(t), the tidal level sequence h (t) of the analysis station and the tidal level sequence R (t) of the reference station, wherein the unknown parameters in the formula (3) can be calculated by adopting an open source program package NS _ TIDE, after the related parameters are calculated, the forecasted runoff station runoff and the tidal level sequence of the reference station are provided, the tidal level of the analysis station can be forecasted by a non-steady state harmonic analysis method, and the expression of the forecasted tidal level is as follows:
Figure BDA0002078291990000041
Figure BDA0002078291990000042
Figure BDA0002078291990000043
Figure BDA0002078291990000044
Figure BDA0002078291990000045
Figure BDA0002078291990000046
wherein i represents an imaginary number; zkRepresenting the tidal level fluctuation of the kth tide division representation; z is a radical of* kIs zkThe conjugate complex number of (a); | zk(t) | and | z* k(t) | represents z respectivelyk(t) and z* k(t) a modulus; im and Re denote the imaginary and real parts of the complex number, respectively.
Further, the autoregressive model modeling formula in step S6 is:
Figure BDA0002078291990000047
wherein y istA prediction error sequence for the non-stationary harmonic analysis method;tis a white noise disturbance; p is the model order; phi is ajAnd (j is 1 to p) is a model coefficient, wherein the model order p is determined by adopting an AIC criterion (Akaike information criterion), an equation set is constructed for a prediction error time sequence of the unsteady state harmonic analysis method in the past m according to a formula (10), and then a least square method is adopted to solve a correlation coefficient, namely the prediction error of the unsteady state harmonic analysis method at the future moment can be predicted in a rolling mode according to the obtained coefficient.
The invention achieves the following beneficial effects: according to the invention, on the basis of unsteady state harmonic analysis, an autoregressive method is adopted to model the past forecast error of the unsteady state harmonic analysis method, and the forecast error of the unsteady state harmonic analysis method is predicted through the constructed autoregressive model, so that the forecast tide level value of the unsteady state harmonic analysis method is corrected, and the high-precision short-term forecast tide level of the estuary is obtained.
Drawings
FIG. 1 is a flow chart of an embodiment;
FIG. 2 is a site distribution diagram of the Yangtze river mouth in the example;
FIG. 3(a) is a comparison graph of the forecast tide level and the measured tide level at the Jiangyin station 24 h;
FIG. 3(b) is a comparison graph of the forecast tide level and the measured tide level at the Jiangyin station 48 h;
FIG. 4 is a comparison graph of predicted tide level and actual measured tide level at Zhenjiang station 48 h;
FIG. 5 is a comparison of the predicted and measured tide levels at 48h of Nanjing station.
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.
The forecasting example of the Yangtze river mouth river tide level is explained in detail by the invention:
(1) fig. 2 is a site distribution diagram set in this embodiment, and according to the characteristics of the estuary of the Yangtze river, the upstream Datong hydrological station is selected as a runoff station, the Yanglin station is selected as a reference station, and the tide levels of the analysis stations (Jiangyin, Zhenjiang and Nanjing stations) are forecasted.
(2) Collecting runoff data of the runoff station and tide level data of a reference station synchronously observed with an analysis station (Jiangyin, Zhenjiang and Nanjing stations). In the embodiment, the collected data comprises actual measurement tide level and runoff data of Yanglin, Jiangyin, Zhenjiang, Nanjing station and Datong station 2014-2018, and also comprises forecast runoff data of the Datong station 2015-2018. The m value varies from 360 to 1200 in this example with an amplification step of 120, and tables 1-3 show the mean square error of the short-term prediction of 24h and the statistics of the yield for different error margins (+ -10 cm, + -20 cm, + -30 cm) for different m values in the Jiangyin station, Zhenjiang station and Nanjing station. And finally determining m values of the Jiangyin station, the Zhenjiang station and the Nanjing station to be 1080, 840 and 1080 respectively through comparison and selection.
Table 1: statistical table for 24h forecast mean square error and qualification rate of Jiangyin station along with change of m values
Figure BDA0002078291990000061
(Note: RMSE represents mean square error; NS represents unsteady state harmonic analysis method prediction error; NA represents prediction error of the present invention)
Table 2: statistical table for 24h forecast mean square error and qualified rate of Zhenjiang station changing with m value
Figure BDA0002078291990000062
Table 3: statistical table for 24h forecast mean square error and qualified rate of Nanjing station changing with m value
Figure BDA0002078291990000063
(3) According to the flow chart 1, the tide levels of river yin, Zhenjiang and Nanjing station 2015-.
Table 4: prediction mean square error and qualification rate statistics for each analysis station
Figure BDA0002078291990000071
The results show that: the mean square error of the tide level value predicted by the method is obviously smaller than the prediction error of the unsteady state harmonic analysis method, the prediction precision is obviously improved, the qualification rate is improved, and the shorter the prediction time is, the higher the precision is.
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 (6)

1. A high-precision river mouth short-term tide level forecasting method is characterized by comprising the following steps:
s1, collecting actual measured runoff data of the runoff station and actual measured tide level data of the analysis station and the reference station within M + M time period before the start-up time of the estuary region, calculating a corresponding tide difference sequence according to the collected tide level data of the reference station, wherein the runoff sequence measured or predicted by the runoff station is represented by Q (t), the tide level data sequence of the analysis station is represented by h (t), and the tide difference sequence calculated by the tide sequence measured or predicted by the reference station is represented by R (t);
s2, collecting runoff station forecast runoff data M hours before and K hours after the start time of the estuary area, wherein the M time period is a long time period, and the M time period is a short time period;
s3, adopting a harmonic analysis method, rolling and forecasting the tide level value in the previous M time interval by taking K hours as a forecasting step length based on the tide level data in the previous M time interval before the forecasting time of the reference station, and calculating a corresponding forecasting tide difference sequence; predicting the tide level value within K hours after the reference station is started and reporting the tide level data of the M time period before the time and calculating the corresponding tide difference;
s4, adopting an unsteady state harmonic analysis method, solving a corresponding unknown coefficient based on the measured tide level data of the analysis station in the previous M time period M time period before the forecast time, the measured runoff station runoff sequence in the same time period and the tide difference sequence of the reference station, combining the runoff station runoff forecast value in the previous M time period in the step S2 and the forecast tide difference sequence of the reference station in the same time period in the step S3, and adopting K hours as a forecast step length to roll and forecast the tide level value in the previous M time period before the start forecast time of the analysis station; analyzing the actual measurement runoff station runoff sequence, the analysis station tidal level sequence and the reference station tidal level sequence based on the M time period before the analysis station start reporting time, solving a corresponding unknown coefficient, and forecasting the tidal level value K hours after the analysis station start reporting time by combining the forecast value K hours after the step S2 and the forecast tidal level of the reference station K hours after the step S3;
s5, calculating the difference value between the forecast tide level value of the analysis station at the previous m time period in S4 and the actually measured tide level data at the time period corresponding to the analysis station in S1, and taking the difference value as a prediction error sequence of the analysis station at the previous m time period;
s6, constructing an autoregressive model for the error sequence calculated in the S5 by adopting an autoregressive method;
s7, predicting a prediction error value of the unsteady state harmonic analysis method within K hours after the start time according to the autoregressive model constructed in S6;
and S8, subtracting the forecast error value predicted in S7 from the tide level value K hours after the start-of-report time calculated in S4 to obtain the final forecast tide level value K hours after the start-of-report time of the analysis station.
2. The method as claimed in claim 1, wherein in S1, the period M is 8785 hours, and the range M is: and m is more than or equal to 360 and less than or equal to 1200, the unit is hour, an autoregressive model is constructed based on the prediction error sequences of different m time periods in the past, and the target value of the minimum prediction mean square error reached when K-hour rolling prediction is carried out is a m determined value.
3. The method as claimed in claim 1, wherein K is less than or equal to 48 in S2.
4. The method as claimed in claim 1, wherein the harmonic analysis method comprises the following steps:
Figure FDA0002463039070000021
Figure FDA0002463039070000022
wherein h (t) is the actually measured tide level of the analysis station η0Is the average sea level; t is time; k is the number of the tide separating; n is the number of the partial tides used for harmonic analysis; sigmak、
Figure FDA0002463039070000023
fk、uk、υ0,k、HkAnd gkRespectively the frequency, phase angle, crossing point factor, crossing point correcting angle, astronomical initial phase angle, amplitude and lag angle of the kth tide, wherein the harmonic constant HkAnd gkThe solution of (2) adopts a least square method.
5. The method as claimed in claim 1, wherein the expression of the non-steady state harmonic analysis method tide level is as follows:
Figure FDA0002463039070000024
wherein c isj(j＝0～2)，
Figure FDA0002463039070000025
Is an unknown coefficient; (p)s,qs,rs) And (p)f,qf,rf) Is an unknown index; qR(t) is a low-pass filtered upstream flow station flow sequence; r (t) is a reference station tidal range sequence, which can be calculated from the reference station tidal level; tQ and tR represent the propagation time of runoff of the runoff station and tidal waves of the reference station to the analysis station respectively, and Q is calculated respectivelyR(t) and maximum cross-correlation coefficient determination of R (t) and h (t); sigmakThe frequency of the kth tide; runoff sequence Q of import runoff stationR(t), the tidal level sequence h (t) of the analysis station and the tidal level sequence R (t) of the reference station, wherein the unknown parameters in the formula (3) can be calculated by adopting an open source program package NS _ TIDE, after the related parameters are calculated, the forecasted runoff station runoff and the tidal level sequence of the reference station are provided, the tidal level of the analysis station can be forecasted by a non-steady state harmonic analysis method, and the expression of the forecasted tidal level is as follows:
Figure FDA0002463039070000031
Figure FDA0002463039070000032
Figure FDA0002463039070000033
Figure FDA0002463039070000034
Figure FDA0002463039070000035
Figure FDA0002463039070000036
wherein i represents an imaginary number; zkRepresenting the tidal level fluctuation of the kth tide division representation; z is a radical of* kIs zkThe conjugate complex number of (a); | zk(t) | and | z* k(t) | represents z respectivelyk(t) and z* k(t) a modulus; im and Re represent the imaginary part and the real part of the complex number respectively;
Figure FDA0002463039070000037
the phase angle of the kth tide.
6. The method as claimed in claim 1, wherein the autoregressive model in step S6 is modeled by the following formula:
Figure FDA0002463039070000038
wherein yt is a prediction error sequence of the unsteady harmonic analysis method;tis a white noise disturbance; p is the model order; phi is ajAnd (j is 1 to p) is a model coefficient, wherein the model order p is determined by adopting an AIC criterion (Akaike information criterion), an equation set is constructed for a prediction error time sequence of the unsteady state harmonic analysis method in the past m according to a formula (10), and then a least square method is adopted to solve a correlation coefficient, namely the prediction error of the unsteady state harmonic analysis method at the future moment can be predicted in a rolling mode according to the obtained coefficient.
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CN109992748A (en) * 2019-04-09 2019-07-09 河海大学 A kind of river mouth tidal level harmonic analysis method

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CN102867106A (en) * 2012-08-14 2013-01-09 贵州乌江水电开发有限责任公司 Method and system for predicting short-term running water
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CN103617306B (en) * 2013-10-23 2014-08-20 广东省水文局佛山水文分局 River mouth salt tide forecasting method based on tides and runoff
CN105701066A (en) * 2016-03-08 2016-06-22 广东省水利水电科学研究院 Estuary-tidal-range fast prediction method
CN105910587B (en) * 2016-04-11 2018-05-15 中国人民解放军理工大学 A kind of tide prediction method based on tidal parameter inverting
CN106441244B (en) * 2016-09-19 2018-09-18 青岛地球软件技术有限公司 A kind of tide prediction device based on global position system
CN106951693B (en) * 2017-03-06 2019-11-26 武汉大学 River mouth runoff, tidal control section determination method and its application method
CN107577648A (en) * 2017-09-04 2018-01-12 北京京东尚科信息技术有限公司 For handling the method and device of multivariate time series data
CN109376937B (en) * 2018-10-31 2020-06-09 中国水利水电科学研究院 Self-adaptive scheduling end-of-term water level prediction method based on ensemble empirical mode decomposition

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109992748A (en) * 2019-04-09 2019-07-09 河海大学 A kind of river mouth tidal level harmonic analysis method

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