CN106803131A - A kind of river flood forecasting procedure based on discrete generalized Nash Confluence Models - Google Patents
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Abstract
The invention discloses a kind of river flood forecasting procedure based on discrete generalized Nash Confluence Models, by introducing stagnant storage curve, simplify broad sense Nash Confluence Models, and set up discrete broad sense Nash Confluence Models, a kind of river flood forecasting procedure is proposed based on the model, by at the beginning of section out of flow procedure is expressed as the period under river course, period Mo inbound traffics and current time and the linear combination of outflow of some moment before, weight coefficient is drawn according to stagnant storage curve；This river flood forecasting procedure based on discrete generalized Nash Confluence Models that the present invention is provided, due to having incorporated partial history effective information, can improve forecast precision, and simple, intuitive, be easy to the popularization and application in engineering practice.
Description
Technical Field
The invention belongs to the technical field of hydrological forecasting, and particularly relates to a river flood forecasting method based on a discrete generalized Nash confluence model.
Background
The existing river flood calculation in hydrology is based on the Saint-Venn equation set, adopts a generalized analysis method to generalize a continuous equation into a water balance equation and generalize a power equation into a tank storage curve equation; according to different expression forms of the groove storage curve, the method mainly comprises a Maskyoto method, a characteristic river length method and a hysteresis flow algorithm.
In actual work, the actual river reach to be calculated hardly meets the simplified channel storage curve equation of the methods; the prior art adopts a method of segmental continuous calculation, such as a Masjing junction curve, a Jia-Li-Ning unit line and a Nash instantaneous unit line; the method is essentially to analyze the outflow process formed by unit inflow without considering the change condition of the initial water storage capacity of the river channel; the generalized Nash confluence model based on the Nash instantaneous unit line theory includes the water withdrawal process of the initial water storage amount of the river channel, and the prediction precision is improved to a certain extent; however, the generalized Nash confluence formula is complex in form and comprises an integral term, so that the application of the generalized Nash confluence formula in engineering is limited.
Disclosure of Invention
Aiming at the defects or improvement requirements in the prior art, the invention provides a river flood forecasting method based on a discrete generalized Nash confluence model, aiming at simplifying the generalized Nash confluence model by introducing a stagnation curve and establishing the discrete generalized Nash confluence model so as to simplify calculation and improve forecasting precision.
In order to achieve the above object, according to an aspect of the present invention, there is provided a river flood forecasting method based on a discrete generalized Nash confluence model, comprising the following steps:
(1) selecting historical flood according to historical synchronous observation data of inflow and outflow of the river reach to be forecasted;
(2) establishing a discrete generalized Nash confluence model of a river reach to be forecasted; based on historical field flood information, carrying out optimization calibration on parameters n and K of the discrete generalized Nash confluence model by adopting an SCE-UA algorithm;
wherein n refers to the number of the Nash linear reservoirs, and K is a parameter reflecting the storage regulation capacity of the Nash linear reservoirs; (3) forecasting the outlet flow at the (t + delta t) moment by utilizing the discrete generalized Nash confluence model according to the outlet flow at the (n-1) delta t time interval before the current moment and the inlet flow at the [ t, t + delta t ] time interval; wherein, the delta t refers to the calculation time period length;
(3) forecasting the outlet flow at the (t + delta t) moment by utilizing the discrete generalized Nash confluence model according to the outlet flow at the (n-1) delta t time interval before the current moment and the inlet flow at the [ t, t + delta t ] time interval; where Δ t is the calculation period length.
Preferably, in the river flood forecasting method based on the discrete generalized Nash confluence model, the method for establishing the discrete generalized Nash confluence model of the river reach to be forecasted in the step (2) includes the following substeps:
(2.1) establishing a generalized Nash confluence model under the condition that the initial condition is not zero:
wherein: i (t), O (t) are respectively inflow and outflow processes of the river section, O(j)(0) Is the j-order derivative of O (t) at the initial moment, u (-) is a Nash instantaneous unit line, n is the number of Nash linear reservoirs, and K is a parameter reflecting the storage regulation capability of the Nash linear reservoirs;
(2.2) use of the hysteresis curve Rn(t) simplifying the generalized Nash confluence model;
defining stagnation curve
Wherein,the number of the Nash linear reservoirs is n;
the generalized Nash confluence model is simplified as follows:
then R isn(t) when the number of Nash linear reservoirs is n, the stagnant storage flow of inflow of 1 unit in the river channel is continued, so that the inflow of the unit is divided into two parts, and one part of the inflow forms the outlet section flow Sn(t), the other part is left in the river channel to form a stagnant impoundment flow rate Rn(t)；Rn-j(t) is the lag storage flow when the number of Nash linear reservoirs at the time t is (n-j);
(2.3) carrying out discretization treatment on the simplified generalized Nash confluence model, and establishing a discrete generalized Nash confluence model;
the discrete generalized Nash confluence model is as follows:
wherein I (t) refers to the inflow rate of the river reach at the time t, O (t) refers to the outflow rate of the river reach at the time t, O (t + △ t) refers to the outflow rate of the river reach at the time (t + △ t), O (t-I △ t) refers to the outflow rate of the river reach at the time (t-I △ t), △ I (t + △ t) refers to [ t, t + △ t ]]The inflow increment in the interval △ I (t + △ t) -I (t + △ t) -I (t); Rn-j(△ t) is a lag storage curve when the number of linear reservoirs at time △ t is (n-j), Rn(△ t) is a lag accumulation curve where the number of linear reservoirs at time △ t is n, Ri(△ t) is the slow storage curve when the number of linear reservoirs is i at time △ t, j is natural number, and the slow storage curveRepresenting the hold up in the channel for 1 unit of inflow.
Preferably, the river flood forecasting method based on the discrete generalized Nash confluence model includes the following substeps in step (2.3):
(2.3.1) establishing a forecasting model of the outflow at the moment (t +. DELTA.t) according to the physical meaning of the generalized Nash confluence model and the simplified Nash confluence model,
namely, the flow at the time of (t +. DELTA.t) consists of the water withdrawal of the water storage capacity of the current river channel and the outflow generated by upstream inflow in the time period of [ t, t +. DELTA.t ];
(2.3.2) carrying out discretization treatment on a derivative term and an integral term of the forecasting model of the flow rate at the (t plus delta t) moment to establish a discrete generalized Nash confluence model,
wherein: i (t), O (t) are respectively the inflow and outflow of the river reach at the time t; o (t +. DELTA.t) is the discharge rate at time (t +. DELTA.t);
in the discrete generalized Nash confluence model, [ t, t + △ t [ ]]The outflow formed by the upstream inflow in the time period comprises two parts, one part is the outflow formed by the current time flow I (t) passing through △ t, the other part is the outflow formed by the accumulated flow being deducted by the inflow increment in the time period, namely the outflow O (t + △ t) at the time (t + △ t) is the outflow in the time period (n-1) △ t before the current time and the outflow in the time period [ t, t + △ t ]]Linear combinations of the incoming flows over a time period; the weight coefficient of each term passes through the hysteresis curve Rn(t) is calculated.
Preferably, the river flood forecasting method based on the discrete generalized Nash confluence model includes the following substeps in step (2.3.2):
(2.3.2.1) discretizing the derivative terms of the forecast model of the outflow at time (t +. DELTA.t), in particular calculating the derivatives of the respective orders of O (t) from the forward difference approximation,
wherein,calculating a formula for the number of combinations;
(2.3.2.2) discretizing an integral term of the discharge forecasting model at the moment (t +. DELTA.t);
(a) assuming that the inflow I(s) varies linearly during the time interval [ t, t +. DELTA.t ], the integral term is transformed into a linear variation of the inflow I(s) during the time interval [ t, t +. DELTA.t ]
Wherein Δ I (t + Δ t) ═ I (t + Δ t) -I (t), refers to the inflow increment during the period [ t, t + Δ t ];
since the inflow I(s) varies linearly over the time period t, t +. DELTA.t, for s e [ t, t + DELTA.t ],
(b) let τ be t + Δ t-S, according to Sn(t) and u (t) using the fractional integral formula and Rn(t) and Sn(t) conversion of integral term into
Wherein,
preferably, in the river flood forecasting method based on the discrete generalized Nash confluence model, n is 1, 2 or 3.
Preferably, the first and second electrodes are formed of a metal,according to the river flood forecasting method based on the discrete generalized Nash confluence model, when n is 1, O ist+1＝R1Ot+[MK(1-R1)-R1]It+[1-MK(1-R1)]It+1(ii) a Wherein, Ot+1＝O(t+Δt)，Ot＝O(t),It+1＝I(t+Δt)，It＝I(t),R1＝R1(Δt)，MK＝K/Δt。
Preferably, in the river flood forecasting method based on the discrete generalized Nash confluence model, when n is 2, O is usedt+1＝(R1+R2)Ot-R1Ot-1+[MK(2-R1-R2)-R2]It+[1-MK(2-R1-R2)]It+1(ii) a Wherein, Ot+1＝O(t+Δt)，Ot＝O(t),Ot-1＝O(t-Δt)，It+1＝I(t+Δt)，It＝I(t),R1＝R1(Δt)，R2＝R2(Δt)，MK＝K/Δt。
Preferably, in the river flood forecasting method based on the discrete generalized Nash confluence model, when n is 3, O is usedt+1＝(0.5R1+R2+R3)Ot-(R1+R2)Ot-1+0.5R1Ot-2
+[MK(3-R1-R2-R3)-R3]It+[1-MK(3-R1-R2-R3)]It+1；
Wherein, Ot+1＝O(t+Δt)，Ot＝O(t),Ot-1＝O(t-Δt)，Ot-2＝O(t-2Δt)，It+1＝I(t+Δt)，It＝I(t),R1＝R1(Δt)，R2＝R2(Δt)，R3＝R3(Δt)，MK＝K/Δt。
Preferably, in the river flood forecasting method based on the discrete generalized Nash confluence model, the parameters n and K are determined according to the following method:
respectively taking n as 1, 2 and 3 by using an enumeration method, taking the minimum root mean square error as a target function, carrying out optimization and calibration on a parameter K by adopting an SCE-UA algorithm capable of quickly searching a parameter global optimal solution, and taking the corresponding n and K values when the root mean square error is minimum as parameter calibration results;
the SCE-UA algorithm is a complex cross evolution algorithm, is an effective method for solving the nonlinear constraint optimization problem, can quickly search the global optimal solution of the parameters of the hydrological model, and has the basic idea that the deterministic complex search technology and the natural biological competition evolution principle are combined to solve the minimization problem.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
(1) compared with the conventional commonly used MassJinggen method, the river channel flood forecasting method based on the discrete generalized Nash confluence model provided by the invention can improve the forecasting precision of flood peak and flood process due to full utilization of the historical effective information of the measured data;
(2) compared with the existing generalized Nash confluence model, the discrete generalized Nash confluence model adopted by the river flood forecasting method based on the discrete generalized Nash confluence model is expressed as a linear combination of a plurality of inflow rates and outflow rates in form, is more intuitive and is convenient to popularize and apply in practice;
(3) compared with other statistical methods, the river flood forecasting method based on the discrete generalized Nash confluence model expresses the weight coefficient of the linear combination as a function of the stagnation curve, and only n and K are used as parameters without extra addition of the parameters.
Drawings
Fig. 1 is a flowchart of a river flood forecasting method of a discrete generalized Nash confluence model according to an embodiment;
fig. 2 is a process line comparison diagram of the river flood forecasting result in the embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The technical means of the present invention will be specifically described below by way of examples.
The specific embodiment takes the example of the riverside water distribution puerto-river separation rock river reach as illustration; the water distribution bealock reservoir is a leading step for three-level development of a downstream river reach in a dry stream of a Qingjiang river, the river-separating rock reservoir is positioned at the downstream of the reservoir at 92km, and the river basin area of a section area interval of two dam sites is 3570km2(ii) a The two water reservoirs not only undertake the flood control task of the Qingjiang river basin, but also reserve 10 hundred million meters for the peak staggering of the flood of the Yangtze river3Flood control reservoir capacity to cooperate with the three gorges reservoir to alleviate flood threat of Yangtze river Jingjiang river reach; the high-precision river flood forecast of the water distribution puerto-river separation rock river section has important significance for river clearing and even Yangtze river flood control; in the present invention, the river reach refers to a section of the river between the upper and lower control sections.
The flow of forecasting by adopting the river flood forecasting method based on the discrete generalized Nash confluence model provided by the embodiment of the invention, as shown in FIG. 1, specifically comprises the following steps:
(1) according to historical reduction data of dam site sections of two reservoirs, a flood process (the calculation time interval delta t is 3h) of 7 months in 1997 in the river reach is selected for calculation, flood of the river reach is mainly caused by heavy rain at the middle and upper reaches of Qingjiang, the proportion of incoming water in the zones of the water distribution bealock-river separation rock is small, and in order to keep water balance, the incoming water in the zones is uniformly distributed to the section of the water distribution bealock site in proportion.
(2) Establishing a discrete generalized Nash confluence model of the river reach, and taking the minimum root mean square error as a target function, and respectively performing optimization and calibration on parameters of the discrete generalized Nash confluence model by adopting an SCE-UA algorithm, wherein in the embodiment of the invention, the parameter n is 3, and the parameter K is 1.53 h; wherein n refers to the number of the Nash linear reservoirs, and K is a parameter reflecting the storage regulation capacity of the Nash linear reservoirs;
(3) forecasting the outlet flow at the (t +. DELTA.t) moment by using a discrete generalized Nash confluence model of the river reach according to the outlet flow at the 2. DELTA.t time interval before the current moment and the inlet flow at the [ t, t +. DELTA.t ] time interval:
Ot+1＝(0.5R1+R2+R3)Ot-(R1+R2)Ot-1+0.5R1Ot-2
+[MK(3-R1-R2-R3)-R3]It+[1-MK(3-R1-R2-R3)]It+1；
wherein, Ot+1＝O(t+Δt)，Ot＝O(t),Ot-1＝O(t-Δt)，Ot-2＝O(t-2Δt)，It+1＝I(t+Δt)，It＝I(t),R1＝R1(Δt)，R2＝R2(Δt)，R3＝R3(Δt)，MK＝K/Δt。
In order to verify the simulation effect of the river flood forecasting method provided by the invention, the forecasting result of the river flood forecasting method provided by the invention in the embodiment is compared with the forecasting effect of the river flood forecasting method adopting the Masjing root method;
the results of the precision evaluations for both methods are listed in table 1;
table 1 flood peak forecast accuracy evaluation result
In the data listed in table 1, the field flood had 2 flood peaks; the relative peak errors of the Masjing root method are respectively-5.30% and-10.75%, and the method provided by the embodiment is improved to a greater extent than the Masjing root method, and respectively reaches 1.52% and-0.75%, thereby showing stronger real-time forecasting capability.
The simulation result pair of the flood process by adopting the masjing root method and the method provided by the invention is shown in fig. 2, and as can be seen from fig. 2, the forecast result of the method provided by the invention is closer to the actual measurement process line, the certainty coefficients respectively reach 0.9782, and the certainty coefficient of the masjing root method is only 0.9638; the reason is that the method provided by the invention comprises the current output flow OtBesides, the first two times O are also includedt-1And Ot-2The information implies the change trend of the flood process, so that the forecasting capability of the river flood forecasting method provided by the invention is more excellent.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (7)
1. A river channel flood forecasting method based on a discrete generalized Nash confluence model is characterized by comprising the following steps:
(1) selecting historical flood according to historical synchronous observation data of inflow and outflow of the river reach to be forecasted;
(2) establishing a discrete generalized Nash confluence model of a river reach to be forecasted; based on historical field flood information, carrying out optimization calibration on parameters n and K of the discrete generalized Nash confluence model by adopting an SCE-UA algorithm;
wherein n refers to the number of the Nash linear reservoirs, and K is a parameter reflecting the storage regulation capacity of the Nash linear reservoirs;
(3) forecasting the outlet flow at the (t + delta t) moment by utilizing the discrete generalized Nash confluence model according to the outlet flow at the (n-1) delta t time interval before the current moment and the inlet flow at the [ t, t + delta t ] time interval; where Δ t is the calculation period length.
2. The river flood forecasting method according to claim 1, wherein the method for establishing the discrete generalized Nash confluence model in the step (2) comprises the following sub-steps:
(2.1) establishing a generalized Nash confluence model under the condition that the initial condition is not zero:
wherein: i (t), O (t) are respectively inflow and outflow processes of the river section, O(j)(0) Is O (t) the derivative of order j at the initial instant, u (-) is the Nash transient unit line;
(2.2) use of the hysteresis curve Rn(t) simplifying the generalized Nash confluence model;
defining stagnation curve
Wherein,the number of the Nash linear reservoirs is n;
the generalized Nash confluence model is simplified as follows:
(2.3) establishing a flow forecasting model according to the simplified generalized Nash confluence model and carrying out discretization treatment to obtain a discretized generalized Nash confluence model;
the discrete generalized Nash confluence model is as follows:
wherein I (t) refers to the inflow rate of the river reach at the time t, O (t) refers to the outflow rate of the river reach at the time t, O (t + △ t) refers to the outflow rate of the river reach at the time (t + △ t), O (t-I △ t) refers to the outflow rate of the river reach at the time (t-I △ t), △ I (t + △ t) refers to [ t, t + △ t ]]The inflow increment in the interval △ I (t + △ t) -I (t + △ t) -I (t); Rn-j(△ t) is a lag storage curve when the number of linear reservoirs at time △ t is (n-j), Rn(△ t) is a lag accumulation curve where the number of linear reservoirs at time △ t is n, Ri(△ t) is the slow storage curve when the number of linear reservoirs is i at time △ t, and the slow storage curveRepresenting the hold up in the channel for 1 unit of inflow.
3. A method as claimed in claim 2, wherein step (2.3) includes the sub-steps of:
(2.3.1) establishing a forecasting model of the flow rate at the moment (t +. DELTA.t) according to the simplified generalized Nash confluence model,
wherein R isn-j(t) is a lag storage curve when the number of linear reservoirs at the time t is (n-j);
and (2.3.2) carrying out discretization treatment on a derivative term and an integral term of the forecasting model, and establishing a discrete generalized Nash confluence model.
4. The river flood forecasting method according to claim 2, wherein n is 1, 2 or 3, the root mean square error is the minimum target function by an enumeration method, the parameter K is optimally calibrated by an SCE-UA algorithm capable of quickly searching a globally optimal solution of the parameter, and the corresponding n and K values when the root mean square error is the minimum are used as parameter calibration results.
5. The river flood forecasting method of claim 4,
n＝1，Ot+1＝R1Ot+[MK(1-R1)-R1]It+[1-MK(1-R1)]It+1；
wherein, Ot+1O (t + Δ t), which is the outflow rate at time (t + △ t), OtThe flow rate at the time t is referred to; i istMeans the inflow at time t; i ist+1I (t + Δ t), means the inflow at time (t + △ t), R1＝R1(Δ t) is a retention curve when the number of linear reservoirs at time △ t is 1, MK＝K/Δt。
6. The river flood forecasting method of claim 4,
n＝2，Ot+1＝(R1+R2)Ot-R1Ot-1+[MK(2-R1-R2)-R2]It+[1-MK(2-R1-R2)]It+1；
wherein, Ot+1O (t + Δ t), which is the outflow rate at time (t + △ t), OtThe flow rate at the time t is referred to; o ist-1Is the outflow at time (t- △ t), ItMeans the inflow at time t; i ist+1I (t + Δ t), means the inflow at time (t + △ t), R1＝R1(Δ t) is a retention curve at △ t when the number of linear reservoirs is 1, and R2＝R2(Δ t) is a retention curve at △ t when the number of linear reservoirs is 2, MK＝K/Δt。
7. The river flood forecasting method of claim 4,
n＝3，Ot+1＝(0.5R1+R2+R3)Ot-(R1+R2)Ot-1+0.5R1Ot-2
+[MK(3-R1-R2-R3)-R3]It+[1-MK(3-R1-R2-R3)]It+1；
wherein, Ot+1O (t + Δ t), which is the outflow rate at time (t + △ t), OtThe flow rate at the time t is referred to; o ist-1Is the outflow at time (t- △ t), Ot-2Is the outflow at time (t-2 △ t), ItMeans the inflow at time t; i ist+1I (t + Δ t), means the inflow at time (t + △ t), R1＝R1(Δ t) is a retention curve at △ t when the number of linear reservoirs is 1, and R2＝R2(Δ t) is a hysteresis curve of △ t at the time when the number of linear reservoirs is 2, and R3＝R3(Δ t) is a retention curve at △ t when the number of linear reservoirs is 3, MK＝K/Δt。
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| CN107688702A (en) * | 2017-08-24 | 2018-02-13 | 西安理工大学 | A kind of river flood traffic camouflaging rule analogy method based on wolf pack algorithm |
| CN109871988A (en) * | 2019-01-28 | 2019-06-11 | 河海大学 | A kind of flood forecasting early warning precision analytical method |
| CN109992868A (en) * | 2019-03-25 | 2019-07-09 | 华中科技大学 | A kind of river flood forecasting procedure based on different ginseng discrete generalized Nash Confluence Model |
| CN111400655A (en) * | 2020-06-08 | 2020-07-10 | 中国水利水电科学研究院 | Correction optimization method and system for warehousing traffic |
| CN112861360A (en) * | 2021-02-19 | 2021-05-28 | 河海大学 | Maskyo flow calculation error correction method based on system response theory |
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| CN107688702A (en) * | 2017-08-24 | 2018-02-13 | 西安理工大学 | A kind of river flood traffic camouflaging rule analogy method based on wolf pack algorithm |
| CN107688702B (en) * | 2017-08-24 | 2020-11-17 | 西安理工大学 | Lane colony algorithm-based river channel flood flow evolution law simulation method |
| CN109871988A (en) * | 2019-01-28 | 2019-06-11 | 河海大学 | A kind of flood forecasting early warning precision analytical method |
| CN109992868A (en) * | 2019-03-25 | 2019-07-09 | 华中科技大学 | A kind of river flood forecasting procedure based on different ginseng discrete generalized Nash Confluence Model |
| CN111400655A (en) * | 2020-06-08 | 2020-07-10 | 中国水利水电科学研究院 | Correction optimization method and system for warehousing traffic |
| CN112861360A (en) * | 2021-02-19 | 2021-05-28 | 河海大学 | Maskyo flow calculation error correction method based on system response theory |
| CN112861360B (en) * | 2021-02-19 | 2021-10-26 | 河海大学 | Maskyo flow calculation error correction method based on system response theory |
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