Flood forecasting method based on time-varying parameters

Abstract

The invention discloses a flood forecasting method based on a time-varying parameter, and relates to the technical field of flood forecasting, by considering a target time-varying parameter and a preset reference time-invariant parameter, an incidence relation between a target runoff regression coefficient and the target time-varying parameter and an incidence relation between the target runoff regression coefficient and the reference time-invariant parameter are determined; according to the method, a time-varying parameter reservoir model is constructed based on the incidence relation, and finally flood forecasting is performed based on the time-varying parameter reservoir model, so that the time-varying runoff production intensity can be effectively considered, overall analysis is performed from the macroscopic perspective of the system, and the slope confluence process is generalized into a wide and shallow open channel water flow process, thereby improving the accuracy of flood forecasting.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of flood prediction, in particular to a flood forecasting method based on time-varying parameters. BACKGROUND

[0002] Since the linear reservoir model was introduced, it has been widely used in catchment concentration. Traditional concentration models are mostly linear time-invariant systems, and when applied to flood forecasting, the phenomenon of smaller flood peak lag and larger flood peak advance is often encountered. Ultimately, this is because of the nonlinearity of concentration, that is, the concentration process does not satisfy the superposition assumption or the doubling assumption. The nonlinearity of catchment concentration is generally considered to be caused by the following factors: first, uneven spatial distribution of rainfall. For example, when the rainstorm center is in the upper reaches, the reservoir effect is large, and the concentration route is long, so the flood peak is low, and the peak time lags. Conversely, when the rainstorm center is in the lower reaches, the flood peak is high, and the peak time is advanced. Second, water source division factor. As is known to all, surface runoff and groundwater runoff have great differences in reservoir effect and concentration speed. Groundwater runoff is relatively flat and the flood peak lags, while surface runoff is relatively sharp and the flood peak advances. Third, due to factors such as rainfall intensity and soil moisture content, even if the spatial distribution of rainfall is uniform, the concentration process also exhibits nonlinear characteristics. Therefore, related technologies generally use time-invariant linear reservoir models for flood forecasting, which has the problem of low prediction accuracy. SUMMARY

[0003] The present application provides a flood forecasting method based on time-varying parameters, aiming to solve the problem of low flood forecasting accuracy in related technologies.

[0004] The present application provides a flood forecasting method based on time-varying parameters, comprising:

[0005] obtaining a target time-varying parameter and a preset reference time-invariant parameter; wherein the target time-varying parameter represents the runoff intensity of a certain period;

[0006] determining a target runoff recession coefficient and the correlation between the target time-varying parameter and the reference time-invariant parameter according to the target time-varying parameter and the preset reference time-invariant parameter, to obtain a first relationship function; wherein the target runoff recession coefficient represents the runoff recession coefficient corresponding to the period corresponding to the target time-varying parameter;

[0007] constructing a time-varying parameter reservoir model according to the first relationship function, the reference runoff recession coefficient, and the preset runoff depth and flow conversion coefficient; wherein the time-varying parameter reservoir model represents a model for flood flow prediction based on the target time-varying parameter; the reference runoff recession coefficient is the preset runoff recession coefficient corresponding to the reference time-invariant parameter;

[0008] Based on the time-varying parameter reservoir model, time-varying parameter-based flood flow prediction is performed to obtain a flood forecasting result.

[0009] In a possible implementation, the determining, according to the target time-varying parameter and the preset reference time-invariant parameter, of a correlation between the target runoff recession coefficient and the target time-varying parameter and the reference time-invariant parameter to obtain a first relationship function includes:

[0010] The target generalized wide and shallow open channel flow velocity is obtained based on the target time-varying parameter.

[0011] The reference generalized wide and shallow open channel flow velocity is obtained based on the reference time-invariant parameter.

[0012] The target concentration time and the target time-varying parameter and the preset reference time-invariant parameter are determined according to the target generalized wide and shallow open channel flow velocity and the reference generalized wide and shallow open channel flow velocity to obtain a second relationship function.

[0013] A third relationship function between the target concentration time and the target runoff recession coefficient is obtained, and a fourth relationship function between the target concentration time and the target runoff recession coefficient is determined according to the third relationship function, where the target concentration time is a concentration time corresponding to the target time-varying parameter.

[0014] A fifth relationship function between the reference concentration time and the reference runoff recession coefficient is obtained, and a sixth relationship function between the reference concentration time and the reference runoff recession coefficient is determined according to the third relationship function, where the reference concentration time is a concentration time corresponding to the reference time-invariant parameter.

[0015] The first relationship function is determined according to the second relationship function, the fourth relationship function, and the sixth relationship function.

[0016] In a possible implementation, the target generalized wide and shallow open channel flow velocity is obtained based on the target time-varying parameter by using the Manning formula, including:

[0017] The riverbed slope parameter and the roughness parameter inherent in the target basin are obtained.

[0018] The target generalized wide and shallow open channel flow velocity is obtained based on the riverbed slope parameter, the roughness parameter inherent in the target basin, and the target time-varying parameter by using the Manning formula.

[0019]

[0020] where v t represents the target generalized wide and shallow open channel flow velocity, n represents the roughness parameter, i represents the riverbed slope parameter, and r tThe target time-varying parameter is represented.

[0021] In a possible implementation, the reference generalized wide and shallow open channel flow velocity is obtained based on the reference time-invariant parameter, and the reference generalized wide and shallow open channel flow velocity comprises:

[0022] The riverbed slope parameter and the roughness parameter inherent to the target basin are obtained.

[0023] The reference generalized wide and shallow open channel flow velocity is obtained by using the Manning formula based on the riverbed slope parameter, the roughness parameter inherent to the target basin, and the reference time-invariant parameter.

[0024]

[0025] wherein v I The reference generalized wide and shallow open channel flow velocity is represented by v r The reference time-invariant parameter is represented by I r The roughness parameter is represented by n, and the riverbed slope parameter is represented by i.

[0026] In a possible implementation, the second relationship function is obtained by determining the correlation between the target concentration time and the target time-varying parameter and the preset reference time-invariant parameter according to the target generalized wide and shallow open channel flow velocity and the reference generalized wide and shallow open channel flow velocity.

[0027] The target concentration time is determined according to the target generalized wide and shallow open channel flow velocity.

[0028] The reference concentration time is determined according to the reference generalized wide and shallow open channel flow velocity.

[0029] The correlation between the target concentration time and the target time-varying parameter and the preset reference time-invariant parameter is obtained according to the target concentration time and the reference concentration time, and the second relationship function is obtained.

[0030]

[0031] wherein L represents a fixed basin length, v r The target generalized wide and shallow open channel flow velocity is represented by v I The reference generalized wide and shallow open channel flow velocity is represented by v t The target time-varying parameter is represented by I r The reference time-invariant parameter is represented by I r The reference concentration time is represented by T t The target concentration time is represented by T

[0032] In a possible implementation, the third relationship function between the target concentration time and the target runoff recession coefficient is acquired, and a fourth relationship function between the target concentration time and the target runoff recession coefficient is determined according to the third relationship function, including:

[0033] The third relationship function between the target concentration time and the target runoff recession coefficient is acquired as follows:

[0034]

[0035] wherein t represents a time, T t represents the target concentration time, C t represents the target runoff recession coefficient, and θ represents a runoff surplus coefficient, and θ≦C t .

[0036] The third relationship function is simplified to determine the fourth relationship function between the target concentration time and the target runoff recession coefficient as follows:

[0037]

[0038] wherein log represents a logarithmic function.

[0039] In a possible implementation, the fifth relationship function between the reference concentration time and the reference runoff recession coefficient is acquired, and a sixth relationship function between the reference concentration time and the reference runoff recession coefficient is determined according to the third relationship function, including:

[0040] The fifth relationship function between the reference concentration time and the reference runoff recession coefficient is acquired as follows:

[0041]

[0042] The third relationship function is simplified to determine the fourth relationship function between the reference concentration time and the reference runoff recession coefficient as follows:

[0043]

[0044] wherein C r represents the reference runoff recession coefficient, T r represents the reference concentration time, and θ≦C r .

[0045] In a possible implementation, the first relationship function is determined according to the second relationship function, the fourth relationship function and the sixth relationship function, including:

[0046] The fourth relationship function and the sixth relationship function are input into the second relationship function to obtain the first relationship function as follows:

[0047]

[0048] In a possible implementation, the time-varying parameter reservoir model is constructed according to the first relationship function, the reference runoff recession coefficient, and a preset runoff depth-flow conversion coefficient.

[0049]

[0050] wherein Q t represents the flow at time t, Q t+1 represents the flow at time t+1, and U represents the preset runoff depth-flow conversion coefficient. t t+1 wherein Q t represents the flow at time t, Q t+1 represents the flow at time t+1, and U represents the preset runoff depth-flow conversion coefficient.

[0051] In a possible implementation, the time-varying parameter reservoir model is constructed according to the first relationship function, the reference runoff recession coefficient, and a preset runoff depth-flow conversion coefficient.

[0052] The time-varying parameter reservoir model is used to input a target time-varying parameter at any time to perform flood flow prediction, and a flood forecasting result is obtained.

[0053] Beneficial effects:

[0054] The method provided in the application can effectively consider the time-varying runoff intensity, can analyze the whole system from a macro perspective, can generalize the slope confluence process into a wide and shallow open channel flow process, and can improve the accuracy of flood forecasting. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the description of the embodiments of the application. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0056] Figure 1 is a flowchart of a flood forecasting method based on time-varying parameters according to an embodiment of the application;

[0057] Figure 2 is a flowchart of obtaining a first relationship function according to an embodiment of the application;

[0058] Figure 3 ​is a structural schematic diagram of a flood forecasting device based on time-varying parameters according to an embodiment of the present application;

[0059] Figure 4 is a structural schematic diagram of an electronic device according to an embodiment of the present application;

[0060] Label explanation: 301-parameter acquisition module, 302-relation function determination module, 303-model construction module, 304-flood forecasting module, 401-memory, 402-processor, 403-communication bus. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0062] In the related art, a linear time-invariant system is generally used for flood forecasting. In the flood forecasting process, the phenomenon that the flood peak of a "large flood" is smaller and lags behind, and the flood peak of a "small flood" is larger and leads is often encountered, thereby causing the problem of low accuracy of flood forecasting.

[0063] Therefore, the embodiments of the present application propose a flood forecasting method based on time-varying parameters. From the macroscopic point of view, the slope confluence process is generalized as a wide and shallow open channel flow process (referred to as a generalized wide and shallow open channel), the relationship between the average confluence velocity and the time-varying parameter is analyzed by using the Manning formula, and then the theoretical relationship between the linear reservoir parameter and the runoff intensity is derived, thereby improving the accuracy of flood forecasting.

[0064] Please refer to Figure 1 is a flowchart of a flood forecasting method based on time-varying parameters according to an embodiment of the present application. The method comprises the following steps.

[0065] S101, acquiring a target time-varying parameter and a preset reference time-invariant parameter; wherein the target time-varying parameter represents the runoff intensity in a certain period of time;

[0066] The slope confluence process is affected by variable factors such as rain intensity and soil moisture state. The greater the rain intensity and the greater the soil moisture content, the faster the confluence, and vice versa. The greater the rain intensity and the greater the soil moisture content mean the greater the runoff intensity. Therefore, the runoff intensity is used as the target time-varying parameter, so as to realize the prediction of flood flow at different times.

[0067] The target time-varying parameter refers to a parameter that changes with time, and the reference time-invariant parameter is a preset fixed parameter, so as to reflect the influence of the change of the target time-varying parameter.

[0068] In S102, a first relationship function is determined according to the target time-varying parameter and the preset reference time-invariant parameter, and a correlation between the target runoff recession coefficient and the target time-varying parameter and the reference time-invariant parameter, wherein the target runoff recession coefficient represents a runoff recession coefficient corresponding to a time period corresponding to the target time-varying parameter.

[0069] By determining the correlation between the target runoff recession coefficient and the target time-varying parameter and the reference time-invariant parameter, the runoff recession coefficient in the original linear reservoir model can be transformed into a function affected by the time-varying parameter, so as to convert the original linear reservoir model into a time-varying parameter reservoir model.

[0070] For example, a relationship among the target time-varying parameter, the reference time-invariant parameter, the target concentration time and the reference concentration time can be constructed first, and then a relationship between the target concentration time and the target runoff recession coefficient and a relationship between the reference concentration time and the reference runoff recession coefficient are constructed. After fusing the several relationships, the correlation between the target runoff recession coefficient and the target time-varying parameter and the reference time-invariant parameter, i.e., the first relationship function, can be determined. The first relationship function is a nonlinear function affected by the time-varying parameter. By determining the target time-varying parameter in real time, the nonlinear influence of the target time-varying parameter on the flood flow can be determined.

[0071] In S103, a time-varying parameter reservoir model is constructed according to the first relationship function, the reference runoff recession coefficient and a preset runoff depth-flow conversion coefficient, wherein the time-varying parameter reservoir model represents a model for flood flow prediction based on the target time-varying parameter, and the reference runoff recession coefficient is a preset runoff recession coefficient corresponding to the reference time-invariant parameter.

[0072] In the related art, although the time-varying parameter is applied, it is only linearly applied and the problem that the runoff recession coefficient changes under different time-varying parameters is not considered. Therefore, in the embodiments of the present application, the time-varying parameter reservoir model is constructed according to the first relationship function, the reference runoff recession coefficient and the preset runoff depth-flow conversion coefficient, and the nonlinear influence of the target time-varying parameter on the runoff recession coefficient is fully considered, so that the flood prediction is more accurate.

[0073] In S104, flood flow prediction based on the time-varying parameter is performed based on the time-varying parameter reservoir model, and a flood prediction result is obtained.

[0074] Exemplarily, based on a time-varying parameter reservoir model, a flood flow prediction based on time-varying parameters is performed to obtain a flood forecast result, including: based on the time-varying parameter reservoir model, the target time-varying parameters at any moment are input into the time-varying parameter reservoir model to perform flood flow prediction to obtain a flood forecast result.

[0075] The target time-varying parameters can effectively reflect the dynamic influencing factors of rainfall. Therefore, nonlinear processing of the target time-varying parameters through the time-varying parameter reservoir model can effectively avoid the problem of inaccurate forecasting caused by linear non-time-varying processing, thereby improving the accuracy of flood forecasting.

[0076] The time-varying parameter reservoir model proposed in the embodiment of the present application is constructed based on the linear reservoir model, so the linear reservoir model is first introduced. The linear reservoir model can be: Q t+1 =CQ t +(1-C)r t U; where Q t+1 represents the flow at time t+1, Q t represents the flow rate at time t, r t represents the runoff intensity during period t, U represents the runoff depth-to-discharge conversion coefficient, and C represents the runoff decay coefficient. The runoff decay coefficient is the only parameter in the linear reservoir model and is a sensitive parameter. A smaller parameter indicates a shorter confluence time, and vice versa. It can be seen that in related technologies, the runoff decay coefficient is set to a linear value. However, with varying runoff intensity, the confluence time also varies to a certain extent, resulting in inaccurate flood forecasts when using existing linear reservoir models.

[0077] like Figure 2 As shown in FIG, it is a flowchart of obtaining the first relationship function provided by the embodiment of the present application. Figure 2 As shown, according to the target time-varying parameter and the preset reference time-invariant parameter, the correlation relationship between the target runoff decay coefficient and the target time-varying parameter and the reference time-invariant parameter is determined to obtain a first relationship function, including:

[0078] S201. Based on the target time-varying parameters, obtain the target generalized wide-shallow open channel flow velocity (i.e., slope confluence velocity, representing the average velocity of all water particles on the slope);

[0079] The embodiment of the present application conducts an overall analysis from a macroscopic perspective, generalizing the slope confluence process into a wide-shallow open channel flow process, which is called a generalized wide-shallow open channel. Therefore, the Manning formula can be used to analyze the relationship between the average confluence velocity and the runoff intensity, and then deduce the theoretical relationship between the linear reservoir parameters and the runoff intensity.

[0080] To consider the uneven spatial distribution of rainfall and underlying surface conditions, runoff and confluence calculation is often carried out by unit. Taking a unit watershed as an example, a generalized wide and shallow open channel is introduced. The entire slope surface is generalized as a generalized open channel. The width B of the open channel is related to the range of the unit watershed. The river bed slope i is related to the average slope of the slope surface and does not change along the way. The river roughness n is related to vegetation, soil type, land use, etc. The water depth h is the runoff intensity r (periodic runoff), which is very shallow compared with the river width, forming a wide and shallow open channel flow. The Manning formula is used to describe the water flow velocity as follows: Wherein, v represents the water flow velocity, n represents the roughness parameter of the river, R d is the hydraulic radius of the river, and J is the hydraulic slope.

[0081] Exemplarily, the Manning formula is used to obtain the target generalized wide and shallow open channel flow velocity based on the target time-varying parameter, including:

[0082] Obtaining the river bed slope parameter and the roughness parameter inherent to the target watershed;

[0083] According to the river bed slope parameter, the roughness parameter inherent to the target watershed and the target time-varying parameter, the Manning formula is used to obtain the target generalized wide and shallow open channel flow velocity as follows:

[0084]

[0085] Wherein, v t represents the target generalized wide and shallow open channel flow velocity, n represents the roughness parameter, i represents the river bed slope parameter, and r t represents the target time-varying parameter.

[0086] S202, obtaining the reference generalized wide and shallow open channel flow velocity based on the reference time-invariant parameter;

[0087] The obtaining process of the reference generalized wide and shallow open channel flow velocity is the same as that of the target generalized wide and shallow open channel flow velocity. The Manning formula is used for analysis, so as to determine the reference generalized wide and shallow open channel flow velocity.

[0088] Exemplarily, the reference generalized wide and shallow open channel flow velocity is obtained based on the reference time-invariant parameter, including:

[0089] Obtaining the river bed slope parameter and the roughness parameter inherent to the target watershed;

[0090] According to the river bed slope parameter, the roughness parameter inherent to the target watershed and the reference time-invariant parameter, the Manning formula is used to obtain the reference generalized wide and shallow open channel flow velocity as follows:

[0091]

[0092] Wherein, v IVref represents a reference generalized wide shallow channel water flow velocity, I r Vref represents a reference generalized wide shallow channel water flow velocity, I

[0093] S203, determining a relationship between the target concentration time and the target time-varying parameter and the preset reference time-invariant parameter according to the target generalized wide shallow channel water flow velocity and the reference generalized wide shallow channel water flow velocity, obtaining a second relationship function;

[0094] Considering that the roughness and the river bed slope are determined by the inherent properties of the basin and are relatively stable, and the runoff intensity is a time-varying factor, the slope flow concentration velocity changes with time, so the runoff intensity is the main factor affecting the nonlinearity of slope flow concentration. The slope flow concentration velocity is positively correlated with soil moisture and rainfall intensity, and soil moisture and rainfall intensity are the decisive factors of runoff intensity. Therefore, on the basis of determining the target generalized wide shallow channel water flow velocity and the reference generalized wide shallow channel water flow velocity, the concentration time of the same section of the basin can be determined, that is, the target concentration time corresponding to the target generalized wide shallow channel water flow velocity and the reference concentration time corresponding to the reference generalized wide shallow channel water flow velocity. The target concentration time divided by the reference concentration time can obtain the relationship between the target concentration time and the target time-varying parameter and the preset reference time-invariant parameter.

[0095] S204, obtaining a third relationship function between the target concentration time and the target runoff recession coefficient, and determining a fourth relationship function between the target concentration time and the target runoff recession coefficient according to the third relationship function; wherein the target concentration time is the concentration time corresponding to the target time-varying parameter;

[0096] The linear reservoir model actually distributes the runoff in an equal ratio series in time, so the runoff recession coefficients of each period can be added and summed, and when the concentration time is infinite, the sum is 1. Therefore, based on this, the runoff surplus coefficient is introduced, so that the relationship between the target concentration time and the target runoff recession coefficient becomes a solvable relationship.

[0097] S205, obtaining a fifth relationship function between the reference concentration time and the reference runoff recession coefficient, and determining a sixth relationship function between the reference concentration time and the reference runoff recession coefficient according to the third relationship function; wherein the reference concentration time is the concentration time corresponding to the reference time-invariant parameter;

[0098] The fifth relationship function and the third relationship function are obtained in the same way, and the sixth relationship function and the fourth relationship are obtained in the same way, which will not be repeated here.

[0099] S206, determining the first relationship function according to the second relationship function, the fourth relationship function and the sixth relationship function.

[0100] It can be known through step S203 that the second relationship function is obtained by dividing the target concentration time by the reference concentration time, and thus it can be determined that the runoff surplus coefficient is eliminated in the calculation process, and the first relationship function obtained finally does not contain the runoff surplus coefficient, thereby verifying that the first relationship function acquisition method proposed in the embodiment of the application is accurate.

[0101] In a possible implementation, the second relationship function between the target concentration time and the target time-varying parameter and the preset reference time-invariant parameter is determined according to the target generalized wide and shallow open channel flow velocity and the reference generalized wide and shallow open channel flow velocity, and the second relationship function is obtained.

[0102] The target concentration time is determined according to the target generalized wide and shallow open channel flow velocity, and the reference concentration time is determined according to the reference generalized wide and shallow open channel flow velocity.

[0103] For the same fixed length of the watershed, the concentration time can be determined in the case of knowing the flow velocity, and thus in the case of knowing the target generalized wide and shallow open channel flow velocity and the reference generalized wide and shallow open channel flow velocity, a fixed watershed length can be used to obtain the target concentration time and the reference concentration time.

[0104] The second relationship function between the target concentration time and the target time-varying parameter and the preset reference time-invariant parameter is obtained according to the target concentration time and the reference concentration time, and the second relationship function is obtained.

[0105]

[0106] Wherein, L represents the fixed length of the watershed, v r represents the target generalized wide and shallow open channel flow velocity, v I represents the reference generalized wide and shallow open channel flow velocity, r t represents the target time-varying parameter, I r represents the reference time-invariant parameter, T r represents the reference concentration time, T t represents the target concentration time.

[0107] The second relationship function indicates the relationship between the target concentration time and the target time-varying parameter and the preset reference time-invariant parameter, and thus the relationship between the runoff recession coefficient and the concentration time is determined on the basis of the second relationship function, and the runoff recession coefficient that changes nonlinearly with the target time-varying parameter is determined, thereby avoiding the problem of inaccurate prediction caused by using the existing linear runoff recession coefficient.

[0108] In a possible implementation, a third relationship function between the target concentration time and the target runoff recession coefficient is obtained, and a fourth relationship function between the target concentration time and the target runoff recession coefficient is determined according to the third relationship function, including:

[0109] The third relationship function between the target concentration time and the target runoff recession coefficient is obtained as follows:

[0110]

[0111] where t represents a time, T represents the target concentration time, C represents the target runoff recession coefficient, and θ represents a runoff surplus coefficient, and θ ≤ C. t t t ;

[0112] The linear reservoir actually distributes the runoff in time according to a geometric progression, and the proportions of each period are (1-C), (1-C)C, (1-C)C 2 , (1-C)C n , and so on. The sum of all proportions is 1, satisfying the water balance, that is, the identity:

[0113]

[0114] where lim represents a limit function, and T represents the concentration time.

[0115] Based on the above identity, it can be seen that the linear reservoir model satisfies the water balance only when t tends to infinity, and the concentration process is completed, so the concentration time is infinite. The identity cannot effectively establish the relationship between the target concentration time and the target runoff recession coefficient, therefore, the runoff surplus coefficient is introduced to solve the relationship in the embodiments of the present application. The runoff surplus refers to the part of the runoff that has not completed concentration after a period of time after the runoff of the basin, and the runoff still remains in the basin and has not passed through the outlet section of the basin.

[0116] The third relationship function can be transformed as follows:

[0117]

[0118] Based on the third relationship function after the transformation, it can be seen that the runoff recession coefficient and the runoff surplus coefficient are in an exponential relationship. When the concentration time is constant, the greater the runoff recession coefficient, the greater the runoff surplus coefficient, indicating that the more runoff that has not completed concentration, that is, the greater the runoff recession coefficient, the longer the time required to complete the concentration process.

[0119] The third relationship function is simplified to determine the fourth relationship function between the target concentration time and the target runoff recession coefficient as follows:

[0120] ​​

[0121] wherein, log represents a logarithmic function.

[0122] It can be seen from the fourth relationship that the concentration time and the runoff yield coefficient are in logarithmic relationship. When the runoff recession coefficient is constant, the greater the runoff yield coefficient, the smaller the concentration time, that is, the more runoff is not completed, and the shorter the concentration time. The above analysis is consistent with the general understanding of the linear reservoir model, and proves the feasibility of the scheme described in the embodiments of the present application.

[0123] In a possible implementation, a fifth relationship function between the reference concentration time and the reference runoff recession coefficient is obtained, and a sixth relationship function between the reference concentration time and the reference runoff recession coefficient is determined according to the third relationship function, including:

[0124] The fifth relationship function between the reference concentration time and the reference runoff recession coefficient is:

[0125]

[0126] The third relationship function is simplified to determine the fourth relationship function between the reference concentration time and the reference runoff recession coefficient, including:

[0127]

[0128] wherein, C r represents the reference runoff recession coefficient, T r represents the reference concentration time, and θ≦C r .

[0129] The fifth relationship function and the third relationship function are obtained in the same way, and the sixth relationship function and the fourth relationship are obtained in the same way, which will not be described here.

[0130] In a possible implementation, the first relationship function is determined according to the second relationship function, the fourth relationship function and the sixth relationship function, including:

[0131] The fourth relationship function and the sixth relationship function are input into the second relationship function to obtain the first relationship function, including:

[0132]

[0133] The first relationship function provides a runoff recession coefficient conversion formula between different runoff yield intensities. The function shows that the greater the runoff yield intensity, the smaller the runoff recession coefficient, indicating that the confluence time is shorter, which is consistent with the existing research results. In addition, the first relationship function proposed in the embodiments of the present application is irrelevant to the runoff yield surplus coefficient. Therefore, for any runoff yield surplus coefficient, the ratio of the confluence time can meet the second relationship function as long as the above relationship is met for different runoff recession coefficients.

[0134] In the embodiments of the present application, the reference runoff recession coefficient refers to the average value of the runoff recession coefficients corresponding to all floods in the historical data, and the time-invariant parameter refers to the average value of the runoff yield intensities corresponding to all floods in the historical data.

[0135] In a possible implementation, according to the first relationship function, the reference runoff recession coefficient, and the preset runoff depth-flow conversion coefficient, the time-varying parameter reservoir model is constructed as follows:

[0136]

[0137] wherein Q t represents the flow at time t, Q t+1 represents the flow at time t+1, and U represents the preset runoff depth-flow conversion coefficient.

[0138] In the related art, the reference runoff recession coefficient C r (located between 0 and 1) is the average value of the runoff recession coefficients corresponding to all floods in the historical data, without considering the influence of runoff yield intensity. Generally, the greater the runoff yield intensity, the faster the confluence speed, and the smaller the corresponding runoff recession coefficient, which is manifested in the flood process as a steeper and shorter flood peak. The time-varying parameter reservoir model proposed in the embodiments of the present application considers the influence of runoff yield intensity on the runoff recession coefficient, taking as a whole as the runoff recession coefficient, and the target time-varying parameter r t is greater, the greater the runoff yield intensity I r is (wherein I r is the average runoff yield intensity of all floods in the historical data, which is a time-invariant parameter), and therefore is also greater, r and because C is between 0 and 1, r r is smaller, that is, the greater the runoff yield intensity, the smaller the corresponding runoff recession coefficient, and the steeper and more rapid the flood peak, which is consistent with the general understanding.

[0139] The time-varying parameter reservoir model provided in the embodiments of the present application can be seen that the target time-varying parameter is used to nonlinearly adjust the runoff recession coefficient, so that the runoff recession coefficient can be more in line with the actual situation, thereby improving the accuracy of flood forecasting.

[0140] Please refer to Figure 3Based on the same inventive concept, the embodiment of the present application provides a flood forecasting device based on time-varying parameters, which comprises:

[0141] The parameter acquisition module 301 is configured to acquire a target time-varying parameter and a preset reference time-invariant parameter; wherein the target time-varying parameter represents the runoff yield intensity of a certain period;

[0142] The relationship function determination module 302 is configured to determine the correlation between the target runoff recession coefficient and the target time-varying parameter and the reference time-invariant parameter according to the target time-varying parameter and the preset reference time-invariant parameter, and obtain a first relationship function; wherein the target runoff recession coefficient represents the runoff recession coefficient corresponding to the period corresponding to the target time-varying parameter;

[0143] The model construction module 303 is configured to construct a time-varying parameter reservoir model according to the first relationship function, a reference runoff recession coefficient, and a preset runoff depth-flow conversion coefficient; wherein the time-varying parameter reservoir model represents a model for flood flow prediction based on the target time-varying parameter; the reference runoff recession coefficient is a preset runoff recession coefficient corresponding to the reference time-invariant parameter;

[0144] The flood forecasting module 304 is configured to perform flood flow prediction based on time-varying parameters based on the time-varying parameter reservoir model, and obtain a flood forecasting result.

[0145] In a possible implementation, the relationship function determination module 302 comprises a first speed acquisition sub-module, a second speed acquisition sub-module, a first data processing sub-module, a second data processing sub-module, a third data processing sub-module, and a fourth data processing sub-module;

[0146] The first speed acquisition sub-module is configured to acquire a target generalized wide and shallow open channel flow speed based on the target time-varying parameter;

[0147] For example, the target generalized wide and shallow open channel flow speed is acquired based on the target time-varying parameter by using the Manning formula, which comprises: acquiring the inherent riverbed slope parameter and roughness parameter of the target basin; acquiring the target generalized wide and shallow open channel flow speed by using the Manning formula according to the inherent riverbed slope parameter, roughness parameter, and target time-varying parameter of the target basin: Wherein, v t represents the target generalized wide and shallow open channel flow speed, n represents the roughness parameter, i represents the riverbed slope parameter, and r t represents the target time-varying parameter.

[0148] The second speed acquisition sub-module is configured to acquire a reference generalized wide and shallow open channel flow speed based on the reference time-invariant parameter;

[0149] Exemplarily, the reference generalized wide and shallow open channel water flow velocity is obtained based on the reference time-invariant parameter, and the obtaining comprises: obtaining a riverbed slope parameter and a roughness parameter inherent to the target basin; and obtaining the reference generalized wide and shallow open channel water flow velocity based on the riverbed slope parameter, the roughness parameter inherent to the target basin, and the reference time-invariant parameter by using a Manning formula, wherein the reference generalized wide and shallow open channel water flow velocity is: wherein v I represents the reference generalized wide and shallow open channel water flow velocity, I r represents the reference time-invariant parameter, n represents the roughness parameter, and i represents the riverbed slope parameter.

[0150] The first data processing submodule is configured to determine a correlation between the target concentration time and the target time-varying parameter and the preset reference time-invariant parameter based on the target generalized wide and shallow open channel water flow velocity and the reference generalized wide and shallow open channel water flow velocity, and obtain a second relationship function.

[0151] Exemplarily, the determining of the correlation between the target concentration time and the target time-varying parameter and the preset reference time-invariant parameter based on the target generalized wide and shallow open channel water flow velocity and the reference generalized wide and shallow open channel water flow velocity, and the obtaining of the second relationship function comprise: determining a target concentration time based on the target generalized wide and shallow open channel water flow velocity; determining a reference concentration time based on the reference generalized wide and shallow open channel water flow velocity; and obtaining the correlation between the target concentration time and the target time-varying parameter and the preset reference time-invariant parameter based on the target concentration time and the reference concentration time, and obtaining the second relationship function as: wherein L represents a fixed basin length, v r represents the target generalized wide and shallow open channel water flow velocity, v I represents the reference generalized wide and shallow open channel water flow velocity, r t represents the target time-varying parameter, I r represents the reference time-invariant parameter, T r represents the reference concentration time, T t represents the target concentration time.

[0152] The second data processing submodule is configured to obtain a third relationship function between the target concentration time and a target runoff recession coefficient, and determine a fourth relationship function between the target concentration time and the target runoff recession coefficient based on the third relationship function, wherein the target concentration time is a concentration time corresponding to the target time-varying parameter.

[0153] Exemplarily, the obtaining of the third relationship function between the target concentration time and the target runoff recession coefficient, and the determining of the fourth relationship function between the target concentration time and the target runoff recession coefficient based on the third relationship function comprise: obtaining the third relationship function between the target concentration time and the target runoff recession coefficient as: wherein t represents a time, T t represents a target confluence time, C t represents a target runoff recession coefficient, and θ represents a runoff surplus coefficient, and θ≦C t ; simplifying the third relationship function, determining a fourth relationship function between the target confluence time and the target runoff recession coefficient as: wherein log represents a logarithmic function.

[0154] The third data processing submodule is configured to obtain a fifth relationship function between a reference confluence time and a reference runoff recession coefficient, and determine a sixth relationship function between the reference confluence time and the reference runoff recession coefficient according to the third relationship function, wherein the reference confluence time is a confluence time corresponding to a reference time-invariant parameter.

[0155] For example, the third data processing submodule is configured to obtain a fifth relationship function between a reference confluence time and a reference runoff recession coefficient, and determine a sixth relationship function between the reference confluence time and the reference runoff recession coefficient according to the third relationship function, wherein the reference confluence time is a confluence time corresponding to a reference time-invariant parameter. simplifying the third relationship function, determining a fourth relationship function between the reference confluence time and the reference runoff recession coefficient as: wherein C r represents a reference runoff recession coefficient, T r represents a reference confluence time, and θ≦C r .

[0156] The fourth data processing submodule is configured to determine a first relationship function according to the second relationship function, the fourth relationship function, and the sixth relationship function.

[0157] For example, the fourth data processing submodule is configured to determine a first relationship function according to the second relationship function, the fourth relationship function, and the sixth relationship function.

[0158] The model construction module 303 is configured to construct a time-varying parameter reservoir model according to the first relationship function, a reference runoff recession coefficient, and a preset runoff depth and flow conversion coefficient. wherein Q t represents a flow at time t, Q t+1 represents a flow at time t+1, and U represents a preset runoff depth and flow conversion coefficient.

[0159] The flood forecasting module 304 is specifically configured to input the target time-varying parameter at any time into the time-varying parameter reservoir model to perform flood flow prediction based on the time-varying parameter reservoir model, and obtain a flood forecasting result.

[0160] For the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts are referred to the part of the method embodiment.

[0161] Please refer to Figure 4 Based on the same inventive concept, another embodiment of the present application provides an electronic device, which comprises a memory 401 and a processor 402. The memory 401 and the processor 402 complete mutual communication through a communication bus 403.

[0162] The memory 401 is configured to store code instructions.

[0163] The processor 402 is configured to execute the code instructions, so that the electronic device performs the CAN channel access authentication method provided in the embodiments of the present application.

[0164] The communication bus 403 mentioned above can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The communication bus 403 can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or only one type of bus. The communication interface is used for communication between the terminal and other devices. The memory 401 can include a random access memory (RAM) and can also include a non-volatile memory, for example, at least one disk memory. Optionally, the memory 401 can also be at least one storage device located away from the aforementioned processor 402.

[0165] The processor 402 described above can be a general processor, including a central processing unit (CPU), a network processor (NP), etc. It can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic, a discrete hardware component.

[0166] In addition, to achieve the above object, the embodiment of the present application further provides a computer readable storage medium, which has a computer program / instruction stored thereon, and the computer program / instruction is executed by a processor to implement the steps in the flood forecasting method based on time-varying parameters disclosed in the embodiment of the present application.

[0167] In addition, to achieve the above object, the embodiment of the present application further provides a computer program product, which is run on an electronic device, and when the computer program product is executed by a processor, the steps in the flood forecasting method based on time-varying parameters disclosed in the embodiment of the present application are implemented.

[0168] Each of the embodiments in the present specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0169] The embodiment of the present application is described with reference to the flowcharts and / or block diagrams of the method, device, electronic device and computer program product according to the embodiment of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing terminal equipment to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal equipment produce a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The means for implementing the functions specified in one flow or multiple flows and / or blocks Figure 1 The means for implementing the functions specified in one flow or multiple flows and / or blocks

[0170] These computer program instructions can also be stored in a computer readable storage medium, which can guide the computer or other programmable data processing terminal equipment to work in a specific way, so that the instructions stored in the computer readable storage medium produce a product including instruction means, which implements the functions specified in the flowcharts and / or block diagrams.Figure 1 one or more processes and / or blocks Figure 1 the function specified in the one or more blocks or one or more blocks.

[0171] These computer program instructions can also be loaded into computer or other programmable data processing terminal devices, so that a series of operational steps are performed on the computer or other programmable terminal devices to generate a computer-implemented process, thus the instructions executed on the computer or other programmable terminal devices provide a process for implementing the functions specified in the flowchart Figure 1 one or more processes and / or blocks Figure 1 the function specified in the one or more blocks or one or more blocks.

[0172] Although the preferred embodiments of the present application have been described, those skilled in the art who obtain the basic inventive concept can make additional changes and modifications to the embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present application.

[0173] Finally, it should also be noted that, in this document, relational terms such as first and second and the like can only be used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a list of elements does not only include those elements, but also includes other elements not explicitly listed or other elements inherent to such process, method, article or terminal device. Without more limitations, an element defined by the statement "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or terminal device including the element.

[0174] The above provides a kind of factor-oriented hierarchical relationship analysis method provided by the present application, detailed introduction is carried out, specific examples are applied in this paper to the principle and implementation mode of the present application are described, the above embodiment is only for helping to understand the method of the present application and its core idea;At the same time, for the general technical personnel of the field, according to the idea of the present application, there will be changes in specific implementation mode and application range, and the above-mentioned description should not be understood as the limitation of the present application.

Claims

1. A flood forecasting method based on time-varying parameters, characterized in that: include: Obtaining a target time-varying parameter and a preset reference time-invariant parameter; wherein the target time-varying parameter represents the runoff intensity in a certain period of time; According to the target time-varying parameter and the preset reference time-invariant parameter, determining the correlation relationship between the target runoff decay coefficient and the target time-varying parameter and the reference time-invariant parameter to obtain a first relationship function; wherein the target runoff decay coefficient represents the runoff decay coefficient corresponding to the time period corresponding to the target time-varying parameter; A time-varying parameter reservoir model is constructed based on the first relationship function, the reference runoff recession coefficient, and the preset runoff depth and flow conversion coefficient; wherein the time-varying parameter reservoir model represents a model for flood flow prediction based on the target time-varying parameter; the reference runoff recession coefficient is a preset runoff recession coefficient corresponding to the reference time-invariant parameter; Based on the time-varying parameter reservoir model, flood flow prediction based on the time-varying parameters is performed to obtain a flood forecast result.

2. The flood forecasting method based on time-varying parameters according to claim 1, characterized in that: The determining of the correlation between the target runoff decay coefficient and the target time-varying parameter and the reference time-invariant parameter based on the target time-varying parameter and the preset reference time-invariant parameter to obtain a first relationship function includes: Based on the target time-varying parameters, the target generalized wide and shallow open channel flow velocity is obtained; Based on the reference time-invariant parameters, a reference generalized wide and shallow open channel flow velocity is obtained; Determining, based on the target generalized wide-shallow open channel water flow velocity and the reference generalized wide-shallow open channel water flow velocity, a correlation relationship between the target confluence time and the target time-varying parameter and a preset reference time-invariant parameter to obtain a second relationship function; Obtaining a third relationship function between a target confluence time and a target runoff decay coefficient, and determining a fourth relationship function between the target confluence time and the target runoff decay coefficient based on the third relationship function; wherein the target confluence time is the confluence time corresponding to the target time-varying parameter; Obtaining a fifth relationship function between a reference confluence time and a reference runoff decay coefficient, and determining a sixth relationship function between the reference confluence time and the reference runoff decay coefficient based on the third relationship function; wherein the reference confluence time is the confluence time corresponding to the reference time-invariant parameter; A first relationship function is determined according to the second relationship function, the fourth relationship function, and the sixth relationship function.

3. The flood forecasting method based on time-varying parameters according to claim 2, characterized in that: The target generalized wide and shallow open channel flow velocity is obtained based on the target time-varying parameter using the Manning formula, including: Obtain the inherent riverbed slope parameters and roughness parameters of the target watershed; According to the inherent riverbed slope parameter, roughness parameter and target time-varying parameter of the target basin, the target generalized wide and shallow open channel flow velocity is obtained using the Manning formula: Among them, v t represents the target generalized wide and shallow open channel flow velocity, n represents the roughness parameter, i represents the riverbed slope parameter, r t represents the target time-varying parameter.

4. The flood forecasting method based on time-varying parameters according to claim 2, characterized in that: The step of obtaining a reference generalized wide and shallow open channel water flow velocity based on the reference time-invariant parameter includes: Obtain the inherent riverbed slope parameters and roughness parameters of the target watershed; According to the inherent riverbed slope parameter, roughness parameter and reference time-invariant parameter of the target basin, the reference generalized wide and shallow open channel flow velocity is obtained using the Manning formula: Among them, v I represents the reference generalized wide and shallow open channel flow velocity, I r represents the reference time-invariant parameter, n represents the roughness parameter, and i represents the riverbed slope parameter.

5. The flood forecasting method based on time-varying parameters according to claim 2, characterized in that: The second relationship function is obtained by determining the correlation between the target confluence time, the target time-varying parameter, and the preset reference time-invariant parameter based on the target generalized wide-shallow open channel water flow velocity and the reference generalized wide-shallow open channel water flow velocity, including: Determining a target confluence time based on the target generalized wide and shallow open channel flow velocity; Determining a reference confluence time based on the reference generalized wide and shallow open channel flow velocity; According to the target confluence time and the reference confluence time, the correlation between the target confluence time, the target time-varying parameter, and the preset reference time-invariant parameter is obtained, and the second relationship function is obtained as follows: Where L represents the fixed length of the watershed, v r represents the target generalized wide and shallow open channel flow velocity, v I represents the reference generalized wide and shallow open channel flow velocity, r t represents the target time-varying parameter, I r represents the reference time-invariant parameter, T r Indicates the reference confluence time, T t Indicates the target confluence time.

6. The flood forecasting method based on time-varying parameters according to claim 5, characterized in that: The obtaining of a third relationship function between the target confluence time and the target runoff decay coefficient, and determining a fourth relationship function between the target confluence time and the target runoff decay coefficient based on the third relationship function, includes: The third relationship function between the target confluence time and the target runoff decay coefficient is obtained as follows: Where t represents the time, T t Indicates the target confluence time, C t represents the target runoff recession coefficient, θ represents the runoff margin coefficient, and θ≦C t ; The third relationship function is simplified to determine the fourth relationship function between the target confluence time and the target runoff decay coefficient: Here, log represents the logarithmic function.

7. The flood forecasting method based on time-varying parameters according to claim 6, characterized in that: The obtaining of a fifth relationship function between the reference confluence time and the reference runoff decay coefficient, and determining a sixth relationship function between the reference confluence time and the reference runoff decay coefficient based on the third relationship function, includes: The fifth relationship function between the reference confluence time and the reference runoff decay coefficient is obtained as follows: The third relationship function is simplified to determine the fourth relationship function between the reference confluence time and the reference runoff decay coefficient: Among them, C r represents the reference runoff decay coefficient, T r Represents the reference confluence time, and θ≦C r .

8. The flood forecasting method based on time-varying parameters according to claim 7, characterized in that: The determining of the first relationship function according to the second relationship function, the fourth relationship function, and the sixth relationship function includes: The fourth relationship function and the sixth relationship function are input into the second relationship function to obtain the first relationship function:

9. The flood forecasting method based on time-varying parameters according to claim 8, characterized in that: The time-varying parameter reservoir model is constructed based on the first relationship function, the reference runoff decay coefficient, and the preset runoff depth and flow conversion coefficient: Among them, Q t represents the flow at time t, Q t+1 represents the flow rate at time t+1, and U represents the preset runoff depth and flow rate conversion coefficient.

10. The flood forecasting method based on time-varying parameters according to claim 1, characterized in that: The method of performing flood flow prediction based on the time-varying parameter reservoir model to obtain a flood forecast result includes: Based on the time-varying parameter reservoir model, the target time-varying parameter at any moment is input into the time-varying parameter reservoir model to perform flood flow prediction and obtain a flood forecast result.

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