A flood forecasting method based on time-varying parameters

Abstract

The application discloses a flood forecasting method based on time-varying parameters, and relates to the technical field of flood forecasting. The method comprises the following steps: determining the correlation between a target runoff recession coefficient and a target time-varying parameter and a preset reference time-invariant parameter by considering the target time-varying parameter and the reference time-invariant parameter; constructing a time-varying parameter reservoir model based on the correlation; and finally, performing flood forecasting based on the time-varying parameter reservoir model. The method can effectively consider the time-varying runoff intensity, and can analyze the whole system from a macro perspective, and can generalize the slope confluence process into a wide and shallow open channel flow process, so that the accuracy of flood forecasting is improved.

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. When applied to flood forecasting, the phenomenon of smaller flood peak and lag for large flood forecasting and larger flood peak and advance for small flood forecasting often occurs. 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 accounts for a large proportion of flood, which is relatively flat and the flood peak lags. Flood dominated by surface runoff is relatively sharp and the flood peak is advanced. 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 accuracy of flood forecasting. SUMMARY

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

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

[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] According to the target time-varying parameter and the preset reference time-invariant parameter, determining the correlation between the target runoff recession coefficient and the target time-varying parameter and the reference time-invariant parameter, and obtaining 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] According to the first relationship function, the reference runoff recession coefficient, and the preset runoff depth and flow conversion coefficient, a time-varying parameter reservoir model is constructed; 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; wherein 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; wherein 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, and includes:

[0017] The riverbed slope parameter and the roughness parameter inherent to 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 to the target basin, and the target time-varying parameter by using the Manning formula, and is:

[0019]

[0020] wherein, the target generalized wide and shallow open channel flow velocity is represented by V, the roughness parameter is represented by n, denotes a riverbed slope parameter, denotes a target time-varying parameter.

[0021] In a possible implementation, the reference generalized broad and shallow open channel flow velocity is obtained based on the reference time-invariant parameter, and the reference generalized broad 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 broad and shallow open channel flow velocity is obtained by using the Manning formula according to the riverbed slope parameter inherent to the target basin, the roughness parameter, and the reference time-invariant parameter.

[0024]

[0025] wherein, denotes the reference generalized broad and shallow open channel flow velocity, denotes the reference time-invariant parameter, denotes the roughness parameter, denotes the riverbed slope parameter.

[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 broad and shallow open channel flow velocity and the reference generalized broad and shallow open channel flow velocity, and the second relationship function comprises:

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

[0028] The reference concentration time is determined according to the reference generalized broad 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, denotes a fixed basin length, denotes the target generalized broad and shallow open channel flow velocity, denotes the reference generalized broad and shallow open channel flow velocity, denotes the target time-varying parameter, denotes the reference time-invariant parameter, denotes the reference concentration time, denotes the target concentration time.

[0032] In a possible implementation, the 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:

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

[0034]

[0035] wherein t represents a time point, represents the target concentration time, represents the target runoff recession coefficient, represents a runoff yield residual coefficient, and θ≦ .

[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, 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 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:

[0040] The fifth relationship function between the reference concentration time and the reference runoff recession coefficient is obtained 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, represents the reference runoff recession coefficient, represents the reference concentration time, and θ≦ .

[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 relation function and the sixth relation function are input into the second relation function to obtain the first relation function:

[0047] .

[0048] In one possible implementation, the construction of the time-varying parameter reservoir model based on the first relationship function, the reference runoff recession coefficient, and the preset runoff depth to flow conversion coefficient is as follows:

[0049]

[0050] in, This represents the flow rate at time t. This represents the flow rate at time t+1. This represents the preset runoff depth to flow rate conversion coefficient.

[0051] In one possible implementation, the step of performing flood discharge prediction based on the time-varying parameter reservoir model to obtain flood forecast results includes:

[0052] Based on the time-varying parameter reservoir model, the target time-varying parameters at any given time are input into the time-varying parameter reservoir model to predict flood flow and obtain flood forecast results.

[0053] Beneficial effects:

[0054] This application provides a flood forecasting method based on time-varying parameters. By considering target time-varying parameters and preset reference time-invariant parameters, the method determines the correlation between the target runoff recession coefficient and the target time-varying parameters and the reference time-invariant parameters. Based on this correlation, a time-varying parameter reservoir model is constructed. Finally, flood forecasting is performed based on this time-varying parameter reservoir model. This method can effectively consider time-varying runoff intensity, analyze the system as a whole from a macroscopic perspective, and generalize the slope confluence process into a wide and shallow open channel flow process, thereby improving the accuracy of flood forecasting. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a flowchart of a flood forecasting method based on time-varying parameters proposed in an embodiment of this application;

[0057] Figure 2This is a flowchart illustrating the process of obtaining a first relational function according to an embodiment of this application;

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

[0059] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application;

[0060] Explanation of reference numerals in the attached figures: 301-Parameter acquisition module, 302-Relationship function determination module, 303-Model construction module, 304-Flood forecasting module, 401-Memory, 402-Processor, 403-Communication bus. Detailed Implementation

[0061] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0062] In related technologies, linear time-invariant systems are generally used for flood forecasting. However, during the flood forecasting process, it is often encountered that the flood peak forecast for "major floods" is smaller and delayed, while the flood peak forecast for "minor floods" is larger and earlier, resulting in low accuracy of flood forecasts.

[0063] In view of this, this application proposes a flood forecasting method based on time-varying parameters. From a macroscopic perspective, the slope confluence process is generalized into a wide and shallow open channel flow process (referred to as a generalized wide and shallow open channel). The Manning formula is applied to analyze the relationship between the average confluence velocity and the time-varying parameters, and then the theoretical relationship between the linear reservoir parameters and the runoff intensity is derived, thereby improving the accuracy of flood forecasting.

[0064] Please refer to Figure 1 The flowchart below illustrates a flood forecasting method based on time-varying parameters, as provided in this application embodiment. The method includes:

[0065] S101. Obtain the target time-varying parameters and the preset reference time-invariant parameters; wherein, the target time-varying parameters characterize the flow intensity during a certain period;

[0066] The runoff process on slopes is affected by factors such as rainfall intensity and soil moisture content. The greater the rainfall intensity and soil moisture content, the faster the runoff tends to be, and vice versa. The greater the rainfall intensity and soil moisture content, the greater the runoff intensity. Therefore, this application uses runoff intensity as the target time-varying parameter to achieve flood flow prediction at different times.

[0067] The target time-varying parameter refers to a parameter that changes over time, while the reference time-invariant parameter is a preset, fixed parameter that can reflect the impact of changes in the target time-varying parameter.

[0068] S102. Based on the target time-varying parameters and the preset reference time-invariant parameters, determine the correlation between the target runoff recession coefficient and the target time-varying parameters and the reference time-invariant parameters, and obtain the first relationship function; wherein, the target runoff recession coefficient represents the runoff recession coefficient corresponding to the time period corresponding to the target time-varying parameters;

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

[0070] For example, we can first establish the relationship between the target time-varying parameter, the reference time-invariant parameter, the target runoff time, and the reference runoff time. Then, we can establish the relationship between the target runoff time and the target runoff recession coefficient, as well as the relationship between the reference runoff time and the reference runoff recession coefficient. After integrating these relationships, we can determine 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. This first relationship function is a nonlinear function affected by the time-varying parameter. By determining the target time-varying parameter in real time, we can determine the nonlinear influence of the target time-varying parameter on the flood discharge.

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

[0072] In related technologies, although time-varying parameters are used, they are applied linearly without considering the changes in the runoff recession coefficient under different time-varying parameters. Therefore, the embodiments of this application construct a time-varying parameter reservoir model based on a first relationship function, a reference runoff recession coefficient, and a preset runoff depth to flow conversion coefficient. This fully considers the nonlinear influence of the target time-varying parameters on the runoff recession coefficient, making flood forecasting more accurate.

[0073] S104. Based on the time-varying parameter reservoir model, flood flow prediction based on time-varying parameters is carried out to obtain flood forecast results.

[0074] For example, based on a time-varying parameter reservoir model, flood flow prediction based on time-varying parameters is performed to obtain flood forecast results, including: based on a time-varying parameter reservoir model, inputting the target time-varying parameters at any time into the time-varying parameter reservoir model to predict flood flow and obtain flood forecast results.

[0075] The target time-varying parameters can effectively reflect the dynamic influencing factors of rainfall. Therefore, by using a time-varying parameter reservoir model to perform nonlinear processing on the target time-varying parameters, the problem of inaccurate forecasts caused by linear non-time-varying processing can be effectively avoided, thereby improving the accuracy of flood forecasts.

[0076] The time-varying parameter reservoir model proposed in this application is constructed based on a linear reservoir model. Therefore, the linear reservoir model will be introduced first. This linear reservoir model can be: ;in, This represents the flow rate at time t+1. This represents the flow rate at time t. Let t represent the runoff intensity at time t, U represent the runoff depth to flow conversion coefficient, and C represent the runoff recession coefficient. The runoff recession coefficient is the only parameter in the linear reservoir model and is a sensitive parameter; the smaller the parameter, the shorter the runoff time, and vice versa. It can be seen that in related technologies, the runoff recession coefficient is set to a linear value. However, with different runoff intensities, the runoff time will also change to some extent, leading to inaccurate flood forecasts when using existing linear reservoir models for flood prediction.

[0077] like Figure 2 The diagram shown is a flowchart of obtaining the first relational function provided in an embodiment of this application. Figure 2 As shown, based on the target time-varying parameters and the preset reference time-invariant parameters, the correlation between the target runoff decline coefficient and the target time-varying parameters and the reference time-invariant parameters is determined, resulting in the first relationship function, which includes:

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

[0079] This application's embodiments analyze the overall process from a macroscopic perspective, generalizing the slope confluence process into a wide and shallow open channel flow process, referred to as a generalized wide and shallow open channel. Therefore, the Manning formula can be used to analyze the relationship between the average confluence velocity and the runoff intensity, thereby deriving the theoretical relationship between linear reservoir parameters and runoff intensity.

[0080] To account for the uneven spatial distribution of rainfall and underlying surface conditions, runoff generation and runoff calculations are often performed on a unit-by-unit basis. Taking a unit watershed as an example, this paper introduces a generalized wide-shallow open channel. The entire slope is generalized as a wide-shallow open channel. The channel width B is related to the unit watershed range; the riverbed gradient i is related to the average slope of the slope and does not change along the course; the channel roughness n is related to vegetation, soil type, land use, etc.; and the flow depth h is the runoff intensity r (time-period runoff). Compared with the river width, the water depth is very shallow, constituting a wide-shallow open channel flow. The flow velocity is described using the Manning formula as follows: Where v represents the water flow velocity, n represents the roughness parameter of the river channel, Rd is the hydraulic radius of the river channel, and J is the hydraulic gradient.

[0081] For example, based on the target time-varying parameters, the target generalized wide and shallow open channel flow velocity is obtained using the Manning formula, including:

[0082] Obtain the inherent riverbed slope parameters and roughness parameters of the target watershed;

[0083] Based on the inherent riverbed slope parameters, roughness parameters, and time-varying parameters of the target watershed, the Manning formula is used to obtain the target generalized wide and shallow open channel flow velocity as follows:

[0084]

[0085] in, This represents the target generalized velocity of water flow in a shallow open channel. Represents the roughness parameter. Indicates the riverbed slope parameters. Indicates the time-varying parameters of the target.

[0086] S202. Based on the reference time-invariant parameters, obtain the reference generalized wide and shallow open channel flow velocity;

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

[0088] For example, obtaining the reference generalized wide and shallow open channel flow velocity based on reference time-invariant parameters includes:

[0089] Obtain the inherent riverbed slope parameters and roughness parameters of the target watershed;

[0090] Based on the inherent riverbed slope parameters, roughness parameters, and reference time-invariant parameters of the target watershed, the reference generalized wide and shallow open channel flow velocity is obtained using the Manning formula:

[0091]

[0092] in, This refers to the velocity of water flow in a generalized wide and shallow open channel. Indicates a reference-invariant parameter. Represents the roughness parameter. This represents the riverbed slope parameters.

[0093] S203. Based on the target generalized wide and shallow open channel flow velocity and the reference generalized wide and shallow open channel flow velocity, determine the correlation between the target confluence time and the target time-varying parameter and the preset reference time-invariant parameter, and obtain the second relationship function.

[0094] Considering that roughness and riverbed slope are inherent properties of the watershed and relatively stable, runoff intensity is a time-varying factor, causing the slope runoff velocity to change over time. Therefore, runoff intensity is the main factor affecting the nonlinearity of slope runoff. Slope runoff velocity is positively correlated with soil moisture and rainfall intensity, which are the determining factors of runoff intensity. Therefore, based on the determination of the target generalized wide-shallow open channel flow velocity and the reference generalized wide-shallow open channel flow velocity, the runoff time for the same watershed segment can be determined, i.e., the target runoff time corresponding to the target generalized wide-shallow open channel flow velocity and the reference runoff time corresponding to the reference generalized wide-shallow open channel flow velocity. Dividing the target runoff time by the reference runoff time yields the correlation between the target runoff time and the target time-varying parameters and the preset reference time-invariant parameters.

[0095] S204. Obtain the third relationship function between the target runoff time and the target runoff recession coefficient, and determine the fourth relationship function between the target runoff time and the target runoff recession coefficient based on the third relationship function; wherein, the target runoff time is the runoff time corresponding to the target time-varying parameter;

[0096] The linear reservoir model essentially distributes runoff as a geometric series over time. Therefore, the runoff recession coefficients for each time period can be summed. When the confluence time is infinite, the summation value is 1. Based on this, a runoff margin coefficient can be introduced to make the relationship between the target confluence time and the target runoff recession coefficient solvable.

[0097] S205. Obtain the fifth relationship function between the reference runoff time and the reference runoff decline coefficient, and determine the sixth relationship function between the reference runoff time and the reference runoff decline coefficient based on the third relationship function; wherein, the reference runoff time is the runoff time corresponding to the reference time-invariant parameter;

[0098] The fifth relation function is obtained in the same way as the third relation function, and the sixth relation function is obtained in the same way as the fourth relation function, so it will not be described again here.

[0099] S206. Determine the first relation function based on the second relation function, the fourth relation function, and the sixth relation function.

[0100] Step S203 reveals that the second relational function is obtained by dividing the target confluence time by the reference confluence time. Therefore, it can be determined that the production flow margin coefficient will be eliminated during the calculation process, and the final first relational function does not contain the production flow margin coefficient, thus confirming that the method for obtaining the first relational function proposed in this application embodiment is accurate.

[0101] In one possible implementation, based on the target generalized wide-shallow open channel flow velocity and a reference generalized wide-shallow open channel flow velocity, the correlation between the target confluence time and the target time-varying parameter and a preset reference time-invariant parameter is determined, resulting in a second relationship function, including:

[0102] The target confluence time is determined based on the target generalized wide and shallow open channel flow velocity; the reference confluence time is determined based on the reference generalized wide and shallow open channel flow velocity.

[0103] For a watershed of fixed length, the confluence time can be determined if the water flow velocity is known. Therefore, if the target generalized wide and shallow open channel water flow velocity and the reference generalized wide and shallow open channel water flow velocity are known, a fixed watershed length can be used to obtain the target confluence time and the reference confluence time.

[0104] Based on the target convergence time and the reference convergence time, the correlation between the target convergence time and the target time-varying parameters and the preset reference time-invariant parameters is obtained, resulting in the second relationship function:

[0105]

[0106] in, Indicates a fixed watershed length. This represents the target generalized velocity of water flow in a shallow open channel. This refers to the velocity of water flow in a generalized wide and shallow open channel. Indicates the time-varying parameters of the target. Indicates a reference-invariant parameter. Indicates reference convergence time. Indicates the target convergence time.

[0107] The second relational function indicates the correlation between the target confluence time and the target time-varying parameters as well as the preset reference time-invariant parameters. Therefore, based on the second relational function, by determining the correlation between the runoff recession coefficient and the confluence time, the runoff recession coefficient that changes nonlinearly with the target time-varying parameters can be determined, thereby avoiding the problem of inaccurate prediction caused by using the existing linear runoff recession coefficient.

[0108] In one possible implementation, a third relationship function between the target runoff time and the target runoff decline coefficient is obtained, and a fourth relationship function between the target runoff time and the target runoff decline coefficient is determined based on the third relationship function, including:

[0109] The third relationship function between the target confluence time and the target runoff recession coefficient is:

[0110]

[0111] Where t represents time. Indicates the target convergence time. Indicates the target runoff recession coefficient. This represents the production run margin coefficient, and θ≦ ;

[0112] A linear reservoir actually distributes runoff over time according to a geometric series, with the proportions for each period being 1-C, (1-C)C, (1-C)C2, ..., (1-C)Cn... All proportions sum to 1, satisfying the water balance equation, i.e., the identity:

[0113]

[0114] Where lim represents finding the limit function, and T represents the confluence time.

[0115] Based on the above identity, it can be seen that the linear reservoir model only satisfies water balance and the confluence process is completed when t approaches infinity; therefore, the confluence time is infinite. This identity cannot effectively establish the correlation between the target confluence time and the target runoff recession coefficient. Therefore, this application's embodiment introduces a runoff margin coefficient to solve for the relationship; where runoff margin refers to the portion of runoff that has not yet completed confluence after a period of time, meaning the runoff rainfall remains within the watershed and has not passed through the watershed outlet section.

[0116] The third relation function can be transformed into:

[0117]

[0118] Based on the third relationship function after the above transformation, it can be seen that the runoff recession coefficient and the runoff margin coefficient have an exponential relationship. When the confluence time is constant, the larger the runoff recession coefficient is, the larger the runoff margin coefficient is, indicating that there is more runoff that has not been completed in the confluence process. That is, the larger the runoff recession coefficient is, the longer it takes to complete the confluence process.

[0119] Simplifying the third relationship function, the fourth relationship function between the target confluence time and the target runoff recession coefficient is determined as follows:

[0120]

[0121] in, Represents a logarithmic function.

[0122] As can be seen from the fourth relationship above, the runoff confluence time and the runoff margin coefficient have a logarithmic relationship. When the runoff recession coefficient is constant, the larger the runoff margin coefficient, the shorter the runoff confluence time. That is, the more runoff that does not complete confluence, the shorter the required confluence time. The above analysis is consistent with the general understanding of linear reservoir models and also proves the feasibility of the scheme described in the embodiments of this application.

[0123] In one possible implementation, a fifth relationship function between the reference confluence time and the reference runoff recession coefficient is obtained, and a sixth relationship function between the reference confluence time and the reference runoff recession coefficient is determined based on the third relationship function, including:

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

[0125]

[0126] Simplifying the third relationship function, the fourth relationship function between the reference confluence time and the reference runoff decay coefficient is determined as follows:

[0127]

[0128] in, This represents the reference runoff recession coefficient. This indicates the reference convergence time, and θ≦ .

[0129] The fifth relation function is obtained in the same way as the third relation function, and the sixth relation function is obtained in the same way as the fourth relation function, so it will not be described again here.

[0130] In one possible implementation, determining the first relation function based on the second relation function, the fourth relation function, and the sixth relation function includes:

[0131] Inputting the fourth and sixth relation functions into the second relation function yields the first relation function as follows:

[0132] .

[0133] The first relationship function provides a conversion formula for the runoff recession coefficient between different runoff intensities. This function shows that the greater the runoff intensity, the smaller the runoff recession coefficient, indicating a shorter confluence time, which is consistent with existing research results. Furthermore, the first relationship function proposed in this application is independent of the runoff margin coefficient. Therefore, for any runoff margin coefficient, corresponding to different runoff recession coefficients, as long as the above relationship is satisfied, the ratio of confluence times can satisfy the second relationship function.

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

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

[0136]

[0137] in, This represents the flow rate at time t. This represents the flow rate at time t+1. This represents the preset runoff depth to flow rate conversion coefficient.

[0138] In related technologies, the reference runoff recession coefficient is used. (Between 0 and 1) represents the average runoff recession coefficient for all floods in historical data, without considering the influence of runoff intensity. Generally, the greater the runoff intensity, the faster the confluence velocity, and the smaller the corresponding runoff recession coefficient. In flood processes, this manifests as a steeper flood peak and a shorter occurrence time. The time-varying parameter reservoir model proposed in this application takes into account the influence of runoff intensity on the runoff recession coefficient. The overall runoff recession coefficient is the target time-varying parameter. The larger, Also larger (of which) The average runoff intensity of all floods in historical data (which is a time-invariant parameter), therefore It is also bigger, and because Between 0 and 1, so The smaller the coefficient, the greater the runoff intensity, and the smaller the runoff recession coefficient, resulting in a steeper and more rapid flood peak, which is consistent with general understanding.

[0139] As can be seen from the time-varying parameter reservoir model provided in this application, the target time-varying parameters are used to nonlinearly adjust the runoff recession coefficient, so that the runoff recession coefficient can better match the actual situation, thereby improving the accuracy of flood forecasting.

[0140] Please refer toFigure 3 Based on the same inventive concept, embodiments of this application provide a flood forecasting device based on time-varying parameters, the device comprising:

[0141] The parameter acquisition module 301 is used to acquire target time-varying parameters and preset reference time-invariant parameters; wherein, the target time-varying parameters characterize the flow intensity during a certain period;

[0142] The relation function determination module 302 is used to determine the correlation between the target runoff decline coefficient and the target time-varying parameters and the reference time-invariant parameters based on the target time-varying parameters and the preset reference time-invariant parameters, and obtain a first relation function; wherein, the target runoff decline coefficient represents the runoff decline coefficient corresponding to the time period corresponding to the target time-varying parameters;

[0143] The model building module 303 is used to construct a time-varying parameter reservoir model based on the first relational function, the reference runoff recession coefficient, and the preset runoff depth to flow conversion coefficient; wherein, the time-varying parameter reservoir model represents a model for flood flow prediction based on target time-varying parameters; 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 used to predict flood flow based on the time-varying parameters of the reservoir model and obtain flood forecasting results.

[0145] In one possible implementation, the relation function determination module 302 includes a first speed acquisition submodule, a second speed acquisition submodule, a first data processing submodule, a second data processing submodule, a third data processing submodule, and a fourth data processing submodule.

[0146] The first velocity acquisition submodule is used to acquire the target generalized wide and shallow open channel flow velocity based on the target time-varying parameters;

[0147] For example, based on the target time-varying parameters, the target generalized wide-shallow open channel flow velocity is obtained using the Manning formula, including: obtaining the inherent riverbed slope parameters and roughness parameters of the target watershed; and obtaining the target generalized wide-shallow open channel flow velocity using the Manning formula based on the inherent riverbed slope parameters, roughness parameters, and the target time-varying parameters of the target watershed. ;in, This represents the target generalized velocity of water flow in a shallow open channel. Represents the roughness parameter. Indicates the riverbed slope parameters. Indicates the time-varying parameters of the target.

[0148] The second velocity acquisition submodule is used to acquire the reference generalized wide and shallow open channel flow velocity based on the reference time-invariant parameters.

[0149] For example, obtaining the reference generalized wide and shallow open channel flow velocity based on the reference time-invariant parameters includes: obtaining the inherent riverbed slope parameters and roughness parameters of the target watershed; and using the Manning formula to obtain the reference generalized wide and shallow open channel flow velocity based on the inherent riverbed slope parameters, roughness parameters, and the reference time-invariant parameters of the target watershed. ;in, This refers to the velocity of water flow in a generalized wide and shallow open channel. Indicates a reference-invariant parameter. Represents the roughness parameter. This represents the riverbed slope parameters.

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

[0151] For example, the step of determining the correlation between the target confluence time and the target time-varying parameter and the preset reference time-invariant parameter based on the target generalized wide-shallow open channel flow velocity and the reference generalized wide-shallow open channel flow velocity, to obtain the second relational function, includes: determining the target confluence time based on the target generalized wide-shallow open channel flow velocity; determining the reference confluence time based on the reference generalized wide-shallow open channel flow velocity; and obtaining the correlation between the target confluence time and the target time-varying parameter and the preset reference time-invariant parameter based on the target confluence time and the reference confluence time, to obtain the second relational function as follows:

[0152]

[0153] in, Indicates a fixed watershed length. This represents the target generalized velocity of water flow in a shallow open channel. This refers to the velocity of water flow in a generalized wide and shallow open channel. Indicates the time-varying parameters of the target. Indicates a reference-invariant parameter. Indicates reference convergence time. Indicates the target convergence time.

[0154] The second data processing submodule is used to obtain a third relationship function between the target runoff time and the target runoff decline coefficient, and to determine a fourth relationship function between the target runoff time and the target runoff decline coefficient based on the third relationship function; wherein, the target runoff time is the runoff time corresponding to the target time-varying parameter;

[0155] For example, obtaining the third relationship function between the target runoff time and the target runoff decline coefficient, and determining the fourth relationship function between the target runoff time and the target runoff decline coefficient based on the third relationship function, includes: obtaining the third relationship function between the target runoff time and the target runoff decline coefficient as follows: Where t represents time, Indicates the target convergence time. Indicates the target runoff recession coefficient. This represents the production run margin coefficient, and θ≦ Simplifying the third relationship function, the fourth relationship function between the target confluence time and the target runoff recession coefficient is determined as follows: ;in, Represents a logarithmic function.

[0156] The third data processing submodule is used to obtain the fifth relationship function between the reference confluence time and the reference runoff decline coefficient, and to determine the sixth relationship function between the reference confluence time and the reference runoff decline coefficient based on the third relationship function; wherein, the reference confluence time is the confluence time corresponding to the reference time-invariant parameter;

[0157] For example, obtaining the fifth relationship function between the reference confluence time and the reference runoff decline coefficient, and determining the sixth relationship function between the reference confluence time and the reference runoff decline coefficient based on the third relationship function, includes: obtaining the fifth relationship function between the reference confluence time and the reference runoff decline coefficient as follows: Simplifying the third relationship function, the fourth relationship function between the reference confluence time and the reference runoff decline coefficient is determined as follows: ;in, This represents the reference runoff recession coefficient. This indicates the reference convergence time, and θ≦ .

[0158] The fourth data processing submodule is used to determine the first relation function based on the second relation function, the fourth relation function, and the sixth relation function.

[0159] For example, determining the first relation function based on the second relation function, the fourth relation function, and the sixth relation function includes: inputting the fourth relation function and the sixth relation function into the second relation function to obtain the first relation function as follows: .

[0160] Model building module 303 is specifically used to construct a time-varying parameter reservoir model based on the first relational function, the reference runoff recession coefficient, and the preset runoff depth to flow conversion coefficient. ;in, This represents the flow rate at time t. This represents the flow rate at time t+1. This represents the preset runoff depth to flow rate conversion coefficient.

[0161] The flood forecasting module 304 is specifically used to input the target time-varying parameters at any time into the time-varying parameter reservoir model based on the time-varying parameter reservoir model to predict flood flow and obtain flood forecasting results.

[0162] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0163] Please refer to Figure 4 Based on the same inventive concept, another embodiment of this application provides an electronic device, which includes a memory 401 and a processor 402. The memory 401 and the processor 402 communicate with each other via a communication bus 403.

[0164] Memory 401 is used to store code instructions.

[0165] The processor 402 is used to run code instructions, causing the electronic device to execute the CAN channel access authentication method provided in the embodiments of this application.

[0166] The aforementioned communication bus 403 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus 403 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is used to represent it in the figure, but this does not indicate that there is only one bus or one type of bus. The communication interface is used for communication between the aforementioned terminal and other devices. The memory 401 can include random access memory (RAM), or it can include non-volatile memory, such as at least one disk storage device. Optionally, the memory 401 can also be at least one storage device located remotely from the aforementioned processor 402.

[0167] The processor 402 mentioned above can be a general-purpose 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 devices, discrete gate or transistor logic devices, or discrete hardware components.

[0168] In addition, to achieve the above objectives, embodiments of this application also provide a computer-readable storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements the steps in a flood forecasting method based on time-varying parameters as disclosed in embodiments of this application.

[0169] In addition, to achieve the above objectives, this application also provides a computer program product that, when run on an electronic device, enables the processor to execute the steps of a flood forecasting method based on time-varying parameters as disclosed in this application.

[0170] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0171] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, electronic devices, and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0172] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a processFigure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0173] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0174] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0175] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0176] The above provides a detailed description of the hierarchical relationship analysis method for influencing factors provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A flood forecasting method based on time-varying parameters, characterized in that, include: Obtain the target time-varying parameters and the preset reference time-varying parameters; wherein, the target time-varying parameters characterize the flow intensity during a certain period; Based on the target time-varying parameters and the preset reference time-invariant parameters, the correlation between the target runoff decline coefficient and the target time-varying parameters and the reference time-invariant parameters is determined, resulting in a first relationship function; wherein, the target runoff decline coefficient characterizes the runoff decline coefficient corresponding to the time period corresponding to the target time-varying parameters, including: 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, the reference generalized wide and shallow open channel flow velocity is obtained; according to the target generalized wide and shallow open channel flow velocity and the reference generalized wide and shallow open channel flow velocity, the correlation between the target confluence time and the target time-varying parameters and the preset reference time-invariant parameters is determined, resulting in a second relationship function; a third relationship function between the target confluence time and the target runoff recession coefficient is obtained, and according to the third relationship function, a fourth relationship function between the target confluence time and the target runoff recession coefficient is determined; wherein, the target confluence time is the confluence time corresponding to the target time-varying parameters; a fifth relationship function between the reference confluence time and the reference runoff recession coefficient is obtained, and according to the third relationship function, a sixth relationship function between the reference confluence time and the reference runoff recession coefficient is determined; wherein, the reference confluence time is the confluence time corresponding to the reference time-invariant parameters; a first relationship function is determined based on the second relationship function, the fourth relationship function, and the sixth relationship function. A time-varying parameter reservoir model is constructed based on the first relationship function, the reference runoff recession coefficient, and the preset runoff depth to flow conversion coefficient; wherein, the time-varying parameter reservoir model represents a model for flood flow prediction based on target time-varying parameters; the reference runoff recession coefficient is the preset runoff recession coefficient corresponding to the reference time-invariant parameter; Based on the time-varying parameter reservoir model, flood flow prediction based on time-varying parameters is performed to obtain flood forecast results.

2. The flood forecasting method based on time-varying parameters according to claim 1, characterized in that, The step of obtaining the target generalized wide and shallow open channel flow velocity based on the target time-varying parameters and using the Manning formula includes: Obtain the inherent riverbed slope parameters and roughness parameters of the target watershed; Based on the inherent riverbed slope parameters, roughness parameters, and time-varying parameters of the target watershed, the target generalized wide and shallow open channel flow velocity is obtained using the Manning formula: in, This represents the target generalized velocity of water flow in a shallow open channel. Represents the roughness parameter. Indicates the riverbed slope parameters. Indicates the time-varying parameters of the target.

3. The flood forecasting method based on time-varying parameters according to claim 1, characterized in that, The step of obtaining the reference generalized wide and shallow open channel flow velocity based on the reference time-invariant parameter includes: Obtain the inherent riverbed slope parameters and roughness parameters of the target watershed; Based on the inherent riverbed slope parameters, roughness parameters, and reference time-invariant parameters of the target watershed, the reference generalized wide and shallow open channel flow velocity is obtained using the Manning formula: in, This refers to the velocity of water flow in a generalized wide and shallow open channel. Indicates a reference-invariant parameter. Represents the roughness parameter. This represents the riverbed slope parameters.

4. The flood forecasting method based on time-varying parameters according to claim 1, characterized in that, The second relationship function is obtained by determining the correlation between the target confluence time and the target time-varying parameters and the preset reference time-invariant parameters based on the target generalized wide and shallow open channel flow velocity and the reference generalized wide and shallow open channel flow velocity, including: The target confluence time is determined based on the target generalized wide and shallow open channel water flow velocity. The reference confluence time is determined based on the reference generalized wide and shallow open channel water flow velocity; Based on the target convergence time and the reference convergence time, the correlation between the target convergence time, the target time-varying parameter, and the preset reference time-invariant parameter is obtained, resulting in the second relationship function: in, Indicates a fixed watershed length. This represents the target generalized velocity of water flow in a shallow open channel. This refers to the velocity of water flow in a generalized wide and shallow open channel. Indicates the time-varying parameters of the target. Indicates a reference-invariant parameter. Indicates reference convergence time. Indicates the target convergence time.

5. The flood forecasting method based on time-varying parameters according to claim 4, characterized in that, The process of obtaining the third relationship function between the target runoff time and the target runoff decline coefficient, and determining the fourth relationship function between the target runoff time and the target runoff decline coefficient based on the third relationship function, includes: The third relationship function between the target confluence time and the target runoff recession coefficient is as follows: Where t represents time. Indicates the target convergence time. Indicates the target runoff recession coefficient. This represents the production run margin coefficient, and θ≦ ; Simplifying the third relationship function, the fourth relationship function between the target confluence time and the target runoff recession coefficient is determined as follows: in, Represents a logarithmic function.

6. The flood forecasting method based on time-varying parameters according to claim 5, characterized in that, The process of obtaining the fifth relationship function between the reference confluence time and the reference runoff decline coefficient, and determining the sixth relationship function between the reference confluence time and the reference runoff decline coefficient based on the third relationship function, includes: The fifth relationship function between the reference confluence time and the reference runoff decline coefficient is obtained as follows: Simplifying the third relationship function, the fourth relationship function between the reference confluence time and the reference runoff decline coefficient is determined as follows: in, This represents the reference runoff recession coefficient. Indicates the reference convergence time, and θ≦ .

7. The flood forecasting method based on time-varying parameters according to claim 6, characterized in that, The step of determining the first relation function based on the second relation function, the fourth relation function, and the sixth relation function includes: The fourth relation function and the sixth relation function are input into the second relation function to obtain the first relation function: 。 8. The flood forecasting method based on time-varying parameters according to claim 7, characterized in that, The time-varying parameter reservoir model is constructed based on the first relationship function, the reference runoff recession coefficient, and the preset runoff depth to flow conversion coefficient as follows: in, This represents the flow rate at time t. This represents the flow rate at time t+1. This represents the preset runoff depth to flow rate conversion coefficient.

9. The flood forecasting method based on time-varying parameters according to claim 1, characterized in that, The process of predicting flood flow based on time-varying parameters using the time-varying parameter reservoir model to obtain flood forecast results includes: Based on the time-varying parameter reservoir model, the target time-varying parameters at any given time are input into the time-varying parameter reservoir model to predict flood flow and obtain flood forecast results.

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