A method and system for constructing a prediction model for the delay duration of sand peaks in floods with delayed sand peaks.

Abstract

This invention relates to the field of water conservancy and hydropower engineering technology, and discloses a method and system for constructing a prediction model for the lag time of sand peaks in floods with delayed sand peaks. The method includes: collecting measured data of floods with delayed sand peaks, and calculating the lag time T of the flood sand peak, in which the sand peak appears later than the flood peak. z Calculate the average sediment concentration, sediment inflow coefficient ζ, and sediment type coefficient η of the flood average flow; analyze the relationship between the flood duration T, sediment inflow coefficient ζ, sediment type coefficient η, and sediment peak lag time T. z This invention establishes a predictive model for the lag duration of sand peaks in delayed-type floods, based on the constructed model. This model can accurately predict the lag duration of delayed-type floods, thereby improving the accuracy of sand peak lag duration prediction and providing a reference for reservoir sediment discharge scheduling and flood control decisions.

Description

Technical Field

[0001] This invention relates to the field of water conservancy and hydropower engineering technology, and in particular to a method and system for constructing a prediction model for the delay duration of sand peaks in floods with delayed sand peaks. Background Technology

[0002] Floods in which the sediment peak lags behind the flood peak are called sediment-lagging floods. Before the flood peak arrives, the floodwaters violently scour the river channel, but once the flow begins to recede, the sediment load increases sharply. This phenomenon easily leads to riverbed siltation, further exacerbating flood control pressure in downstream areas and posing a significant challenge to downstream flood control efforts. Therefore, studying the sediment peak lag time in sediment-lagging floods is of great significance for guiding reservoir sediment discharge scheduling and flood control decision-making.

[0003] Domestic and international calculations of sand peak lag duration mainly rely on the peak occurrence method and mathematical modeling. The peak occurrence method can only provide a rough estimate of the duration and cannot reflect the overall change in sediment concentration during floods. While mathematical modeling can accurately calculate the sand peak lag duration, it requires a large number of parameters. This invention calculates the sand peak lag duration of floods with lag characteristics based on measured flood data. It analyzes the correlation between flood duration, sediment inflow coefficient, sand type coefficient, and sand peak lag duration using a multivariate nonlinear regression strategy, and constructs a prediction model for the sand peak lag duration of such floods. The established model can predict the sand peak lag duration of floods with lag characteristics with relatively high accuracy. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the aforementioned existing problems, this invention is proposed. Therefore, this invention provides a method for constructing a prediction model for the lag duration of sand peaks in floods with delayed sand peaks, thus solving the problem of how to predict the lag duration of sand peaks in floods with delayed sand peaks.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for constructing a prediction model for the lag duration of sand peaks in floods with lag time, comprising:

[0008] Collect measured data on floods with delayed sand peaks, and calculate the sand peak lag time T during the flood process, where the sand peak appears later than the flood peak. z ;

[0009] Based on the measured data of the sand peak delayed flood, the average flood discharge was calculated. Average sand content The sand inflow coefficient ζ and the sand type coefficient η;

[0010] The analysis included the flood duration T, sediment inflow coefficient ζ, sediment type coefficient η, and sediment peak lag time T. z The relationship between them was investigated, and a prediction model for the delay time of sand peaks in sand peak-delayed floods was constructed.

[0011] As a preferred embodiment of the method for constructing the sand peak lag duration prediction model for sand peak-lagging floods described in this invention, the method includes: collecting measured data of the sand peak-lagging flood and calculating the sand peak lag duration T. z include:

[0012] The measured data of the delayed-type flood with sand peaks include the daily average flow rate, daily average sediment content, and flood duration T.

[0013] Based on the measured daily average flow and daily average sediment concentration from the aforementioned data on floods with delayed sand peaks, the sand peak delay time T of the flood with delayed sand peaks is calculated. z. ,include:

[0014] The time of sand peak appearance minus the time of flood peak appearance is the sand peak lag time. If multiple sand peaks appear during a flood, the lag time is the time difference between the sand peak closest to the flood peak.

[0015] As a preferred embodiment of the method for constructing the sand peak lag duration prediction model for sand peak-lagging floods described in this invention, the calculation based on the measured data of the sand peak-lagging floods includes:

[0016] The average flood discharge is calculated based on the average daily flow, average daily sediment concentration, and flood duration T. Average sand content And based on the average flood flow and average sediment content Calculate the sand inflow coefficient ζ and sand type coefficient η;

[0017] Calculate the average flood discharge Represented as:

[0018]

[0019] in, Q represents the average flood discharge, Q1 is the average daily discharge on the first day of the flood, and Q T Let T be the average daily flow rate on day T of the flood, and T be the duration of the flood.

[0020] As a preferred embodiment of the method for constructing the sand peak lag duration prediction model for sand peak-lagging floods described in this invention, the method includes: calculating the average sediment concentration. Represented as:

[0021]

[0022] in, S represents the average sediment content of the flood, S1 is the average daily flow on the first day of the flood, and S T Let T be the average daily flow rate on day T of the flood, and T be the duration of the flood.

[0023] In a preferred embodiment of the method for constructing the sand peak lag duration prediction model for sand peak-lagging floods as described in this invention, the sand inflow coefficient ζ is calculated as follows:

[0024]

[0025] Where ζ represents the sediment inflow coefficient of a flood event. This indicates the average sediment content of a flood event. This indicates the average flow rate of a flood event.

[0026] In a preferred embodiment of the method for constructing the sand peak lag duration prediction model for sand peak-lagging floods as described in this invention, the sand type coefficient η is calculated as follows:

[0027]

[0028] Where η represents the sand type coefficient of the flood, S max Indicates the maximum sediment content of the flood. This indicates the average sediment content of a flood.

[0029] As a preferred embodiment of the method for constructing the prediction model for the lag duration of sand peaks in lag-type floods according to the present invention, the method for constructing the prediction model for the lag duration of sand peaks in lag-type floods includes:

[0030] Using flood duration T, sediment inflow coefficient ζ, and sediment type coefficient η as independent variables, and sediment peak lag time as the dependent variable, a sediment peak lag time prediction model for sediment peak lag type floods is constructed, expressed as:

[0031] T z =kT a ζ b η c

[0032] Where k, a, b, and c represent coefficients;

[0033] Nonlinear fitting is performed based on the flood duration, sediment inflow coefficient, sediment type coefficient, and sediment peak lag time. The values ​​of the coefficients are determined according to the performance patterns of the independent and dependent variables.

[0034] Secondly, this invention provides a system for constructing a prediction model for the lag duration of sand peaks in floods with lag time, comprising:

[0035] The data collection module is used to collect measured data on floods with delayed sand peaks and to calculate the sand peak lag time T during the flood process, where the sand peak appears later than the flood peak. z ;

[0036] The calculation module is used to calculate the average flood discharge based on the measured data of the sand peak lagging type flood. Average sand content The sand inflow coefficient ζ and the sand type coefficient η;

[0037] The module is used to analyze the flood duration T, sediment inflow coefficient ζ, sediment type coefficient η, and sediment peak lag time T. z The relationship between them was investigated, and a prediction model for the delay time of sand peaks in sand peak-delayed floods was constructed.

[0038] Thirdly, the present invention provides an electronic device, comprising:

[0039] Memory and processor;

[0040] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for constructing the prediction model of the sand peak lag duration of the sand peak lag type flood are implemented.

[0041] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for constructing the prediction model for the peak delay duration of the sand peak in a flood.

[0042] Compared with existing technologies, the beneficial effects of this invention are as follows: Based on measured data of delayed-peak floods from hydrological stations, this invention calculates the delay duration of the sand peak in delayed-peak floods; then, based on flood data, it calculates the average flow, average sediment concentration, sediment inflow coefficient, and sand type coefficient, analyzes the correlation between flood duration, sediment inflow coefficient, sand type coefficient, and delay duration of the sand peak, and then constructs a prediction model for the delay duration of delayed-peak floods. The established model can predict the delay duration of delayed-peak floods more accurately, thereby improving the accuracy of predicting the delay duration of the sand peak and providing a reference for reservoir sediment discharge scheduling and flood control decisions. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0044] Figure 1 This is a schematic diagram of the overall process for constructing a sand peak lag duration prediction model for sand peak lag type floods according to an embodiment of the present invention;

[0045] Figure 2 This is a schematic diagram showing the correlation between the calculated and measured values ​​of the sand peak lag duration prediction model for sand peak lag type floods at the Huayuankou station in the lower reaches of the Yellow River from 1989 to 2017, according to an embodiment of the present invention.

[0046] Figure 3 This is a schematic diagram comparing the calculated and measured values ​​of the sand peak delay duration prediction model for sand peak delay-type floods according to an embodiment of the present invention. Detailed Implementation

[0047] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0048] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0050] Example 1

[0051] Reference Figure 1-2 As an embodiment of the present invention, a method for constructing a prediction model for the lag duration of sand peaks in floods with lag peaks is provided, comprising:

[0052] S100: Collect measured data on floods with delayed sand peaks, and calculate the sand peak lag time T during the flood process, where the sand peak appears later than the flood peak. z ;

[0053] Specifically, the measured data of delayed sand peak floods include the daily average flow, daily average sediment concentration, and flood duration T. In this embodiment, measured data of 51 delayed sand peak floods were collected at the Huayuankou station in the wandering section of the lower Yellow River from 1989 to 2017, including the daily average flow, daily average sediment concentration, and flood duration of each flood.

[0054] Furthermore, the lag time T of the sand peak in a sand peak-lagging flood is calculated. z. include:

[0055] The maximum daily average flow during a flood is the flood peak. The maximum daily average sediment content during a complete sand peak process (including the sand rising section, the peak section, and the sand falling section) is the sand peak. The time of sand peak occurrence minus the time of flood peak occurrence is the sand peak lag time. If multiple sand peaks occur during a flood, the lag time is the time difference between the sand peak closest to the flood peak.

[0056] It should be noted that this step is used for data collection and basic calculations. It mainly involves collecting relevant data from actual sand peak lag-type flood events, including the average daily flow and average daily sediment concentration during the flood process. These data are then used to calculate the specific lag time of the sand peak relative to the flood peak. The lag time is a key parameter for building the subsequent prediction model, reflecting the dynamic relationship between flood flow and sediment movement.

[0057] S200, based on measured data of delayed-type floods, calculates the average flood discharge. Average sand content The sand inflow coefficient ζ and the sand type coefficient η;

[0058] First, based on the average daily flow, average daily sediment content, and duration of each flood, calculate the average flow of each flood. and average sediment content

[0059] Among them, the average flood discharge is calculated. Represented as:

[0060]

[0061] in, The average flood discharge is expressed in cubic meters per second (m³). 3 / s, Q1 is the average daily flow rate on the first day of the flood, unit: m³ / s. 3 / s, Q T The average daily flow rate on day T of the flood, in cubic meters per second (m³). 3 / s, T is the duration of the flood, in days (d);

[0062] Calculate the average sediment content Represented as:

[0063]

[0064] in, This indicates the average sediment content of floodwater, in kg / m³. 3 S1 represents the average daily flow rate on the first day of the flood, in kg / m³. 3 S T The average daily flow rate on day T of the flood, in kg / m³. 3 T represents the duration of the flood, in days (d).

[0065] Based on the average flow rate of each flood and average sediment content Calculate the sediment inflow coefficient ζ and sediment type coefficient η for each flood, where the sediment inflow coefficient ζ is expressed as:

[0066]

[0067] Where ζ represents the sediment coefficient of the flood. This indicates the average sediment content of a flood event, in kg / m³. 3 , The average flow rate of a flood event is expressed in cubic meters per second (m³). 3 / s.

[0068] The formula for calculating the sand type coefficient η is:

[0069]

[0070] Where η represents the sand type coefficient of the flood, S max This indicates the daily average maximum sediment concentration of a flood, in kg / m³. 3 , The average sediment content of a flood, expressed in kg / m³. 3 .

[0071] It should be noted that this step involves extracting characteristic parameters. From the measured data, important characteristic parameters such as the average flow rate, average sediment concentration, sediment inflow coefficient, and sediment type coefficient of the flood are further extracted and calculated. This helps to deepen the understanding of flood characteristics and sediment movement mechanisms and serves as an important basis for input variables when constructing prediction models. The sediment inflow coefficient and sediment type coefficient reflect the ability of the incoming water to carry sediment and its impact on the lag time.

[0072] S300, analyze flood duration T, sediment inflow coefficient ζ, sediment type coefficient η, and sediment peak lag time T. z The relationship between them was investigated, and a prediction model for the delay time of sand peaks in sand peak-delayed floods was constructed.

[0073] Specifically, in this embodiment, measured flood data from the Huayuankou station in the wandering section of the lower Yellow River from 1989 to 2017 were collected, including the average daily flow, average daily sediment content, and flood duration for each flood. The sediment peak lag time of 51 sediment peak lag-type floods was calculated and statistically analyzed.

[0074] Based on the above statistics of the average daily flow, average daily sediment concentration, and flood duration of each flood, the average flow Q and average sediment concentration S of each flood are calculated. Then, based on the average flow Q, average sediment concentration S, and flood data, the sediment inflow coefficient ζ and sediment type coefficient η of each flood are calculated.

[0075] Using flood duration T, sediment inflow coefficient ζ, and sediment type coefficient η as independent variables, and sediment peak lag time as the dependent variable, a sediment peak lag time prediction model for sediment peak lag type floods is constructed, expressed as:

[0076] T z =kT a ζ b η c

[0077] Where k, a, b, and c represent coefficients;

[0078] Nonlinear fitting was performed based on flood duration, sediment inflow coefficient, sediment type coefficient, and sediment peak lag time. The values ​​of the coefficients were determined based on the performance patterns of the independent and dependent variables.

[0079] It should be noted that after obtaining the data and parameters from the first two steps, the analysis is performed on the flood duration T, sediment inflow coefficient, sediment type coefficient, and sediment peak lag time T. z By studying the intrinsic connections between these factors, statistical or nonlinear fitting methods are used to construct a predictive model, revealing how each factor works together to influence the lag time of sand peaks, enabling rapid prediction of sand peak lag time based on new flood characteristics.

[0080] This invention calculates the lag duration of sand peaks in floods with delayed sand peaks based on measured data from hydrological stations. Then, it calculates the average flow, average sediment concentration, sediment inflow coefficient, and sand type coefficient based on flood data, analyzes the correlation between flood duration, sediment inflow coefficient, sand type coefficient, and lag duration, and constructs a prediction model for the lag duration of sand peaks in floods with delayed sand peaks. The established model can predict the lag duration of sand peaks in floods with delayed sand peaks relatively accurately, thereby improving the accuracy of predicting the lag duration and providing a reference for reservoir sediment discharge scheduling and flood control decisions.

[0081] The above is an illustrative scheme for constructing a sand peak lag duration prediction model for a sand peak-lagging flood according to this embodiment. It should be noted that the technical solution of the sand peak lag duration prediction model construction system for this sand peak-lagging flood belongs to the same concept as the technical solution of the sand peak lag duration prediction model construction method described above. Details not described in detail in the technical solution of the sand peak lag duration prediction model construction system for this embodiment can be found in the description of the technical solution of the sand peak lag duration prediction model construction method described above.

[0082] The system for constructing the prediction model for the delay duration of sand peaks in delayed-type floods in this embodiment includes:

[0083] The data collection module is used to collect measured data on floods with delayed sand peaks and to calculate the sand peak lag time T during the flood process, where the sand peak appears later than the flood peak. z ;

[0084] The calculation module is used to calculate the average flood discharge based on measured data of sand peak lagging type floods. Average sand content The sand inflow coefficient ζ and the sand type coefficient η;

[0085] The module is used to analyze flood duration T, sediment inflow coefficient ζ, sediment type coefficient η, and sediment peak lag time T. z The relationship between them was investigated, and a prediction model for the delay time of sand peaks in sand peak-delayed floods was constructed.

[0086] This embodiment also provides an electronic device suitable for constructing a prediction model for the delay duration of sand peaks in floods with delayed sand peaks, including:

[0087] The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement the method for constructing a prediction model for the delay duration of sand peaks in floods with delay as proposed in the above embodiments.

[0088] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for constructing a prediction model for the delay duration of sand peaks in floods with delay as proposed in the above embodiments.

[0089] The storage medium proposed in this embodiment and the method for constructing a prediction model for the delay duration of sand peaks in floods proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0090] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0091] Example 2

[0092] Reference Figure 2-3 As an embodiment of the present invention, a method for constructing a prediction model for the delay duration of sand peaks in floods with delay is provided. To verify its beneficial effects, scientific demonstration is carried out through economic benefit calculations and simulation experiments.

[0093] This embodiment provides a verification test of a method for constructing a prediction model for the lag duration of sand peaks in delayed-type floods. The method is based on the duration, sediment inflow coefficient, and sand type coefficient of floods at the Huayuankou hydrological station in the lower reaches of the Yellow River from 1989 to 2017, and the calculated lag duration of sand peaks in delayed-type floods. Using the calculated lag duration as the measured value, a multivariate nonlinear regression strategy is employed to calibrate the parameters k, a, b, and c in the prediction model. The calibrated values ​​for k, a, b, and c are 0.029, 1.453, -0.082, and 0.395, respectively. The model is then validated using data from delayed-type floods from 2018 to 2020.

[0094] The calibration and validation results of the model show that: Figure 2 The correlation coefficient between the duration of the flood, the sediment inflow coefficient, the sediment type coefficient and the sediment peak lag time is 0.77. Therefore, the constructed model can predict the sediment peak lag time of the sediment peak lag type flood at Huayuankou Station in the wandering section of the lower Yellow River relatively well.

[0095] To verify the empirical formula, Figure 3 The model-calculated and measured values ​​of the sand peak lag time at Huayuankou Station in the wandering section of the lower Yellow River are given. As can be seen from the figure, the model-calculated value and the measured value of the sand peak lag time of the sand peak lag type flood at Huayuankou Station in the wandering section of the lower Yellow River are basically consistent.

[0096] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for constructing a prediction model for the delay duration of sand peaks in floods with delayed sand peaks, characterized in that, include: Collect measured data on floods with delayed sand peaks, and calculate the sand peak lag time T during the flood process, where the sand peak appears later than the flood peak. z ; Based on the measured data of the delayed-type flood with sand peak, the average flow rate of the flood event was calculated. Average sediment content of each flood The sand inflow coefficient ζ and the sand type coefficient η; Analysis of flood duration T, sediment inflow coefficient ζ, sediment type coefficient η, and sediment peak lag time T z The relationship between them was investigated, and a prediction model for the delay time of sand peaks in sand peak-delayed floods was constructed. The calculated zeta coefficient ζ is expressed as: Where ζ represents the zeta coefficient. This indicates the average sediment content of a flood event. This indicates the average flow rate of a flood event; The sand type coefficient η is calculated as follows: Where η represents the sand type coefficient, S max Indicates the maximum sediment content of a flood. This indicates the average sediment content of the flood. The prediction model for the delay time of the sand peak in the aforementioned sand peak-delayed flood includes: Using flood duration T, sediment inflow coefficient ζ, and sediment type coefficient η as independent variables, and sediment peak lag time as the dependent variable, a sediment peak lag time prediction model for sediment peak lag type floods is constructed, expressed as: T z =kT a g b or c Where k, a, b, and c represent coefficients, and T represents the duration of the flood in days; Nonlinear fitting is performed based on the flood duration, sediment inflow coefficient, sediment type coefficient, and sediment peak lag time. The values ​​of the coefficients are determined according to the performance patterns of the independent and dependent variables.

2. The method for constructing the sand peak lag duration prediction model for sand peak-lagging floods as described in claim 1, characterized in that, Collect measured data of the delayed-peak flood and calculate the flood peak delay time T. z include: The measured data of the delayed-type flood with sand peaks include the daily average flow rate, daily average sediment content, and flood duration T. Based on the measured daily average flow and daily average sediment concentration from the aforementioned data on delayed-peak floods, the delay time T of the delayed-peak flood is calculated. z include: The time of sand peak appearance minus the time of flood peak appearance is the sand peak lag time. If multiple sand peaks appear during a flood, the lag time is the time difference between the sand peak closest to the flood peak.

3. The method for constructing a sand peak lag duration prediction model for sand peak-lagging floods as described in claim 1 or 2, characterized in that, Based on the measured data of the delayed-type flood with sand peaks, the calculations include: The average flow rate of the flood event is calculated based on the average daily flow rate, average daily sediment concentration, and flood duration T. Average sediment content of each flood And based on the average flow rate of the flood events. and the average sediment content of each flood event Calculate the sand inflow coefficient ζ and sand type coefficient η; Calculate the average flow rate of the flood event. Represented as: in, Q represents the average flow rate of a flood event, where Q1 is the average daily flow rate on the first day of the flood. T Let T be the average daily flow rate on day T of the flood, and T be the duration of the flood.

4. The method for constructing the sand peak lag duration prediction model for sand peak-lagging floods as described in claim 3, characterized in that, Calculate the average sediment content of the flood events. Represented as: in, S represents the average sediment concentration of a flood event, S1 is the average daily sediment concentration on the first day of the flood, and S T Let T be the average daily sediment content on day T of the flood, and T be the duration of the flood.

5. A system for constructing a prediction model for the delay duration of sand peaks in floods with delayed sand peaks, characterized in that, include, The data collection module is used to collect measured data on floods with delayed sand peaks and to calculate the sand peak lag time T during the flood process, where the sand peak appears later than the flood peak. z ; The calculation module is used to calculate the average flow rate of a flood event based on the measured data of the delayed-type flood. Average sediment content of each flood The sand inflow coefficient ζ and the sand type coefficient η; The module is used to analyze flood duration T, sediment inflow coefficient ζ, sediment type coefficient η, and sediment peak lag time T. z The relationship between them was investigated, and a prediction model for the delay time of sand peaks in sand peak-delayed floods was constructed. The calculated zeta coefficient ζ is expressed as: Where ζ represents the zeta coefficient. This indicates the average sediment content of a flood event. This indicates the average flow rate of a flood event; The sand type coefficient η is calculated as follows: Where η represents the sand type coefficient, S max Indicates the maximum sediment content of a flood. This indicates the average sediment content of the flood. The prediction model for the delay time of the sand peak in the aforementioned sand peak-delayed flood includes: Using flood duration T, sediment inflow coefficient ζ, and sediment type coefficient η as independent variables, and sediment peak lag time as the dependent variable, a sediment peak lag time prediction model for sediment peak lag type floods is constructed, expressed as: T z =kT a g b or c Where k, a, b, and c represent coefficients, and T represents the duration of the flood in days; Nonlinear fitting is performed based on the flood duration, sediment inflow coefficient, sediment type coefficient, and sediment peak lag time. The values ​​of the coefficients are determined according to the performance patterns of the independent and dependent variables.

6. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the method for constructing the sand peak lag duration prediction model of any one of claims 1 to 4.

7. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for constructing the sand peak lag duration prediction model for sand peak lag type floods as described in any one of claims 1 to 4.

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