Construction method and system of alluvial river average residual depth prediction model

Abstract

The invention discloses a method for constructing an alluvial river average residual depth prediction model in the technical field of water conservancy and hydropower engineering, and the method comprises the steps: obtaining the actual measurement data of the section of an alluvial river, and carrying out the preprocessing, thereby obtaining the thalweg of the alluvial river, the pre-flood average river facies coefficient of a river reach, and the post-flood average river facies coefficient of the river reach; performing pretreatment to obtain the average residual depth of the alluvial river; the upstream flow and sediment transport rate data are preprocessed, and the average flow and the average sediment transport rate are obtained; performing pretreatment to obtain a non-flood-season average water and sediment index and a flood-season average water and sediment index; analyzing the relationship among the pre-flood and post-flood average river facies coefficients of the river reach, the flood season and non-flood season average water and sediment indexes and the alluvial river average residual depth, and constructing a prediction model; according to the method, the average residual depth of the alluvial river can be accurately predicted, the average residual depth of the alluvial river under different spatial scales and long-term dynamic changes can be reflected, and the method has far-reaching guiding significance for river management and river regulation.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy and hydropower engineering, and particularly relates to a method and system for constructing a prediction model of the average residual depth of alluvial rivers. Background Art

[0002] The deep pool - shallow beach system is a basic geomorphic feature of alluvial rivers and is considered an important indicator of river channel morphology and aquatic ecology. The change in the average residual depth of alluvial rivers can reflect the vertical adjustment of the deep pool - shallow beach system, provide information about sedimentation, erosion processes, and river channel evolution, and has practical significance for understanding the water body ecosystem and enriching the study of riverbed evolution.

[0003] Regarding the calculation of the average residual depth of alluvial rivers at home and abroad, it is mainly the method of measured data. Based on the obtained topographic data, the average residual depth of alluvial rivers is identified and calculated. However, the topography and geomorphology of alluvial rivers are complex and changeable, and the measured topographic data often only cover part of the area and time period, making it difficult to reflect the average residual depth of alluvial rivers under different spatial scales and long - term dynamic changes. The present invention calculates the average residual depth of alluvial rivers based on measured topographic data, analyzes the correlation between the average river phase coefficient before the flood season of the river section, the average river phase coefficient after the flood season of the river section, the average water - sediment index during the non - flood season, the average water - sediment index during the flood season, and the average residual depth of alluvial rivers through a multivariate non - linear regression strategy, and constructs a prediction model for the average residual depth of alluvial rivers. The established model can accurately predict the average residual depth of alluvial rivers. Summary of the Invention

[0004] Aiming at the deficiencies in the prior art, the present invention provides a method for constructing a prediction model of the average residual depth of alluvial rivers, which solves the problem of how to predict the average residual depth of alluvial rivers and its influencing factors.

[0005] The object of the present invention is achieved as follows: A method for constructing a prediction model of the average residual depth of alluvial rivers includes the following steps:

[0006] Step 1) Obtain the cross - section measured data of the alluvial river and the daily average flow rate and daily average sediment transport rate data of the upstream of the alluvial river;

[0007] Step 2) Perform a first pre - processing on the cross - section measured data of the alluvial river to obtain the thalweg of the alluvial river, the average river phase coefficient before the flood season of the river section and the average river phase coefficient after the flood season of the river section ;

[0008] Step 3) Perform a second pre - processing based on the thalweg of the alluvial river to obtain the average residual depth of the alluvial river ;

[0009] Step 4) Perform the third preprocessing on the daily average flow rate and daily average sediment transport rate data of the upper reaches of the alluvial river to obtain the average flow rate and average sediment transport rate; the average flow rate includes the average flow rate in the non-flood season and the average flow rate in the flood season , the average sediment transport rate includes the average sediment transport rate in the non-flood season and the average sediment transport rate in the flood season ;

[0010] Step 5) Perform the fourth preprocessing based on the average flow rate and average sediment transport rate to obtain the average water-sediment index in the non-flood season and the average water-sediment index in the flood season ;

[0011] Step 6) Analyze the relationship between the average river regime coefficient before flood of the river reach , the average river regime coefficient after flood of the river reach , the average water-sediment index in the non-flood season , the average water-sediment index in the flood season and the average residual depth of the alluvial river , and construct a prediction model for the average residual depth of the alluvial river

[0012] Further, step 2) specifically includes:

[0013] The measured cross-section data of the alluvial river includes the elevation data of cross-section coordinate points, the floodplain width data of the cross-section, and the floodplain water depth data of the cross-section;

[0014] Use the elevation data of cross-section coordinate points to determine the main channel range of each cross-section, and connect the lowest points of the main channels of each cross-section to obtain the thalweg of the alluvial river;

[0015] Use the floodplain width data of the cross-section before flood and the floodplain water depth data of the cross-section before flood to calculate the average river regime coefficient of the river reach before flood, expressed as:

[0016]

[0017] Wherein, is the average river regime coefficient of the river reach before flood, s is the channel length of the alluvial river, N is the number of cross-sections of the alluvial river, and are respectively the floodplain widths of the l-th cross-section and the (l + 1)-th cross-section of the alluvial river from upstream to downstream, and are respectively the floodplain water depths of the l-th cross-section and the (l + 1)-th cross-section of the alluvial river from upstream to downstream, is the distance from the l-th cross-section to the (l + 1)-th cross-section of the alluvial river from upstream to downstream;

[0018] The post-flood flat beach river width data and post-flood flat beach water depth data of the river section are used to calculate the post-flood flat beach river facies coefficient of the river section, which is expressed as:

[0019]

[0020] Among them, is the average river facies coefficient of the river section after flood, and are respectively the post-flood flat beach river widths of the l-th section and the (l + 1)-th section of the alluvial river from upstream to downstream, and are respectively the post-flood flat beach water depths of the l-th section and the (l + 1)-th section of the alluvial river from upstream to downstream.

[0021] Furthermore, step 3) specifically includes:

[0022] All deep pools are extracted on the thalweg line of the alluvial river by using the residual depth method, and the residual depth of each deep pool in the alluvial river is calculated, which is expressed as:

[0023]

[0024] Among them, H r is the residual depth of the deep pool, A r is the area of the deep pool, and L r is the length of the deep pool;

[0025] The average residual depth of the alluvial river is calculated by using the residual depths of all deep pools in the alluvial river, which is expressed as:

[0026]

[0027] Among them, is the average residual depth of all deep pools in the alluvial river, x is the number of deep pools in the target river section, H r1 is the residual depth of the first deep pool of the alluvial river from upstream to downstream, and H rx is the residual depth of the x-th deep pool of the alluvial river from upstream to downstream.

[0028] Furthermore, step 4) specifically includes:

[0029] Calculation of the average flow rates in the non-flood season and the flood season, which is expressed as:

[0030]

[0031]

[0032] Among them, and are the average flows during the non-flood season and the flood season respectively, and n and m are the total number of days in the non-flood season and the flood season respectively, and are the average daily flows on the first day of the non-flood season and the flood season respectively, and are the average daily flows on the nth day of the non-flood season and the mth day of the flood season respectively;

[0033] The calculation of the average sediment transport rate in the non-flood season and the flood season is expressed as:

[0034]

[0035]

[0036] where, and are the average sediment transport rates in the non-flood season and the flood season respectively, and n and m are the total number of days in the non-flood season and the flood season respectively, and are the average daily sediment transport rates on the first day of the non-flood season and the flood season respectively, and are the average daily sediment transport rates on the nth day of the non-flood season and the mth day of the flood season respectively.

[0037] Furthermore, step 5) specifically includes:

[0038] Calculating the average water-sediment index in the non-flood season using the average flow in the non-flood season and the average sediment transport rate in the non-flood season, which is expressed as:

[0039]

[0040] where, is the average water-sediment index in the non-flood season, is the average flow in the non-flood season, is the average sediment transport rate in the non-flood season.

[0041] Calculating the average water-sediment index in the flood season using the average flow in the flood season and the average sediment transport rate in the flood season, which is expressed as:

[0042]

[0043] where, is the average water-sediment index in the flood season, is the average flow in the flood season, is the average sediment transport rate in the flood season.

[0044] Furthermore, constructing the average residual depth prediction model of the alluvial river in step 6) includes:

[0045] Taking the average river phase coefficient before the flood season of the river reach and the average river phase coefficient after the flood season of the river reach , the average sediment-water index during the non-flood season and the average sediment-water index during the flood season are used as independent variables, and the average residual depth of the alluvial river is used as the dependent variable to construct a prediction model for the average residual depth of the alluvial river, expressed as:

[0046]

[0047] where k 1 , k 2 , a, b, c, and d represent coefficients;

[0048] Based on the average river regime coefficient before the flood season, the average river regime coefficient after the flood season, the average sediment-water index during the non-flood season, the average sediment-water index during the flood season, and the average residual depth of the alluvial river in the said river reach, perform non-linear fitting, and determine the values of the said coefficients according to the performance laws of the independent variables and the dependent variable.

[0049] A system for constructing a prediction model for the average residual depth of an alluvial river includes:

[0050] A data acquisition module for acquiring the measured cross-section data of the alluvial river and the daily average flow rate and daily average sediment transport rate data of the upstream of the alluvial river;

[0051] A first preprocessing module performs first preprocessing on the measured cross-section data of the alluvial river to obtain the thalweg of the alluvial river, the average river regime coefficient before the flood season of the river reach and the average river regime coefficient after the flood season of the river reach ;

[0052] A second preprocessing module performs second preprocessing based on the thalweg of the alluvial river to obtain the average residual depth of the alluvial river ;

[0053] A third preprocessing module performs third preprocessing on the daily average flow rate and daily average sediment transport rate data of the upstream of the alluvial river to obtain the average flow rate and the average sediment transport rate; the average flow rate includes the average flow rate during the non-flood season and the average flow rate during the flood season , and the average sediment transport rate includes the average sediment transport rate during the non-flood season and the average sediment transport rate during the flood season ;

[0054] A fourth preprocessing module performs fourth preprocessing based on the average flow rate and the average sediment transport rate to obtain the average sediment-water index during the non-flood season and the average sediment-water index during the flood season ;

[0055] A model construction module for analyzing the average river regime coefficient before the flood season of the said river reach and the average river regime coefficient after the flood season of the river reach , non-flood season average sediment-water index , flood season average sediment-water index and the average residual depth of alluvial rivers to establish the relationship and construct a prediction model for the average residual depth of alluvial rivers.

[0056] Compared with the prior art, the beneficial effects of the present invention are as follows: Based on the measured topographic data, the present invention calculates the average residual depth of alluvial rivers, analyzes the correlation between the average river regime coefficient before flood, the average river regime coefficient after flood, the non-flood season sediment-water index, the flood season sediment-water index and the average residual depth of alluvial rivers through a multiple non-linear regression strategy, and constructs a prediction model for the average residual depth of alluvial rivers. The established model can accurately predict the average residual depth of alluvial rivers, can reflect the average residual depth of alluvial rivers under different spatial scales and long-term dynamic changes, and has far-reaching guiding significance for river management and river regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0058] Figure 1 is a flowchart of the method for constructing a prediction model for the average residual depth of alluvial rivers and the system according to an embodiment of the present invention.

[0059] Figure 2 is a schematic diagram of the correlation between the calculated value and the measured value of the average residual depth prediction model of the meandering section in the lower Yellow River from 1986 to 2015 according to an embodiment of the present invention.

[0060] Figure 3 is a schematic diagram of the comparison between the calculated value and the measured value of the average residual depth prediction model according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0062] Embodiment 1

[0063] Refer toFigures 1-3 , which is an embodiment of the present invention, provides a method for constructing a prediction model of the average residual depth of an alluvial river, including:

[0064] S1. Obtain the measured cross-section data of the alluvial river and the daily average flow rate and daily average sediment transport rate data of the upper reaches of the alluvial river;

[0065] Specifically, in this example, the measured data of 36 cross-sections in the meandering section of the lower Yellow River from 1986 to 2015 are collected, including the elevation data of cross-section coordinate points, the floodplain width and floodplain water depth of the cross-section; the daily average flow rate and daily average sediment transport rate of the Aishan Hydrological Station in the upper reaches of the meandering section of the lower Yellow River from 1986 to 2015 are collected.

[0066] S2. Perform a first preprocessing on the measured cross-section data of the alluvial river to obtain the thalweg, the average river phase coefficient before the flood season of the river reach and the average river phase coefficient after the flood season of the river reach ;

[0067] Specifically, determine the main channel range of each cross-section according to the elevation data of the cross-section coordinate points, and then determine the point with the lowest elevation in the main channel;

[0068] Furthermore, connect the points with the lowest elevation on the 36 cross-sections in the meandering section of the lower Yellow River to determine the thalweg of the meandering section of the lower Yellow River.

[0069] Calculate the average river phase coefficient before the flood season of the river reach according to the floodplain width data and floodplain water depth data before the flood season of the 36 cross-sections, expressed as:

[0070]

[0071] Among them, is the average river phase coefficient before the flood season of the river reach, s is the channel length of the alluvial river, unit: km, N is the number of cross-sections of the alluvial river, unit: piece, and are the floodplain widths before the flood season of the l-th cross-section and the (l + 1)-th cross-section of the alluvial river from the upstream to the downstream, unit: m, and are the floodplain water depths before the flood season of the l-th cross-section and the (l + 1)-th cross-section of the alluvial river from the upstream to the downstream, unit: m, is the distance from the l-th cross-section to the (l + 1)-th cross-section of the alluvial river from the upstream to the downstream, unit: km.

[0072] Calculate the average river phase coefficient before the flood season of the river reach according to the floodplain width data and floodplain water depth data after the flood season of the 36 cross-sections, expressed as:

[0073]

[0074] Among them, is the average river regime coefficient after flood season for the river reach, s is the channel length of the alluvial river, unit: km, N is the number of cross-sections of the alluvial river, unit: number, and are respectively the flood-season flat-flood river widths of the l-th cross-section and the (l + 1)-th cross-section of the alluvial river counted from upstream to downstream, unit: m, and are respectively the flood-season flat-flood water depths of the l-th cross-section and the (l + 1)-th cross-section of the alluvial river counted from upstream to downstream, unit: m, is the distance from the l-th cross-section to the (l + 1)-th cross-section of the alluvial river counted from upstream to downstream, unit: km.

[0075] S3, perform a second preprocessing based on the thalweg of the alluvial river to obtain the average residual depth of the alluvial river ;

[0076] Specifically, use the residual depth method to extract all deep pools on the thalweg of the meandering section, that is, determine and calculate the water depth (i.e., residual depth) on the thalweg of the meandering section under the assumption of zero flow. In this case, the area where the residual depth is greater than zero is identified as a deep pool;

[0077] Furthermore, calculate the residual depth of each deep pool in the meandering section, expressed as:

[0078]

[0079] Among them, H r represents the residual depth of each deep pool, unit: m, A r represents the area of each deep pool, unit: m 2 , L r represents the length of each deep pool, unit: m;

[0080] Even further, use the residual depths of all deep pools on the meandering section to calculate the average residual depth of the meandering section, expressed as:

[0081]

[0082] Among them, is the average residual depth of all deep pools on the meandering section, unit: m, x is the number of deep pools in the meandering section, unit: number, H r1 is the residual depth of the first deep pool in the meandering section counted from upstream to downstream, unit: m, H rx is the residual depth of the x-th deep pool in the meandering section counted from upstream to downstream, unit: m;

[0083] It should be noted that if the residual depth H of the deep pool rLess than 0.01 m. Considering the errors in the measured topographic data, this area is not listed as a deep pool and is not included in the calculation of the average residual depth of the alluvial river.

[0084] S4. Perform a third preprocessing on the daily average flow rate and daily average sediment transport rate data of the upper reaches of the alluvial river to obtain the average flow rate and average sediment transport rate. The average flow rate includes the average flow rate during the non-flood season and the average flow rate during the flood season , and the average sediment transport rate includes the average sediment transport rate during the non-flood season and the average sediment transport rate during the flood season ;

[0085] Specifically, in this example, based on the daily average flow rate and daily average sediment transport rate of the Aishan Hydrological Station in the upper reaches of the meandering section of the lower Yellow River from 1986 to 2015, calculate the average flow rate during the non-flood season for each year , the average flow rate during the flood season , the average sediment transport rate during the non-flood season and the average sediment transport rate during the flood season ;

[0086] Among them, the calculation of the average flow rate during the non-flood season and flood season for each year is expressed as:

[0087]

[0088]

[0089] Among them, and are the average flow rates during the non-flood season and flood season for each year, unit: m 3 / s, n and m are the total number of days during the non-flood season and flood season for each year, unit: days, and are the daily average flow rates on the first day of the non-flood season and flood season for each year, unit: m 3 / s, and are the daily average flow rates on the nth day of the non-flood season and the mth day of the flood season for each year, unit: m 3 / s;

[0090] The calculation of the average sediment transport rate during the flood season and non-flood season for each year is expressed as:

[0091]

[0092]

[0093] Among them, and The average sediment transport rates for the non-flood season and flood season of each year, unit: kg / s, n and m are the total number of days in the non-flood season and flood season of each year respectively, unit: days. and are the average daily sediment transport rates on the first day of the non-flood season and flood season of each year respectively, unit: kg / s. and are the average daily sediment transport rates on the nth day of the non-flood season and the mth day of the flood season of each year respectively, unit: kg / s.

[0094] It should be noted that the division of the flood season and non-flood season in the meandering section of the lower Yellow River is as follows: the non-flood season is from early November of the previous year to the end of June of the current year, and the flood season is from early July to the end of October of the current year.

[0095] S5, perform the fourth preprocessing based on the average flow rate and average sediment transport rate to obtain the average sediment-water index for the non-flood season and the average sediment-water index for the flood season ;

[0096] Specifically, calculate the average sediment-water index for the non-flood season according to the average flow rate and average sediment transport rate in the non-flood season, expressed as:

[0097]

[0098] where is the average sediment-water index for the non-flood season, is the average flow rate in the non-flood season, unit: m 3 / s, is the average sediment transport rate in the non-flood season, unit: kg / s.

[0099] Calculate the average sediment-water index for the flood season according to the average flow rate and average sediment transport rate in the flood season, expressed as:

[0100]

[0101] where is the average sediment-water index for the flood season, is the average flow rate in the flood season, unit: m 3 / s, is the average sediment transport rate in the flood season, unit: kg / s.

[0102] S6, analyze the relationship between the average river regime coefficient before flood in the reach, the average river regime coefficient after flood in the reach, the average sediment-water index in the non-flood season the average sediment-water index in the flood season and the average residual depth of alluvial rivers and construct a prediction model for the average residual depth of alluvial rivers;

[0103] Specifically, taking the pre-flood average river pattern coefficient of the meandering section in the lower Yellow River from 1986 to 2015 , the post-flood average river pattern coefficient of the river section , the average sediment-water index during the non-flood season , and the average sediment-water index during the flood season as independent variables, and the average residual depth of the alluvial river in the meandering section of the lower Yellow River from 1986 to 2015 as the dependent variable, a prediction model for the average residual depth of the alluvial river is constructed, expressed as:

[0104]

[0105] where k 1 , k 2 , a, b, c, and d represent coefficients;

[0106] Nonlinear fitting is performed according to the pre-flood average river pattern coefficient, post-flood average river pattern coefficient, average sediment-water index during the non-flood season, average sediment-water index during the flood season, and average residual depth of the alluvial river, and the values of the coefficients are determined according to the performance laws of the independent variables and the dependent variable.

[0107] The above is a schematic solution for a method of constructing a prediction model for the average residual depth of an alluvial river in this embodiment. It should be noted that the technical solution of the system for constructing the prediction model for the average residual depth of the alluvial river belongs to the same concept as the technical solution of the method for constructing the prediction model for the average residual depth of the alluvial river described above. For the details not described in detail in the technical solution of the system for constructing the prediction model for the average residual depth of the alluvial river in this embodiment, reference can be made to the description of the technical solution of the method for constructing the prediction model for the average residual depth of the alluvial river described above.

[0108] In this embodiment, a system for constructing a prediction model for the average residual depth of an alluvial river includes:

[0109] A data acquisition module for acquiring the measured cross-section data of the alluvial river and the daily average flow rate and daily average sediment transport rate data of the upstream of the alluvial river;

[0110] A first preprocessing module for performing first preprocessing on the measured cross-section data of the alluvial river to obtain the thalweg of the alluvial river, the pre-flood average river pattern coefficient of the river section and the post-flood average river pattern coefficient of the river section ;

[0111] A second preprocessing module for performing second preprocessing based on the thalweg of the alluvial river to obtain the average residual depth of the alluvial river ;

[0112] The third preprocessing module performs third preprocessing on the daily average flow rate and daily average sediment transport rate data of the upper reaches of the alluvial river to obtain the average flow rate and the average sediment transport rate. The average flow rate includes the average flow rate in the non-flood season and the average flow rate in the flood season , and the average sediment transport rate includes the average sediment transport rate in the non-flood season and the average sediment transport rate in the flood season ;

[0113] The fourth preprocessing module performs fourth preprocessing based on the average flow rate and the average sediment transport rate to obtain the average sediment-water index in the non-flood season and the average sediment-water index in the flood season ;

[0114] The model construction module is used to analyze the relationship between the average river phase coefficient before the flood season of the river reach, the average river phase coefficient after the flood season of the river reach, the average sediment-water index in the non-flood season , the average sediment-water index in the flood season and the average residual depth of the alluvial river , and constructs a prediction model for the average residual depth of the alluvial river.

[0115] This embodiment also provides an electronic device, which is applicable to the construction of a prediction model for the average residual depth of an alluvial river, and includes:

[0116] A memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for constructing a prediction model for the average residual depth of an alluvial river as proposed in the above embodiment.

[0117] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the method for constructing a prediction model for the average residual depth of an alluvial river as proposed in the above embodiment.

[0118] The storage medium proposed in this embodiment and the method for constructing a prediction model for the average residual depth of an alluvial river proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0119] From the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software and necessary general hardware. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation. Based on this understanding, the technical solution of the present invention, in essence 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 floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk or optical disc of a computer, etc., including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0120] Embodiment 2

[0121] Referring to Figures 2-3 , an embodiment of the present invention provides a method for constructing a prediction model of the average residual depth of an alluvial river. In order to verify its beneficial effects, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0122] In this embodiment, a verification test of a method for constructing a prediction model of the average residual depth of an alluvial river is provided. Based on the average river phase coefficient before the flood season, the average river phase coefficient after the flood season, the average water-sediment index during the non-flood season, the average water-sediment index during the flood season, and the average residual depth of the alluvial river in the lower reaches of the Yellow River from 1986 to 2015. Taking the average residual depth of the meandering section as the measured value, a multivariate non-linear regression strategy is adopted to calibrate the parameters k 1 , a, b, k 2 , c, d, and the calibrated k 1 , a, b, k 2 , c, d are -153.598, -4.922, 0.618, 14.922, -1.496, 0.073 respectively. The data of the average residual depth of the meandering section from 2016 to 2020 is used to verify the model.

[0123] The calibration and verification results of the model show that: Figure 2 The correlation coefficients between the average river phase coefficient before the flood season, the average river phase coefficient after the flood season, the average water-sediment index during the non-flood season, the average water-sediment index during the flood season and the average residual depth of the meandering section in the lower reaches of the Yellow River are 0.64. Therefore, the constructed model can better predict the average residual depth of the meandering section in the lower reaches of the Yellow River.

[0124] In order to verify the empirical formula, Figure 3The calculated values and measured values of the average residual depth model for the meandering section in the lower Yellow River are given. It can be seen from the figure that the calculated values of the average residual depth model for the meandering section in the lower Yellow River are basically consistent with the measured values.

[0125] The description of the above embodiments is only used to help understand the method and its core idea of the present invention. It should be noted that for those of ordinary skill in the art of this technology, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims

1. A method for constructing an average residual depth prediction model for alluvial rivers, characterized in that: The following steps are involved: Step 1) Obtain the measured data of the cross section of the alluvial river and the daily average flow and daily average sediment transport rate data of the upper reaches of the alluvial river; Step 2) Preprocess the measured data of the cross section of the alluvial river to obtain the deep-water line of the alluvial river and the average river phase coefficient before the flood season. Average river phase coefficient after flood season in Hehe section ; Step 3) Perform a second preprocessing based on the deep-water line of the alluvial river to obtain the average residual depth of the alluvial river ; Step 4) Perform a third preprocessing on the daily average flow and daily average sediment transport rate data of the upstream of the alluvial river to obtain the average flow and average sediment transport rate; the average flow includes the average flow in the non-flood season Average flow during flood season The average sediment transport rate includes the average sediment transport rate in the non-flood season Average sediment transport rate during the flood season ; Step 5) Perform a fourth preprocessing based on the average flow and average sediment transport rate to obtain the average water-sediment index in the non-flood season Average water-sediment index during the flood season ; Step 6) Analyze the average river phase coefficient of the river section before the flood season , average river phase coefficient after flood season , Average water-sediment index in non-flood season , average water-sediment index during flood season and the average residual depth of alluvial rivers The relationship between them was established and a prediction model for the average residual depth of alluvial rivers was constructed.

2. The method for constructing an average residual depth prediction model for an alluvial river according to claim 1, characterized in that: Step 2) specifically includes: The measured data of the cross section of the alluvial river include elevation data of the cross section coordinate points, river width data of the cross section flat beach and water depth data of the cross section flat beach; The main channel range of each section is determined by using the elevation data of the section coordinate points, and the deep channel line of the alluvial river is obtained by connecting the lowest points of the main channels of each section. The river phase coefficient of the river section before the flood season is calculated by using the river width data of the flat beach before the flood season and the water depth data of the flat beach before the flood season, which is expressed as: ； in, is the average river phase coefficient of the river section before the flood season, s is the length of the alluvial river, N is the number of sections of the alluvial river, and are the width of the flat river before the flood season at the lth section and the l+1th section of the alluvial river from upstream to downstream, and are the pre-flood flat water depths of the lth section and the l+1th section of the alluvial river from upstream to downstream, It is the distance from the lth section to the l+1th section from upstream to downstream of the alluvial river; The river phase coefficient of the river section after the flood season is calculated using the river width data of the flat beach after the flood season and the water depth data of the flat beach after the flood season, which is expressed as: ； in, is the average river phase coefficient of the river section after flood season, and are the widths of the flat river after the flood season at the lth section and the l+1th section of the alluvial river from upstream to downstream, and They are respectively the post-flood flat water depths of the lth section and the l+1th section of the alluvial river from upstream to downstream.

3. The method for constructing an average residual depth prediction model for an alluvial river according to claim 2, characterized in that: Step 3) specifically includes: The residual depth method is used to extract all deep pools on the deep line of the alluvial river, and the residual depth of each deep pool in the alluvial river is calculated, which is expressed as: ； Among them, H r is the residual depth of the deep pool, A r is the area of ​​the deep pool, L r is the length of the deep pool; The average residual depth of alluvial rivers is calculated using the residual depths of all deep pools in the alluvial rivers and is expressed as: ； in, is the average residual depth of all deep pools in the alluvial river, x is the number of deep pools in the target river section, H r1 H is the residual depth of the first deep pool of the alluvial river from upstream to downstream, rx It is the residual depth of the xth deep pool of the alluvial river from upstream to downstream.

4. The method for constructing an average residual depth prediction model for an alluvial river according to claim 3, characterized in that: Step 4) specifically includes: The calculation of the average flow in the non-flood season and the flood season is expressed as: ； ； in, and are the average flow in the non-flood season and the flood season, n and m are the total number of days in the non-flood season and the flood season, respectively. and are the average daily flow in the non-flood season and the first day of the flood season, and are the average daily flow on the nth day in the non-flood season and the mth day in the flood season, respectively; The calculation of the average sediment transport rate in the non-flood season and the flood season is expressed as: ； ； in, and are the average sediment transport rates in the non-flood season and the flood season, n and m are the total number of days in the non-flood season and the flood season, respectively. and are the average daily sediment transport rates in the non-flood season and the first day of the flood season, and They are the average daily sediment transport rates on the nth day in the non-flood season and the mth day in the flood season respectively.

5. The method for constructing an average residual depth prediction model for an alluvial river according to claim 4, characterized in that: Step 5) specifically includes: The average water-sediment index in the non-flood season is calculated using the average flow in the non-flood season and the average sediment transport rate in the non-flood season, which is expressed as: ； in, is the average water-sediment index in the non-flood season, is the average flow in the non-flood season, is the average sediment transport rate in the non-flood season; The average water-sediment index during the flood season is calculated using the average flow during the flood season and the average sediment transport rate during the flood season, which is expressed as: ； in, is the average water-sediment index during the flood season, is the average flow rate during the flood season, is the average sediment transport rate during the flood season.

6. The method for constructing an average residual depth prediction model for an alluvial river according to claim 5, characterized in that: The construction of the alluvial river average residual depth prediction model in step 6) includes: The average river phase coefficient before the flood season , average river phase coefficient after flood season , Average water-sediment index in non-flood season Average water-sediment index during the flood season is the independent variable, the average residual depth of alluvial rivers As the dependent variable, the average residual depth prediction model of alluvial rivers is constructed, which is expressed as: ；； Among them, k1, k2, a, b, c, d represent coefficients; Nonlinear fitting is performed based on the average river phase coefficient of the river section before flood season, the average river phase coefficient of the river section after flood season, the average water-sediment index in non-flood season, the average water-sediment index in flood season and the average residual depth of alluvial rivers, and the value of the coefficient is determined according to the performance rules of the independent variable and the dependent variable.

7. A system for constructing an average residual depth prediction model for alluvial rivers, characterized in that: include: The data acquisition module is used to obtain the measured data of the cross section of the alluvial river and the daily average flow and daily average sediment transport rate data of the upper reaches of the alluvial river; The first preprocessing module performs the first preprocessing on the measured data of the cross section of the alluvial river to obtain the deep line of the alluvial river and the average river phase coefficient before the flood season of the river section. Average river phase coefficient after flood season in Hehe section ; The second preprocessing module performs a second preprocessing based on the deep line of the alluvial river to obtain the average residual depth of the alluvial river. ; The third preprocessing module performs a third preprocessing on the daily average flow and daily average sediment transport rate data of the upper reaches of the alluvial river to obtain the average flow and average sediment transport rate; the average flow includes the average flow in the non-flood season Average flow during flood season The average sediment transport rate includes the average sediment transport rate in the non-flood season Average sediment transport rate during the flood season ; The fourth preprocessing module performs a fourth preprocessing based on the average flow and the average sediment transport rate to obtain an average water-sediment index in the non-flood season. Average water-sediment index during the flood season ; Model building module for analyzing the average river phase coefficient before flood season in the river section , average river phase coefficient after flood season , Average water-sediment index in non-flood season , average water-sediment index during flood season and the average residual depth of alluvial rivers The relationship between them was established and a prediction model for the average residual depth of alluvial rivers was constructed.

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