A regional flood risk assessment method considering pipe silting quantification

Abstract

The application discloses a kind of regional flood risk assessment methods considering pipeline silting quantification, steps are as follows: the real-time monitoring data set of water level is collected;The obtained data is used as input to build a two-dimensional flood numerical simulation model;The influence analysis model of pipeline equivalent silting degree on flood is constructed, the data obtained is input to the two-dimensional flood numerical simulation model, and the flooding depth of each flood risk assessment interval is output;The flood consequence assessment model of each flood risk assessment interval is established, and the flood consequence grade of each flood risk assessment interval is output;Flood probability grade division is carried out, and the flood probability assessment model of each flood risk assessment interval is obtained, and the flood probability grade is output;An improved risk matrix is used to construct a "probability-consequence" coupling assessment model, and a flood risk assessment result is obtained.The application can effectively and accurately predict and evaluate the flood risk distribution of the test field considering pipeline diseases, which is of great significance for urban flood management and disaster prevention and reduction.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of urban flood multi-factor coupling evaluation, and is a regional flood risk evaluation method considering pipe silting quantization. BACKGROUND

[0002] In recent years, floods have become one of the most destructive and economically burdensome natural disasters globally. Therefore, it is particularly urgent to clarify the formation mechanism and development law of urban flood disasters.

[0003] Urban flood risk assessment has become an important means of flood warning and disaster prevention decision-making. Common flood risk analysis usually focuses on disaster analysis (i.e., generating a map indicating flood depth and range) and vulnerability analysis (i.e., determining the relationship between flood and asset loss). Most of these flood risk assessment models focus on the quantification of various factors that cause floods and the damage caused by floods. The spatial and temporal distribution of flood influencing factors includes risk, vulnerability, and exposure. In order to fully consider the complex social and economic problems related to floods, a self-triggered risk evaluation model based on road environment is proposed. This model includes the potential impact of floods on road traffic in the scope of flood risk assessment. However, the above flood risk analysis does not consider the impact of different land use vulnerabilities on the evaluation results.

[0004] Overflow obstruction caused by drainage pipe silting is one of the important factors causing urban floods. Therefore, the degree of pipe silting has a crucial impact on the distribution of flood risk levels in the study area. Currently, the pipe network system of most cities is aging to varying degrees, which further exacerbates urban flood risk. Some scholars have applied drainage pipe planning to the assessment of gray infrastructure in highly urbanized areas, verifying the positive impact of reasonable pipe network system drainage capacity on urban flood control. The urban surface and subsurface two-dimensional coupling model considers the pipe diameter and spatial distribution of the drainage pipe network. However, existing research has not quantified the impact of pipe silting degree on floods into the risk assessment model. The spatiotemporal evolution process of drainage pipe silting degree has both linear and nonlinear rules. How to select a regression prediction model and a high-dimensional nonlinear intelligent model of spatiotemporal dimensions is the key to predicting the equivalent silting degree of drainage pipes.

[0005] Therefore, it is urgent to develop a flood risk assessment method that considers pipe silting disasters and the like. SUMMARY

[0006] The technical solution adopted by the application is as follows: a regional flood risk assessment method considering pipe silting quantization, comprising the following steps:

[0007] Step S1, select a suitable site to build a flood test field, install multiple types of sensing facilities, collect real-time monitoring data sets of flow and water level, and river and lake water level;

[0008] Step S2, input the data obtained from the real-time monitoring data set in step S1 into a two-dimensional flood numerical simulation model;

[0009] Step S3, build a model for analyzing the impact of pipe equivalent deposition on floods, input the pipe equivalent deposition, soil permeability and hydraulic roughness above the pipe obtained by the model for analyzing the impact of pipe equivalent deposition on floods into the two-dimensional flood numerical simulation model in step S2, and output the flooding depth of each flood risk assessment interval;

[0010] Step S4, obtain the flooding depth of each flood risk assessment interval using the two-dimensional flood numerical simulation model in step S3, combine the vulnerability curve with the flooding depth of each flood risk assessment interval, obtain the asset loss of different flood risk assessment intervals under a specific rainfall scenario, determine the flood consequence level of each flood risk assessment interval, and then build a flood consequence assessment model for each flood risk assessment interval, and output the flood consequence level of each flood risk assessment interval;

[0011] Step S5, combine the land type obtained in step S2, the pipe equivalent deposition obtained in step S3, and the flooding depth of each flood risk assessment interval, use the improved TOPSIS method to divide the flood probability level, obtain a flood probability assessment model for each flood risk assessment interval, and output the flood probability level;

[0012] Step S6, combine the flood consequence level of each flood risk assessment interval in step S4 and the flood probability level in step S5, and use the improved risk matrix to build a "probability-consequence" coupling assessment model to obtain the "probability-consequence" coupled flood risk assessment result.

[0013] Further, in step S2, the specific steps are as follows:

[0014] Step S21, one-dimensional pipe and open channel flow hydraulic calculation uses to solve the complete Saint-Venant equation set, and the full pipe flow uses the Preissmann slit method; the Saint-Venant equation set of open channel flow is represented as:

[0015]

[0016] In the formula, A is the flow section area, Q is the flow, g is the gravity acceleration, θ is the angle between the pipe center line and the horizontal line, K is the water delivery rate, and S0 is the pipe bottom slope;

[0017] Further, the control equation of the pressure pipe flow model is as follows:

[0018]

[0019] In the formula, A is the cross-sectional area of flow, Q is the flow rate, g is the acceleration of gravity, K is the delivery rate, and S0 is the slope of the pipeline bottom.

[0020] In step S22, on the basis of step S21, two-dimensional water dynamic calculation is performed using the shallow water equation; the shallow water Boussinesq hypothesis and the static pressure assumption are followed to perform depth average approximation on the vertical direction velocity of the Navier-Stokes equation; in the two-dimensional shallow water flow, it is assumed that the flow is mainly in the horizontal direction, and the change in the vertical direction is ignored; the conservation type shallow water equation in the urban flood simulation model is represented as:

[0021]

[0022] In the formula, t is time, x and y are spatial coordinates, h is water depth, u and v are the velocities in the x and y directions respectively; q i is the i-th net source flux; u i and v i are the velocities in the i and j directions respectively; g is the acceleration of gravity, ε is the eddy viscosity, S 0,x and S 0,y are the bottom slope source terms of the riverbed in the x and y directions respectively; S f,x and S f,y are the friction force source terms in the x and y directions respectively; and n is the number of source fluxes.

[0023] In step S23, a two-dimensional flood numerical simulation model is constructed using the Saint-Venant equation set of the open channel flow in step S21 and the conservation type shallow water equation in step S22.

[0024] Further, in step S3, the specific steps are as follows:

[0025] In step S31, the pipe flow cross-sectional loss degree caused by the siltation of different types of drainage pipelines is defined as the equivalent siltation degree of the pipeline, which is used as the evaluation standard of the loss degree of the pipe flow characteristics; in the actual measurement process, the silted cross-sectional area A c and the siltation degree S D are calculated in the following manner:

[0026]

[0027] In the formula, D is the pipe diameter, and H is the silt thickness.

[0028] In step S32, the siltation degrees of the pipelines are quantified into equivalent siltation degrees according to the calculation formula in step S31 and the integral average method.

[0029] Step S33, on the basis of step S32, configure the seepage solving control equation for the silted pipeline, and the control equation of the permeable medium pipe model is expressed as follows:

[0030]

[0031] In the formula, P c is the permeability coefficient, S p is the cross-sectional area of the permeable medium, Δh / L is the hydraulic slope; Vf is the porosity;

[0032] Step S34, the hydraulic roughness is calculated by using the Colebrook-White formula; the mathematical expression of the hydraulic roughness is as follows:

[0033] Free surface:

[0034] Closed full flow:

[0035] In the formula, f is the frictional resistance coefficient along the pipeline, k is the equivalent roughness of the pipeline, R e is the Reynolds number, and R is the hydraulic radius; according to the pipeline siltation condition, the appropriate equivalent roughness is selected, and the overcurrent resistance caused by the pipeline siltation is included in the flood numerical simulation model;

[0036] Step S35, the equivalent siltation degree in step S32, the permeability coefficient in step S33, and the hydraulic roughness in step S34 are input into the two-dimensional flood numerical simulation model in step S2, and the submergence depth of each flood risk assessment interval is output.

[0037] Further, in step S4, the specific steps are as follows:

[0038] Step S41, according to the statistical yearbook of the city where the test field belongs to in the previous year, the asset value per unit area of different land use types is obtained;

[0039] Step S42, on the basis of step S41, the asset composition inside the building is determined, the combination relationship between the flood depth and the asset loss inside the building is established, the asset loss under different submergence depths is calculated, and the cumulative asset loss is calculated; the asset loss rate under different submergence depths is used to fit the residential land flood loss rate curve to obtain the residential land vulnerability curve as follows:

[0040]

[0041] In the formula, x is the submergence depth, y is the asset loss rate (%), and R is the correlation coefficient;

[0042] Step S43, on the basis of step S42, the correlation between residential land and other different land types is analyzed by linear regression analysis test to establish the regression equation of the flood vulnerability curve; the flood vulnerability curve of residential land is used to conduct regression analysis on the flood vulnerability curve of other land types; the vulnerability of different land types is quantified as follows. landuse The quantification of the vulnerability of different land types is as follows.

[0043] lr landuse = a landuse ln(x r )+ b landuse (9)

[0044] In the formula, x r is the coordinate point of the land type; a landuse and b landuse are related parameters.

[0045] In combination with the vulnerability curve and the submergence depth of each flood risk assessment interval, the asset loss of different flood risk assessment intervals under a specific rainstorm scenario is obtained, and the flood consequence level of each flood risk assessment interval is determined.

[0046] Further, in step S5, the specific steps are as follows:

[0047] Step S51, taking the submergence interval output by the flood numerical simulation model as the basic flood probability assessment unit, the maximum submergence depth, submergence duration, maximum submergence speed, unit maximum flow, land type, and comprehensive health rate of the downstream pipe of the catchment outlet of the subset water area are included in the assessment of flood probability; the calculation formula of the pipe health rate downstream of the catchment outlet of the subset water area is as follows:

[0048]

[0049] In the formula, ζ is the comprehensive health rate, z i represents the health degree of the i th pipe, and e represents the number of pipes downstream of the catchment outlet of the subset water area to which the 2D area belongs.

[0050] Step S52, the unacceptable degree of the maximum submergence depth, submergence duration, maximum submergence speed, unit maximum flow, land type, and pipe health rate in step S51 is graded and expressed, and the unacceptable interval of different parameters of all 2D areas is given according to the graded expression.

[0051] Step S53, according to the unacceptable interval of different parameters in step S52, the traditional TOPSIS method is improved by introducing the YAMANAKA coefficient, and the improved TOPSIS method is used for flood probability level division to obtain the flood probability assessment model of each flood risk assessment interval.

[0052] The Tanimoto coefficient is used to evaluate the similarity between two sets of data. It assumes there are two arrays X = (x1, x2, ..., x...). n Y = (y1, y2, ..., y n If ), then the Tanimoto coefficient can be expressed as:

[0053]

[0054] The improved method for ranking approximate ideal solutions uses linear weighting to determine the subjective and objective combination weights ω of the indicators. j ;x i Let y be the value of the i-th sample in array X; i Let i be the value of the i-th sample in array Y;

[0055]

[0056] In the formula, ω j ,ω' j and These represent the combined weights, the weights determined by the analytic hierarchy process (AHP), and the weights determined by the improved entropy weight method, respectively; γ is the proportion of the improved entropy weight method weight in the overall weights, and... ω'1,ω'2,…ω' q The weights of the analytic hierarchy process (AHP) are arranged from smallest to largest, where q is the number of indicators; the weights of each indicator, w = (ω1, ω2, ..., ω), are obtained from the above analysis. n ) T ;

[0057] The index-weighted normalization matrix in the improved approximation ideal solution ranking method is X = {x ij} m×n , where x ij =ω j b ij Let i = 1, 2, ..., m, j = 1, 2, 3, ..., n; the j-th characteristic attribute values ​​of the ideal solution and the negative ideal solution are respectively and Similarity between the i-th weighted normalized sample and the ideal solution and the negative ideal solution It can be represented as:

[0058]

[0059] In the improved approximation of ideal solution sorting method, the relative proximity of the i-th sample can be expressed as:

[0060] Furthermore, in step S6, the specific steps are as follows:

[0061] Step S61: Input the flood consequence level of each flood risk assessment interval in step S4 and the flood probability level results in step S5.

[0062] Step S62, on the basis of step S61, introduce the disaster theory principle to improve the risk matrix analysis, and divide the flood risk into 5 categories: high risk-high economic vulnerability land, high risk-low economic vulnerability land, medium risk-medium economic vulnerability land, low risk-high economic vulnerability land, and low risk-low economic vulnerability land.

[0063] The beneficial effects of the present application are: a regional flood risk assessment method considering pipeline siltation quantification, which is composed of a two-dimensional flood numerical simulation model, a pipeline equivalent siltation degree influence analysis model, a flood probability evaluation model for each flood risk assessment interval, a flood consequence evaluation model for each flood risk assessment interval, and a "probability-consequence" coupling evaluation model. Select a suitable site to build a flood test field, install multiple types of sensing facilities, and collect real-time monitoring data sets of rainfall, soil infiltration rate, flow and water level of each key node of the underground pipe network, and river and lake water level. Use the underground pipe network conditions obtained from the real-time monitoring data set and the relevant topography, land type, rainfall, and land classification data of the test field as data input to build a two-dimensional flood numerical simulation model. Construct a pipeline equivalent siltation degree influence analysis model, which uses the integral average method to obtain the pipeline equivalent siltation degree, uses the flow calculation method based on Darcy's law to quantify the permeability coefficient of the soil above the pipeline, and uses the Colebrook-White formula to calculate the hydraulic roughness. Input the pipeline equivalent siltation degree, the permeability coefficient of the soil above the pipeline, and the hydraulic roughness calculated by the pipeline equivalent siltation degree influence analysis model into the two-dimensional flood numerical simulation model, and output the flooding depth of each flood risk assessment interval. Use the two-dimensional flood numerical simulation model to obtain the flooding depth of each flood risk assessment interval. Statistically analyze the unit area asset value of different land types in the test field, fit the vulnerability curve of residential land using the comprehensive curve method, and determine the vulnerability curves of other land types using the regression relationship of different land type vulnerability curves. Combine the vulnerability curve and the flooding depth of each flood risk assessment interval to obtain the asset loss of different flood risk assessment intervals under a specific rainfall scenario, determine the flood consequence grade of each flood risk assessment interval, and then establish a flood consequence evaluation model for each flood risk assessment interval, and output the flood consequence grade of each flood risk assessment interval. Combine the land type, pipeline equivalent siltation degree, and flooding depth of each flood risk assessment interval, introduce the Yabuki coefficient to improve the traditional TOPSIS method, and use the improved TOPSIS method to divide the flood probability grade, obtain the flood probability evaluation model for each flood risk assessment interval, and output the flood probability grade. Combine the flood consequence grade and flood probability grade of each flood risk assessment interval, use the improved risk matrix to construct a "probability-consequence" coupling evaluation model, and obtain the "probability-consequence" coupled flood risk assessment result. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1 A flow chart showing the method of the present application. DETAILED DESCRIPTION

[0065] As Figure 1 shown, the method for assessing regional flood risk considering pipe silting quantity of the present application is characterized in that it comprises the following steps:

[0066] Step S1, selecting a suitable site to build a flood test field, installing multiple types of sensing facilities, and collecting real-time monitoring data sets of flow and water level and river and lake water level; the real-time monitoring data sets include rainfall, soil infiltration rate, and data sets of key nodes of underground pipe network;

[0067] Step S2, taking the data obtained from the real-time monitoring data sets of step S1 as input, and building a two-dimensional flood numerical simulation model; the data includes the underground pipe network situation and the relevant topography, land type, rainfall, and land use classification data of the test field;

[0068] Step S3, constructing a pipe equivalent silting degree influence analysis model for flood, inputting the pipe equivalent silting degree, soil permeability coefficient, and hydraulic roughness of the soil above the pipe obtained by the pipe equivalent silting degree influence analysis model for flood into the two-dimensional flood numerical simulation model of step S2, and outputting the submergence depth of each flood risk assessment interval; wherein the pipe equivalent silting degree is obtained by integral averaging method, the soil permeability coefficient is quantified by using the flow calculation method based on Darcy's law, and the hydraulic roughness is calculated by using the Colebrook-White formula;

[0069] Step S4, obtaining the submergence depth of each flood risk assessment interval by using the two-dimensional flood numerical simulation model of step S3, combining the vulnerability curve and the submergence depth of each flood risk assessment interval, obtaining the asset loss of different flood risk assessment intervals under a specific rainfall scenario, determining the flood consequence grade of each flood risk assessment interval, and then establishing a flood consequence assessment model for each flood risk assessment interval, and outputting the flood consequence grade of each flood risk assessment interval; wherein the asset value per unit area of different land use types in the test field is counted, the residential land vulnerability curve is fitted by using the comprehensive curve method, and the regression relationship of the vulnerability curves of different land use types is used to determine the vulnerability curves of other land use types;

[0070] Step S5, in combination with the land type obtained in step S2, the pipe equivalent deposition degree obtained in step S3 and the submergence depth of each flood risk assessment interval, a flood probability grade is divided by using an improved TOPSIS method, a flood probability assessment model of each flood risk assessment interval is obtained, and the flood probability grade is output; the improved TOPSIS method is improved by introducing the Tanaka coefficient to the traditional TOPSIS method;

[0071] Step S6, in combination with the flood consequence grade of each flood risk assessment interval in step S4 and the flood probability grade in step S5, a probability-consequence coupled assessment model is constructed by using an improved risk matrix, and a probability-consequence coupled flood risk assessment result is obtained.

[0072] Further, in step S2, the specific steps are as follows:

[0073] Step S21, one-dimensional pipe and open channel flow hydraulic calculation is solved by solving complete Saint-Venant equation set, and full pipe flow under pressure is processed by using Preissmann slit method; a plurality of complex working conditions similar to the real situation can be fully realized for hydraulic calculation. The Saint-Venant equation set of open channel flow is expressed as:

[0074]

[0075] In the formula, A is the flow section area, Q is the flow, g is the gravity acceleration, θ is the angle between the pipe center line and the horizontal line, K is the conveyance, and S0 is the pipe bottom slope;

[0076] When the Saint-Venant equation set is applied to the pressurized flow of the pipe under overload, a conceptual Preissmann slit is needed to provide a conceptual free surface for the internal water flow of the pipe. The width of the slit itself is set according to the principle that the wave speed in the slit is 10 times the half-pipe height wave speed.

[0077] In the simulation of some special pipes, such as rising pipes and inverted siphons, the model directly uses a pressure pipe flow model instead of completely solving the Saint-Venant equation, and the control equation of the pressure pipe flow model is as follows:

[0078]

[0079] In the formula, A is the flow section area, Q is the flow, g is the gravity acceleration, K is the conveyance, and S0 is the pipe bottom slope;

[0080] Step S22, on the basis of step S21, two-dimensional water dynamic calculation adopts shallow water equation; following shallow water Bousinessq hypothesis and static pressure assumption, depth average approximation is carried out to the vertical direction flow velocity of Navier-Stokes equation; in two-dimensional shallow water flow, it is assumed that the flow is mainly in the horizontal direction, and the change in the vertical direction is ignored; the conservation type shallow water equation in the urban flood simulation model is expressed as:

[0081]

[0082] In the formula, t is time, x and y are spatial coordinates, h is water depth, u and v are the velocities in x and y directions respectively; q i is the i-th net source flux; u i and v i are the velocities in i and j directions respectively; g is gravitational acceleration, ε is eddy viscosity, S 0,x and S 0,y are the bed slope source terms in x and y directions respectively; S f,x and S f,y are the friction force source terms in x and y directions respectively; n is the number of source fluxes;

[0083] Step S23, a two-dimensional flood numerical simulation model is constructed by using the Saint-Venant equation set of step S21 open channel flow and the conservation type shallow water equation of step S22.

[0084] Further, in step S3, the specific steps are as follows:

[0085] Step S31, the pipe flow section loss degree caused by the siltation of different types of drainage pipes is defined as the equivalent siltation degree of the pipe, which is used as the evaluation standard of the loss degree of pipe flow characteristics; in the actual measurement process, the pipe siltation section area A c and the siltation degree S D are calculated by the following method;

[0086]

[0087] In the formula, D is the pipe diameter, and H is the siltation thickness;

[0088] Step S32, according to the calculation formula in step S31, the equivalent siltation degree is quantified by using the integral average method;

[0089] Step S33, on the basis of step S32, the permeation solving control equation is configured for the silted pipe, so as to reflect the change of pipe permeability caused by pipe siltation. It is assumed that V f represents the void fraction, and the control equation of the permeable medium pipe channel model is expressed as follows based on the flow calculation method based on Darcy's law:

[0090]

[0091] wherein P c is the permeability coefficient, S p is the cross-sectional area of the water permeable medium, and Δh / L is the hydraulic slope; Vf is the porosity;

[0092] In step S34, the hydraulic roughness is calculated by using the Colebrook-White formula; when the pipeline is silted, the roughness inside the pipeline will inevitably change, therefore, the pipeline roughness in the Colebrook-White formula is classified and set; one roughness is used for the bottom one-third, and the other is used for the other part of the cross section; the mathematical expression of the hydraulic roughness is as follows:

[0093] Free surface:

[0094] Closed full flow:

[0095] wherein f is the frictional resistance coefficient, k is the equivalent roughness of the pipeline, R e is the Reynolds number, and R is the hydraulic radius; according to the pipeline siltation condition, the appropriate equivalent roughness is selected, and the overcurrent resistance caused by the pipeline siltation is brought into the flood numerical simulation model;

[0096] In step S35, the equivalent siltation degree in step S32, the permeability coefficient in step S33 and the hydraulic roughness in step S34 are input into the two-dimensional flood numerical simulation model in step S2, and the submergence depth of each flood risk assessment interval is output.

[0097] Further, in step S4, the specific steps are as follows:

[0098] In step S41, the asset value per unit area of different land use types is obtained according to the statistical yearbook of the city to which the test field belongs in the previous year;

[0099] In step S42, on the basis of step S41, the asset composition inside the building is determined, mainly including the indoor decoration cost, the building repair cost and the indoor decoration cost, the combination relationship between the flood depth and the asset loss inside the building is established, that is, the flood submergence threshold of each type of asset is set; the asset loss under different submergence depths is calculated, the cumulative asset loss is calculated; the asset loss rate under different submergence depths is used to fit the residential land flood loss rate curve, and the residential land vulnerability curve is obtained as follows:

[0100]

[0101] wherein x is the submergence depth, y is the asset loss rate (%), and R is the correlation coefficient;

[0102] Step S43, the land use types of the study area are divided into four types, i.e. residential land, public service land, green land, and education and research land. On the basis of step S42, the correlation between the residential land and other different land types is analyzed by linear regression analysis test, and a regression equation of the flood vulnerability curve is established; the flood vulnerability curve of the residential land is used to conduct regression analysis on the flood vulnerability curves of other land types; the vulnerability of different land types is quantified as shown below. landuse

[0103] lr landuse = a landuse ln(x r ) + b landuse (9)

[0104] wherein x r is the coordinate point of the land type; a landuse and b landuse are related parameters;

[0105] In combination with the vulnerability curve and the submergence depth of each flood risk assessment interval, the asset loss of different flood risk assessment intervals under a specific storm scenario is obtained, and the flood consequence level of each flood risk assessment interval is determined.

[0106] Further, in step S5, the specific steps are as follows:

[0107] Step S51, taking the submergence interval output by the flood numerical simulation model as the basic flood probability assessment unit, the maximum submergence depth, submergence duration, maximum submergence speed, unit maximum flow, land type, and comprehensive health rate of the downstream pipe of the catchment inlet of the subset water area are included in the assessment of the flood probability; the calculation formula of the pipe health rate downstream of the catchment inlet of the subset water area is as follows:

[0108]

[0109] wherein ζ is the comprehensive health rate, z i represents the health degree of the i-th pipe, and e represents the number of pipes downstream of the catchment inlet of the subset water area to which the 2D area belongs.

[0110] Step S52, the unacceptability of the maximum submergence depth, submergence duration, maximum submergence speed, unit maximum flow, land type, and pipe health rate in step S51 is graded and expressed, and the unacceptability interval in which all the different parameters of the 2D area are located is given according to the graded expression.

[0111] ​Step S53, according to the unacceptable interval of different parameters in step S52, the Takane coefficient is introduced to improve the traditional TOPSIS method, and the improved TOPSIS method is used to divide the flood probability level, and a flood probability evaluation model of each flood risk evaluation interval is obtained;

[0112] wherein, the Takane coefficient is used to evaluate the similarity between two groups of data, assuming that there are two groups of data X=(x1, x2, …, x n ), Y=(y1, y2, …, y n ), the Takane coefficient can be expressed as:

[0113]

[0114] The improved TOPSIS method uses linear weighting to determine the subjective and objective combined weight ω j of the index; x i is the value of the i-th sample in the array X; y i is the value of the i-th sample in the array Y;

[0115]

[0116] In the formula, ω j , ω' j and are the combined weight, the weight determined by the AHP method and the weight determined by the improved entropy weight method; γ is the proportion of the improved entropy weight method weight in the comprehensive weight, and ω'1, ω'2, … ω' q are the weights of the AHP method arranged from small to large, and q is the number of indexes; the weight of each index w=(ω1, ω2, …, ω n ) T is obtained by the above analysis;

[0117] The index weighting normalization matrix in the improved TOPSIS method is X={x ij} m×n , wherein,

[0118] x ij = ω j b ij , i=1, 2, … m, j=1, 2, 3 … n; the j-th characteristic attribute value of the ideal solution and the negative ideal solution is and The similarity of the i-th weighted normalized sample to the ideal solution and the negative ideal solution can be expressed as:

[0119]

[0120] The relative closeness of the i-th sample in the improved TOPSIS method can be expressed as

[0121] Further, in step S6, the specific steps are as follows:

[0122] Step S61, input the flood consequence grade of each flood risk assessment interval of step S4 and the flood probability grade result of step S5.

[0123] Step S62, on the basis of step S61, introduce the improved risk matrix analysis of disaster theory principle, divide the flood risk into 5 categories: high risk-high economic vulnerability land (risk grade 1), high risk-low economic vulnerability land (risk grade 2), medium risk-medium economic vulnerability land (risk grade 3), low risk-high economic vulnerability land (risk grade 4), low risk-low economic vulnerability land (risk grade 5).

[0124] Table 1 statistical results of flood risk assessment accuracy

[0125] Risk level 1 Risk level 2 Risk level 3 Risk level 4 Risk level 5 Accuracy rate 87.2％ 89.4％ 93.7％ 86.9％ 95.8％

[0126] In the utility example, taking a high-tech development zone of a city as an example, the flood risk of the area is evaluated by the method in the application before the rainstorm, and the evaluation result is compared with the measured result of 30 measuring points after the rainstorm. The statistical results of the accuracy of the 5 risk grade divisions of the 30 measuring points are shown in Table 1. The accuracy of the 5 risk grade divisions is higher than 85%.

Claims

1. A method for regional flood risk assessment that considers pipeline siltation quantification, characterized in that: Includes the following steps: Step S1: Select a suitable site to build a flood test field, install various types of sensing facilities, and collect real-time monitoring datasets of flow rate, water level, and river and lake water levels. Step S2: Using the data obtained from the real-time monitoring dataset in Step S1 as input, build a one- or two-dimensional flood numerical simulation model. Step S3: Construct an analysis model of the impact of pipeline equivalent sedimentation degree on flooding. Input the pipeline equivalent sedimentation degree, the permeability coefficient of the soil above the pipeline and the hydraulic roughness calculated by the analysis model of the impact of pipeline equivalent sedimentation degree on flooding into the one-dimensional flood numerical simulation model in step S2, and output the inundation depth of each flood risk assessment interval. Step S4: Using the one- or two-dimensional flood numerical simulation model in step S3, the inundation depth of each flood risk assessment interval is obtained. Combining the vulnerability curve and the inundation depth of each flood risk assessment interval, the asset loss of different flood risk assessment intervals under a specific rainstorm scenario is obtained, the flood consequence level of each flood risk assessment interval is determined, and then a flood consequence assessment model for each flood risk assessment interval is established, and the flood consequence level of each flood risk assessment interval is output. Step S5: Combining the land type obtained in Step S2, the pipeline equivalent siltation degree obtained in Step S3, and the inundation depth of each flood risk assessment interval, the improved approximation ideal solution sorting method is used to classify the flood probability level, obtain the flood probability assessment model of each flood risk assessment interval, and output the flood probability level. Step S6: Combining the flood consequence level of each flood risk assessment interval in step S4 with the flood probability level in step S5, a "probability-consequence" coupled assessment model is constructed using an improved risk matrix to obtain the "probability-consequence" coupled flood risk assessment result.

2. The regional flood risk assessment method considering pipeline siltation quantification according to claim 1, characterized in that: In step S2, the specific steps are as follows: Step S21: The hydraulic calculation of one-dimensional open channel flow uses a fully solved Saint-Venant equation set. Pressurized full pipe flow is handled using the Pressman slit method. The Saint-Venant equation set for open channel flow is expressed as: （1）； In the formula, A is the cross-sectional area of ​​the flow, Q is the flow rate, g is the acceleration due to gravity, θ is the angle between the center line of the pipe and the horizontal line, K is the water delivery rate, and S0 is the slope of the bottom of the pipe. The governing equations for the pressure pipe flow model are as follows: （2）； In the formula, A is the cross-sectional area of ​​the flow path, Q is the flow rate, g is the acceleration due to gravity, K is the water conveyance rate, and S0 is the slope of the bottom of the pipe. Step S22: Based on step S21, the two-dimensional hydrodynamic calculation adopts the shallow water equation; following the shallow water Bousineske assumption and the hydrostatic assumption, the Navier-Stokes equation is approximated by depth-averaged velocity along the vertical direction; in two-dimensional shallow water flow, it is assumed that the flow is mainly in the horizontal direction, and its variation in the vertical direction is ignored; the conservation type of the shallow water equation in the urban flood simulation model is expressed as: （3）； In the formula, t is time, x and y are spatial coordinates, h is water depth, and u and v are velocities in the x and y directions, respectively. Let i be the flux of the i-th net source term; and ε represents the velocities in the i and j directions, respectively; g is the acceleration due to gravity, and ε is the eddy current viscosity. and These represent the source terms of the riverbed's bottom slope in the x and y directions, respectively. and , respectively, represent the frictional force source terms in the x and y directions; n is the number of source term fluxes; Step S23: Using the Saint-Venant equations for open channel flow from step S21 and the conservation-type shallow water equations from step S22, construct a one- or two-dimensional flood numerical simulation model.

3. The regional flood risk assessment method considering pipeline siltation quantification according to claim 1, characterized in that: In step S3, the specific steps are as follows: Step S31 defines the pipe flow cross-sectional area loss caused by siltation in different types of drainage pipes as the equivalent siltation degree, which serves as an evaluation standard for the degree of loss in pipe flow characteristics. In actual measurement, the silted cross-sectional area of ​​the pipe... and siltation It is calculated in the following way; （4）； In the formula, D is the pipe diameter and H is the siltation thickness; Step S32: Based on the calculation formula in step S31, combine the integral averaging method to convert the siltation of each pipeline into an equivalent siltation degree; Step S33: Based on step S32, configure the permeability control equations for the silted pipe. The control equations for the permeable medium pipe model are expressed as follows: （5）； In the formula, P c S is the permeability coefficient. p The cross-sectional area of ​​the permeable medium. For hydraulic gradient; V f Porosity; Step S34: Calculate the hydraulic roughness using the Colbrook-White formula; the mathematical expression for hydraulic roughness is as follows: Freeform surface: (6); Closed full flow: (7); In the formula, f is the friction factor, k is the equivalent roughness of the pipe, and R... e R is the Reynolds number and R is the hydraulic radius. Based on the pipeline siltation situation, an appropriate equivalent roughness is selected to incorporate the flow resistance caused by pipeline siltation into the flood numerical simulation model. Step S35: Input the equivalent sedimentation degree from step S32, the permeability coefficient from step S33, and the hydraulic roughness from step S34 into the one-dimensional flood numerical simulation model in step S2, and output the inundation depth of each flood risk assessment interval.

4. The regional flood risk assessment method considering pipeline siltation quantification according to claim 1, characterized in that: In step S4, the specific steps are as follows: Step S41: Obtain the asset value per unit area for different land use types based on the previous year's statistical yearbook of the city where the test site is located; Step S42: Based on step S41, determine the asset composition inside the building, establish the combination relationship between flood depth and the loss of each asset inside the building, calculate the asset loss at different inundation depths, and calculate the cumulative asset loss; use the asset loss rate at different inundation depths to fit the flood loss rate curve of residential land to obtain the vulnerability curve of residential land as follows: （8）； In the formula, x is the flooding depth, y is the asset loss rate, and R is the correlation coefficient; Step S43: Based on step S42, the correlation between residential land and other land use types is examined through linear regression analysis to establish the regression equation for the flood vulnerability curve; the flood vulnerability curve of residential land is used to perform regression analysis on the flood vulnerability curves of other land use types; the vulnerability of different land use types... The quantification is shown below; （9）； In the formula, The coordinates of the land use type; and For relevant parameters; By combining vulnerability curves and inundation depths of each flood risk assessment interval, asset losses in different flood risk assessment intervals under specific rainstorm scenarios are obtained, and the flood consequence level of each flood risk assessment interval is determined.

5. A regional flood risk assessment method considering pipeline siltation quantification according to claim 1, characterized in that: In step S5, the specific steps are as follows: Step S51: Using the inundation interval output by the flood numerical simulation model as the basic assessment unit for flood probability, the maximum inundation depth, inundation duration, maximum inundation velocity, maximum flow rate per unit area, land type, and the comprehensive health rate parameters of the downstream pipelines of the catchment area within the unit interval are incorporated into the flood probability assessment. The formula for calculating the pipeline health rate downstream of the catchment area is as follows: （10）； In the formula, For the overall health rate, z i The health status of the i-th pipe is indicated, and e represents the number of pipes downstream of the water inlet of the sub-catchment area to which region 2D belongs; Step S52: Classify the unacceptability of the maximum inundation depth, inundation duration, maximum inundation rate, maximum flow rate per unit, land type and pipeline health rate in step S51, and give the unacceptable range of different parameters in all 2D areas according to the classification. Step S53: Based on the unacceptable range of different parameters in step S52, the Tanimoto coefficient is introduced to improve the traditional approximation ideal solution ranking method, and the improved approximation ideal solution ranking method is used to classify flood probability levels, so as to obtain the flood probability assessment model for each flood risk assessment range. The Tanimoto coefficient is used to evaluate the similarity between two sets of data, assuming there are two arrays. Then the Tanimoto coefficient can be expressed as: （11）； The improved ranking method for approximating the ideal solution uses linear weighting to determine the subjective and objective combination weights of the indicators. ;x i Let y be the value of the i-th sample in array X; i Let i be the value of the i-th sample in array Y; （12）； In the formula, , and These are the combined weights, the weights determined by the analytic hierarchy process (AHP), and the weights determined by the improved entropy weight method, respectively. The proportion of the improved entropy weight method in the overall weight, and ; , ,… The weights of the indicators are arranged in ascending order, with q representing the number of indicators; the weights of each indicator are obtained from the above analysis. ; The index-weighted normalization matrix in the improved approximation ideal solution ranking method is: ,in, The j-th characteristic attribute values ​​of the ideal solution and the negative ideal solution are respectively and The similarity between the i-th weighted normalized sample and the ideal solution and the negative ideal solution. , It can be represented as: （13）； In the improved approximation of ideal solution sorting method, the relative proximity of the i-th sample can be expressed as: .

6. The regional flood risk assessment method considering pipeline siltation quantification according to claim 1, characterized in that: In step S6, the specific steps are as follows: Step S61: Input the flood consequence level of each flood risk assessment interval in step S4 and the flood probability level results in step S5. Step S62: Based on step S61, an improved risk matrix analysis is conducted by introducing disaster theory principles, classifying flood risks into 5 categories: high-risk - high economic vulnerability land, high-risk - low economic vulnerability land, medium-risk - medium economic vulnerability land, low-risk - high economic vulnerability land, and low-risk - low economic vulnerability land.

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