CN114049031A - Method for optimizing site selection of rainwater storage tank - Google Patents

Method for optimizing site selection of rainwater storage tank Download PDF

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CN114049031A
CN114049031A CN202111384975.5A CN202111384975A CN114049031A CN 114049031 A CN114049031 A CN 114049031A CN 202111384975 A CN202111384975 A CN 202111384975A CN 114049031 A CN114049031 A CN 114049031A
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李红艳
张翀
张峰
史文韬
李尚明
马熠阳
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Taiyuan University of Technology
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Abstract

The invention discloses a method for optimizing site selection of a rainwater storage tank, and belongs to the technical field of rainwater storage tank design. The main content of the invention is as follows: based on an SWMM model, firstly determining two factors of ponding and overload which affect urban waterlogging, constructing a node waterlogging risk assessment index system, then combining subjective weights obtained by each index analytic hierarchy process and objective weights obtained by an improved entropy value process by using a game theory idea for weighting, finally obtaining the relative closeness between each node index and a positive and negative ideal scheme by utilizing grey correlation analysis-approximate ideal solution sorting, and arranging a regulation pool at a node with larger relative closeness. The invention introduces the method of game theory and grey correlation analysis-approaching ideal solution sorting for the first time to address the regulation pool, and overcomes the defects of single method weighting and decision. The method has the advantages that more factors are considered during urban waterlogging risk assessment and regulation pool site selection, the obtained site selection result is more reasonable, and the method has obvious advantages in the aspect of effectively reducing urban waterlogging risk.

Description

Method for optimizing site selection of rainwater storage tank
Technical Field
The invention relates to the technical field of rainwater storage tank design, in particular to a method for optimizing site selection of a rainwater storage tank.
Background
At present, in the urban inland inundation risk control method caused by rainfall, regulation and storage facilities are mostly arranged at the tail end of a shunt drainage system, a numerical model calculation method is the first choice for determining the regulation and storage scale, and common comprehensive models integrating surface production flow and pipe network confluence are INFORWORKS, MIKE Flood and the like. In the case of decentralized storage, the main method is as follows: (1) the regulation reservoir was placed at a location where the depth of the water accumulation was greater using INFORWORKS simulations. (2) And (4) simulating by using two-dimensional commercial model software (MIKE Flood) to obtain accumulated water points with the accumulated water depth exceeding 15cm, wherein the accumulated water points are all selected as the final positions of the rainfall Flood regulation and storage pool.
However, the above method has the following disadvantages: (1) factors which influence urban waterlogging comprehensively are not considered during the position decision of the storage regulation pool. (2) In the analytic hierarchy process, subjective factors have a large influence on the site selection result, so that information loss is caused, and the accuracy of decision is reduced. (3) Meanwhile, all nodes with the accumulated water depth exceeding 15cm are arranged as the final positions of the storage regulation pool, the design is not reasonable, and the cost of the whole project is high.
Disclosure of Invention
The invention discloses a method for optimizing a site of a rainwater storage tank, which aims to overcome the defects in the prior art and scientifically and reasonably optimize the position of the storage tank.
The invention adopts the following technical scheme:
a method for optimizing and selecting a site of a rainwater storage tank is based on an SWMM model and urban waterlogging prevention and control standards, rainwater system nodes are used as evaluation objects, the position of the storage tank is used as a target, the waterlogging depth, the waterlogging area, the waterlogging time, the overload time and the overload height of the nodes are used as evaluation indexes, the subjective weight obtained by each index analytic hierarchy process and the objective weight obtained by an improved entropy method are combined and weighted by using a game theory idea, the relative closeness between each node index and a positive-negative ideal scheme is obtained by using gray correlation analysis-approaching ideal solution sorting, the waterlogging risk sorting of the nodes is determined according to the relative closeness, and the storage tank is arranged at the nodes with large waterlogging risks; the method comprises the following specific steps:
step 1, constructing an index system;
step 2, establishing an SWMM model, inputting rainfall data serving as driving data into the SWMM model to obtain index data in all node index systems, and preprocessing the index data;
step 3, weighting each index by utilizing an analytic hierarchy process and an improved entropy method, and combining the indexes and weighting by utilizing a game theory;
and 4, carrying out waterlogging risk assessment by combining gray correlation analysis and approximate ideal solution sorting.
The method, the step 1 comprises the following steps:
the method has the advantages that the factors influencing the site selection of the regulation pool are more, the representativeness, the reliability and the limitation of a data source are considered based on the purpose of urban inland inundation treatment and the principles of scientificity, systematicness and rationality, and two factors, namely node ponding directly related to inland inundation and node overload not considered by the former people, are selected. Selecting factors directly related to waterlogging when establishing an evaluation index system: node water accumulation and node overload; the node water accumulation indexes comprise water accumulation depth, water accumulation time and water accumulation area, and the node overload indexes comprise overload time and overload height.
Wherein the ponding time, the overload time and the overload height in the index system can be directly read by the result report of the SWMM simulation. The depth and area of accumulated water can be calculated from the volume of accumulated water, the width of catchment area and the slope of ground respectively, as shown in the following formula.
Figure BDA0003366731770000021
Figure BDA0003366731770000022
In the formula: s is the water accumulation area, m2(ii) a W is catchment width m; v is the volume of waterlogging, m3(ii) a i is the ground slope,%; h is the depth of water accumulation, cm.
The method, the step 2 comprises the following steps:
step 201, design parameters of the rainwater system include: a rainstorm intensity formula, a design recurrence period, a runoff coefficient, ground water collection time and a reduction coefficient; determining rain system attribute data from hydraulic calculations of the rain system, the rain system attribute data comprising: building an SWMM model according to rainwater system attribute data by using the pipe diameter, gradient, pipe inner bottom elevation and pipe buried depth of each designed pipe section; inputting the designed rainstorm in the corresponding reappearance period specified by the urban inland inundation prevention and control standard as a rainfall condition into the established SWMM model for rainwater system simulation;
step 202, according to the existing urban waterlogging prevention and treatment planning standard, when the depth of ponding exceeds 15cm, urban traffic is inconvenient, firstly, nodes with the depth of ponding exceeding 15cm in a research area are screened out, and evaluation and site selection are carried out on the nodes; assuming that m nodes with water depth exceeding 15cm exist in a research area, each node has n evaluation indexes, and establishing a decision matrix A (x)ij)m×n，xijData for inode j indices;
step 203, adopting an extreme value processing method as a pretreatment method; the specific expression is
Figure BDA0003366731770000031
From the above formula, normalized matrix B ═ y can be obtainedij)m×n，yijNormalized data for inode j index.
The method, the step 3 comprises the following steps:
301, weighting the analytic hierarchy process;
the specific operation steps are as follows:
1) establishing a hierarchical structure; dividing the decision-making related factors into a plurality of layers according to the attributes;
2) constructing a judgment matrix step by step; constructing a judgment matrix T ═ a (a) by a 1-9 scaling methodij)t×tWherein a isijIs an index T in the criterion layeriRelative to TjThe specific decision scale quantization rule is shown in the following table:
Figure BDA0003366731770000032
Figure BDA0003366731770000041
3) calculating the weight of each index; calculating each index weight W of criterion layer as (omega)1,ω2…ωt) Wherein:
Figure BDA0003366731770000042
in the formula aijIs an index T in the criterion layeriRelative to TjThe degree of importance of;
4) checking the consistency; solving the maximum eigenvalue lambda of the decision matrix TmaxCalculating a CR value, and if CR is less than 0.1, determining that the judgment matrix meets the requirement of consistency; determining the eigenvector omega corresponding to the maximum eigenvalue as the weight vector of the index of the layer relative to the target layer of the previous layer; the formula for calculating the CR value is:
Figure BDA0003366731770000043
in the formula, t is the index number in the standard layer; RI is an average random consistency index;
the specific value rules of RI are shown in the following table:
Figure BDA0003366731770000044
step 302, improving entropy method weighting;
the improved entropy method is an objective weighting method for determining weight according to variation information entropy of indexes, and comprises the following steps:
1) y is obtainedijCharacteristic specific gravity p ofij(ii) a To avoid pijIs zero by pair yij0.1 is uniformly added to improve the entropy method, so that the entropy method has wider adaptability and scientificity;
Figure BDA0003366731770000045
2) calculating the information entropy e of j indexj；
Figure BDA0003366731770000046
3) Weighting w of j indexj；
Figure BDA0003366731770000051
Step 303, combining and weighting game theory;
the method utilizes the advantages of game theory comprehensive subjective and objective weighting to carry out combined weighting on the indexes, and comprises the following steps:
1) respectively weighting the indexes by using L different weighting methods to construct a basic weight vector set;
let uk＝{uk1,uk2,…,ukn(K ═ 1,2, …, L) noting that any linear combination of these L different vectors is
Figure BDA0003366731770000052
In the formula: u is one possible weight vector of the weight set; alpha is alphakIs a linear combination coefficient;
2) optimizing L linear combination coefficients alpha by using game theory ideakSo that u is associated with each uknWith minimal dispersion, i.e.
Figure BDA0003366731770000053
Optimizing the first derivative condition may be switched to
Figure BDA0003366731770000054
3) Obtaining (alpha)1,α2,…,αL) Followed by a normalization process, i.e.
Figure BDA0003366731770000055
Finally, the most satisfactory comprehensive weight vector is obtained as
Figure BDA0003366731770000056
The indexes are respectively weighted by using AHP and an Improved entropy method (IEVM), and the indexes are combined and weighted by using Game Theory (GT). The determination of the index weight is a crucial step in the multi-attribute decision, and the accurate index weight has an important influence on a reasonable decision result. Currently, the main weighting methods are classified into subjective weighting methods and objective weighting methods. The subjective weighting method is to carry out weighting through subjective expert knowledge and work experience, and the objective weighting method is to solve the weight by establishing a mathematical model through decision data. Subjective weighting and objective weighting have the advantages and disadvantages, and when a certain weighting method is used independently, the weighting is inaccurate due to the fact that information of a certain aspect is ignored, and then evaluation sequencing results are influenced. The invention integrates the advantages of subjective and objective weighting, provides a game theory combined weighting method for the first time, and can greatly reduce the information loss caused by independent weighting.
The method, the step 4 comprises the following steps:
step 401, solving a weighting specification matrix C; each column of the matrix B is associated with a game theory weight corresponding to the column
Figure BDA0003366731770000061
Multiplied by C ═ Zij)m×n，ZijFor normalizing data yijAnd
Figure BDA0003366731770000062
product of (i)
Figure BDA0003366731770000063
Step 402, determining a positive ideal solution set
Figure BDA0003366731770000064
Screening out the optimal values of each index of the matrix C to form a positive ideal solution set, and using the positive ideal solution set as a reference number sequence in the GRA model, namely
Figure BDA0003366731770000065
In the formula: z is a radical ofj(j) Z for each evaluation nodeijA value;
step 403, solving the grey correlation coefficient r between each evaluation node and the positive ideal solution seti(j)；
Figure BDA0003366731770000066
In the formula: λ is a resolution coefficient;
the gray correlation coefficient matrix obtained by the above formula is R ═ (R)i(j))m×n；
Step 404, determine the positive ideal solution set of the matrix R
Figure BDA0003366731770000067
Negative ideal solution set
Figure BDA0003366731770000068
Figure BDA0003366731770000069
Step 405, calculate inodes to
Figure BDA00033667317700000610
Euclidean distance of
Figure BDA00033667317700000611
Figure BDA00033667317700000612
Step 406, solving the gray relative closeness T of each evaluation node;
Figure BDA0003366731770000071
and T determines the waterlogging risk ranking of each node, and if the T is larger, the waterlogging risk is larger, and a regulation and storage pool should be arranged at the point at first.
The risk assessment of waterlogging is performed by using Gray correlation analysis (GRA) in combination with approximate ideal solution ordering (TOPSIS). Both GRA and TOPSIS are methods of ordering decisions by calculating the closeness between the index sequence and the positive and negative ideal state sequences for each decision scheme, and both methods have disadvantages. Wherein, the GRA method only considers the shape similarity of the evaluation scheme and the reference scheme, the TOPSIS method only considers the similarity on the position, and the dynamic change of the evaluation index is not considered. The GRA method can better reflect the internal change rule of each scheme to make up the deficiency of the TOPSIS method.
Compared with the prior art, the invention has the following technical effects: (1) the invention establishes a pervasive and complete urban waterlogging risk assessment index system, applies the game theory idea to integrate subjective and objective weights, can not only integrally analyze the primary and secondary factors influencing urban waterlogging, but also furthest avoid uncertain factors and reduce information loss caused by independent weighting, has an improvement effect on urban waterlogging in the rainstorm recurrence period with multiple scenes, and improves the applicability and the accuracy of the method. (2) The invention firstly proposes that the GRA-TOPSIS method is applied to the site selection of the regulation and storage pool, overcomes the limitations of the TOPSIS method and the GRA method, and ensures that the relative closeness of the node waterlogging risks is more accurate, thereby more reasonably reflecting the size of the node waterlogging risks; (3) based on the GRA-TOPSIS waterlogging risk evaluation model, the obtained node waterlogging comprehensive evaluation value has the largest coefficient of variation, is more superior to the method of using the GRA method or the TOPSIS method independently, and is beneficial to more finely and accurately sequencing the node waterlogging risks.
Drawings
FIG. 1 is a flow chart of a method for optimizing site selection for a rainwater storage tank;
FIG. 2 is a diagram of a regulation pool site selection evaluation index system;
FIG. 3 is a SWMM model of an urban rainfall flood for an area to be treated;
FIG. 4 is the relative closeness of Euclidean distance to gray of a node with water depth exceeding 15cm in a 5-year one-chance situation.
Detailed Description
To further illustrate the features of the present invention, refer to the following detailed description of the invention and the accompanying drawings.
As shown in fig. 1, the present embodiment discloses a method for optimizing site selection of a rainwater storage tank, which includes the following steps S1 to S4;
s1, selecting factors directly related to waterlogging when establishing an evaluation index system: node water accumulation and node overload. The indexes of node ponding comprise ponding depth, ponding time and ponding area, and the indexes of node overload comprise overload time and overload height.
And S2, constructing an SWMM model, and obtaining and preprocessing index data of all nodes as shown in figure 3. The city plan designs 5 regulation pools to improve urban waterlogging in the case of 5a and 10 a.
And S3, respectively weighting each index by using AHP and IEVM, and combining and weighting the indexes by using GT.
And S4, carrying out waterlogging risk assessment by combining GRA and TOPSIS.
Specifically, in the step S2, the specific process of constructing the urban rainfall flood model SWMM of the area to be treated is as follows:
(1) determining an implementation range: according to the region discharge opening and the intention region to be treated, the pipe network data are arranged, and the catchment range, the land utilization type distribution and the like are clearly combed.
(2) Pipe network data input: and (3) arranging and inputting the position gradient and size data of the actual pipe network pipe section in the area, the position size and elevation data of the inspection well, the position size and elevation data of the rainwater grate, the position size and elevation data of the land parcel access point and the position size and elevation data of the main drainage port into a model.
(3) Parameter determination: according to engineering experience and a model instruction manual, important parameter distribution of the model is set, and the method comprises the following steps: catchment area, comprehensive runoff coefficient, pipeline length, maximum and minimum infiltration rate, osmotic attenuation coefficient, average slope of catchment area and water-tight rate of catchment area.
Specifically, the short-duration rainfall data is a short-duration chicago rain type generated by adopting a rainstorm intensity formula, wherein the rainstorm intensity formula is as follows:
Figure BDA0003366731770000081
in the formula: q is rainfall intensity, L/(s.hm)2) (ii) a T is a recurrence period, a; t is rainfall duration, min.
Specifically, in the step S3, the indexes are weighted by using an analytic hierarchy process and an improved entropy method, and the two indexes are weighted by using a game theory in a combination manner as follows:
(1) AHP entitlement
1) And establishing a hierarchical structure. The decision-related factors are divided into multiple levels according to attributes.
2) Step-by-step structure judgment momentAnd (5) arraying. Constructing a judgment matrix T ═ a (a) by a 1-9 scaling methodij)t×tWherein a isijIs an index T in the criterion layeriRelative to TjThe degree of importance of (a) is specifically determined by the scale quantization rule shown in the following table.
Figure BDA0003366731770000091
3) And calculating the weight of each index. Calculating each index weight W of criterion layer as (omega)1,ω2…ωt) Wherein:
Figure BDA0003366731770000092
in the formula aijIs an index T in the criterion layeriRelative to TjThe degree of importance of.
4) And (5) checking the consistency. Solving the maximum eigenvalue lambda of the decision matrix TmaxAnd calculating a CR value, and if CR is less than 0.1, determining that the matrix meets the consistency requirement. The eigenvector ω corresponding to the largest eigenvalue can be determined as the weight vector of the index of the layer relative to the target layer of the previous layer. The formula for calculating the CR value is:
Figure BDA0003366731770000093
in the formula, t is the index number in the standard layer; RI is the average random consistency index.
The specific value rules of RI are shown in the following table.
Figure BDA0003366731770000101
(2) IEVM empowerment
1) Y is obtainedijCharacteristic specific gravity p ofij. To avoid pijIs zero by pair yijUniformly adding 0.1 to improve the entropy method, so that the entropy method has wider adaptability and scienceAnd (4) sex.
Figure BDA0003366731770000102
2) Calculating the information entropy e of j indexj。
Figure BDA0003366731770000103
3) Weighting w of j indexj。
Figure BDA0003366731770000104
(3) GT combination empowerment
1) And respectively weighting the indexes by using L different weighting methods to construct a basic weight vector set.
Let uk＝{uk1,uk2,…,ukn(K ═ 1,2, …, L) noting that any linear combination of these L different vectors is
Figure BDA0003366731770000105
In the formula: u is one possible weight vector of the weight set; alpha is alphakAre linear combination coefficients.
2) Optimizing L linear combination coefficients alpha by using game theory ideakSo that u is associated with each uknWith minimal dispersion, i.e.
Figure BDA0003366731770000106
Optimizing the first derivative condition may be switched to
Figure BDA0003366731770000107
3) Obtaining (alpha)1,α2,…,αL) Followed by a normalization process, i.e.
Figure BDA0003366731770000111
Finally, the most satisfactory comprehensive weight vector is obtained as
Figure BDA0003366731770000112
The subjective weight obtained by the analytic hierarchy process and the objective weight obtained by the improved entropy method are integrated by using the idea of game theory, and the indexes are combined and weighted, and the result is shown in the following table.
Figure BDA0003366731770000113
Subjective weighting and objective weighting have advantages and disadvantages respectively, and the evaluation result has certain limitation when the subjective weighting and the objective weighting are used independently. As can be seen from the above table, different calculation principles and emphasis points cause the weight distribution obtained by AHP and IEVM to be different, and GT can coordinate such contradictions, for example, the weight given by experts for the water accumulation area index is only 0.123, the weight obtained by IEVM is as high as 0.288, and the weight obtained by GT optimization is 0.169.
Specifically, in the above step S4, the specific process of performing the waterlogging risk assessment by using the GRA in combination with the IEVM is as follows:
(1) and solving a weighting specification matrix C. Each column of the matrix B is associated with a game theory weight corresponding to the column
Figure BDA0003366731770000114
Multiplied by C ═ Zij)m×n(ZijFor normalizing data yijAnd
Figure BDA0003366731770000115
product of (i)
Figure BDA0003366731770000116
)。
(2) Determining a positive ideal solution set
Figure BDA0003366731770000117
Screening out the optimal values of each index of the matrix C to form a positive ideal solution set, and using the positive ideal solution set as a reference number sequence in the GRA model, namely
Figure BDA0003366731770000118
In the formula: z is a radical ofj(j) Z for each evaluation nodeijThe value is obtained.
(3) Solving the grey correlation coefficient r of each evaluation node and the positive ideal solution seti(j)。
Figure BDA0003366731770000121
In the formula: λ is the resolution factor.
In the calculation, the value of λ is always 0.5, but no matter how the reference number sequence and the comparison number sequence are changed, the lower limit value of the corresponding correlation coefficient is 0.3333, which is obviously unreasonable. The resolution coefficient lambda is used as the coefficient of the maximum value, so that the influence of each factor of the system on the correlation degree is fully reflected, and meanwhile, the anti-interference effect is realized, namely, the influence of the abnormal value in the observation sequence on the error of the whole correlation space can be weakened. And combining the data of the actual research to obtain lambda which is 0.3.
The gray correlation coefficient matrix obtained by the above formula is R ═ (R)i(j))m×n。
(4) Determining a positive ideal solution set for matrix R
Figure BDA0003366731770000122
Negative ideal solution set
Figure BDA0003366731770000123
Figure BDA0003366731770000124
(5) Respectively calculate inodes to
Figure BDA0003366731770000125
Euclidean distance of
Figure BDA0003366731770000126
Figure BDA0003366731770000127
(6) And solving the gray relative closeness T of each evaluation node.
Figure BDA0003366731770000128
And T determines the waterlogging risk ranking of each node, and if the T is larger, the waterlogging risk is larger, and a regulation and storage pool should be arranged at the point at first.
The Euclidean distance and gray relative closeness of each node with the water accumulation depth exceeding 15cm in the 5-year one-encounter condition in the recurrence period are obtained by a GRA-TOPSIS model, and compared with a traditional TOPSIS method, the nodes are arranged according to the sequence of the closeness from small to large, as shown in figure 4. The closeness in FIG. 4 is fitted linearly, the fit determining the coefficient R2As high as 0.971, the evaluation value distribution obtained by the GRA-TOPSIS model is verified to be uniform and reasonable.
In order to enhance the contrast, the GRA-TOPSIS method was compared not only with the TOPSIS method but also with the GRA method, and the results are shown in the following table. It should be noted that the weights used in these 3 methods are the game theory weights in table 3, and the data preprocessing methods are all extreme value normalization methods. As can be seen from the following table, the bit order difference of the final comprehensive ordering of the same node by the GRA-TOPSIS method, the GRA method and the TOPSIS method is not more than 2. Meanwhile, the correlation degree of the GRA-TOPSIS method and other 2 methods is tested by using a Spearman grade correlation coefficient test method, the correlation coefficients are respectively 0.976 and 0.939(P is less than 0.01), and the 3 evaluation methods are high in correlation degree, namely the consistency of the obtained evaluation results is satisfactory, and the reasonability of the calculation results of the GRA-TOPSIS evaluation method is further explained.
The variation coefficients of the node waterlogging comprehensive evaluation values obtained by the 3 methods of GRA-TOPSIS, GRA and TOPSIS are 0.628, 0.276 and 0.428 respectively. The larger the variation coefficient is, the higher the resolution level and the dispersion degree of the comprehensive evaluation value are, and the method has stronger suitability for distinguishing different node waterlogging risk levels. Obviously, the variation coefficients of the comprehensive values obtained by the GRA-TOPSIS method are much larger than those of the GRA method and the TOPSIS method, and the obtained comprehensive values are more uniform and reasonable in distribution compared with those of the other 2 methods, and the comprehensive value difference between adjacent sequences is more obvious, so that the waterlogging condition of each node can be more favorably and visually distinguished. Therefore, site selection was performed according to the evaluation result of GRA-TOPSIS method.
Figure BDA0003366731770000131
Figure BDA0003366731770000141
As can be seen from the above table, the locations of the storage tanks are selected from the 5 nodes J21, J19, J20, J18 and J15.
Similarly, the results of the individual node waterlogging evaluation under the condition of 10a are shown in the following table, and the positions of the storage regulation pool are selected from 5 nodes of J21, J19, J20, J17 and J18.
Figure BDA0003366731770000142
Figure BDA0003366731770000151
After the position of the decentralized storage tank is determined, the volume of the storage tank is determined. The volumes of the node dispersed storage and the tail end storage are respectively approximated as the volumes of the storage tanks according to the volumes of the nodes and the parts of all nodes with the water accumulation depth exceeding 15 cm.
Figure BDA0003366731770000152
In the formula: vRegulating storage poolFor regulating the volume of the reservoir, m3(ii) a h is the depth of accumulated water, cm; w is catchment width m; i is the ground slope,%;
the volumes of parts with water depth of more than 15cm at joints J21, J19, J20, J18 and J15 in 5a are 9363m respectively3、6727m3、2037m3、62m3And 16m3Therefore, the storage tank capacity of each node is set to 9400m3、6800m3、2100m3、100m3、100m318500m in total3The volume of the traditional tail end storage tank is set to 22000m3. The volume of the part with the water depth of J21, J19, J20, J17 and J18 nodes exceeding 15cm in 10a is 20512m3、16071m3、6775m3、346m3And 1629m3Therefore, the volume of each node is set to 20600m3、16100m3、6800m3、400m3And 1700m345600m in total3The volume of the conventional terminal reservoir was set to 66400m3. Decentralized regulation the volume of the 5a and 10a first-encounter regulation tanks was reduced by approximately 15.91% and 31.31%, respectively, compared to conventional terminal regulation. The treatment effects of the two storage regulation modes on the waterlogging are compared as shown in the following table.
Figure BDA0003366731770000153
Figure BDA0003366731770000161
As can be seen from the above table, compared with the traditional tail end regulation, in the regeneration period of 5a, when the number of nodes with the accumulated water depth exceeding 15cm is reduced from 10 to 6, the reduction rate reaches 40%, the maximum accumulated water depth is reduced from 18.5cm to 16.30cm, and the reduction rate is 11.89%. When the reproduction period is 10a, the number of nodes with the accumulated water depth exceeding 15cm is reduced from 20 to 19, the reduction rate is 5 percent, the maximum accumulated water depth is reduced from 20.09cm to 18.83cm, and the reduction rate is 6.27 percent. Therefore, under two different conditions of 5a and 10a, the effects of dispersing, regulating and storing and improving urban waterlogging are obvious, and the drainage capacity of a drainage system is greatly improved.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.

Claims (5)

1. A method for optimizing site selection of a rainwater storage tank is characterized by comprising the following steps: based on an SWMM model and an urban inland inundation prevention standard, a rainwater system node is taken as an evaluation object, the position of a regulation pool is taken as a target, the ponding depth, the ponding area, the ponding time, the overload time and the overload height of the node are taken as evaluation indexes, the subjective weight obtained by each index analytic hierarchy process and the objective weight obtained by an improved entropy value process are combined and weighted by applying a game theory idea, the relative closeness between each node index and a positive and negative ideal scheme is obtained by utilizing grey correlation analysis-approximate ideal solution sorting, inland inundation risk sorting of the node is determined according to the relative closeness, and the regulation pool is arranged at the node with a large inland inundation risk; the method comprises the following specific steps:
step 1, constructing an index system;
step 2, establishing an SWMM model, inputting rainfall data serving as driving data into the SWMM model to obtain index data in all node index systems, and preprocessing the index data;
step 3, weighting each index by utilizing an analytic hierarchy process and an improved entropy method, and combining the indexes and weighting by utilizing a game theory;
and 4, carrying out waterlogging risk assessment by combining gray correlation analysis and approximate ideal solution sorting.
2. The method of claim 1, wherein step 1 comprises the steps of:
selecting factors directly related to waterlogging when establishing an evaluation index system: node water accumulation and node overload; the node water accumulation indexes comprise water accumulation depth, water accumulation time and water accumulation area, and the node overload indexes comprise overload time and overload height.
3. The method of claim 1, wherein the step 2 comprises the steps of:
step 201, design parameters of the rainwater system include: a rainstorm intensity formula, a design recurrence period, a runoff coefficient, ground water collection time and a reduction coefficient; determining rain system attribute data from hydraulic calculations of the rain system, the rain system attribute data comprising: building an SWMM model according to rainwater system attribute data by using the pipe diameter, gradient, pipe inner bottom elevation and pipe buried depth of each designed pipe section; inputting the designed rainstorm in the corresponding reappearance period specified by the urban inland inundation prevention and control standard as a rainfall condition into the established SWMM model for rainwater system simulation;
step 202, according to the existing urban waterlogging prevention and treatment planning standard, when the depth of ponding exceeds 15cm, urban traffic is inconvenient, firstly, nodes with the depth of ponding exceeding 15cm in a research area are screened out, and evaluation and site selection are carried out on the nodes; assuming that m nodes with water depth exceeding 15cm exist in a research area, each node has n evaluation indexes, and establishing a decision matrix A (x)ij)m×n，xijData for inode j indices;
step 203, adopting an extreme value processing method as a pretreatment method; the specific expression is
Figure FDA0003366731760000021
From the above formula, normalized matrix B ═ y can be obtainedij)m×n，yijNormalized data for inode j index.
4. The method of claim 1, wherein said step 3 comprises the steps of:
301, weighting the analytic hierarchy process;
the specific operation steps are as follows:
1) establishing a hierarchical structure; dividing the decision-making related factors into a plurality of layers according to the attributes;
2) constructing a judgment matrix step by step; constructing a judgment matrix T ═ a (a) by a 1-9 scaling methodij)t×tWherein a isijIs an index T in the criterion layeriRelative to TjThe specific decision scale quantization rule is shown in the following table:
Figure FDA0003366731760000022
3) calculating the weight of each index; calculating each index weight W of criterion layer as (omega)1,ω2…ωt) Wherein:
Figure FDA0003366731760000023
in the formula aijIs an index T in the criterion layeriRelative to TjThe degree of importance of;
4) checking the consistency; solving the maximum eigenvalue lambda of the decision matrix TmaxCalculating a CR value, and if CR is less than 0.1, determining that the judgment matrix meets the requirement of consistency; determining the eigenvector omega corresponding to the maximum eigenvalue as the weight vector of the index of the layer relative to the target layer of the previous layer; the formula for calculating the CR value is:
Figure FDA0003366731760000031
in the formula, t is the index number in the standard layer; RI is an average random consistency index;
the specific value rules of RI are shown in the following table:
Figure FDA0003366731760000032
step 302, improving entropy method weighting;
the improved entropy method is an objective weighting method for determining weight according to variation information entropy of indexes, and comprises the following steps:
1) y is obtainedijCharacteristic specific gravity p ofij(ii) a To avoid pijIs zero by pair yij0.1 is uniformly added to improve the entropy method, so that the entropy method has wider adaptability and scientificity;
Figure FDA0003366731760000033
2) calculating the information entropy e of j indexj；
Figure FDA0003366731760000034
3) Weighting w of j indexj；
Figure FDA0003366731760000035
Step 303, combining and weighting game theory;
the method utilizes the advantages of game theory comprehensive subjective and objective weighting to carry out combined weighting on the indexes, and comprises the following steps:
1) respectively weighting the indexes by using L different weighting methods to construct a basic weight vector set;
let uk＝{uk1,uk2,…,ukn(K ═ 1,2, …, L) noting that any linear combination of these L different vectors is
Figure FDA0003366731760000036
In the formula: u is one possible weight vector of the weight set; alpha is alphakIs a linear combination coefficient;
2) applying game theoryWant to optimize L linear combination coefficients alphakSo that u is associated with each uknWith minimal dispersion, i.e.
Figure FDA0003366731760000041
Optimizing the first derivative condition may be switched to
Figure FDA0003366731760000042
3) Obtaining (alpha)1,α2,…,αL) Followed by a normalization process, i.e.
Figure FDA0003366731760000043
The most satisfactory comprehensive weight vector is obtained:
Figure FDA0003366731760000044
5. the method of claim 1, wherein said step 4 comprises the steps of:
step 401, solving a weighting specification matrix C; each column of the matrix B is associated with a game theory weight corresponding to the column
Figure FDA0003366731760000045
Multiplied by C ═ Zij)m×n，ZijFor normalizing data yijAnd
Figure FDA0003366731760000046
product of (i)
Figure FDA0003366731760000047
Step 402, determining a positive ideal solution set
Figure FDA0003366731760000048
Screening out the optimal values of each index of the matrix C to form a positive ideal solution set, and using the positive ideal solution set as a reference number sequence in the GRA model, namely
Figure FDA0003366731760000049
In the formula: z is a radical ofj(j) Z for each evaluation nodeijA value;
step 403, solving the grey correlation coefficient r between each evaluation node and the positive ideal solution seti(j)；
Figure FDA00033667317600000410
In the formula: λ is a resolution coefficient;
the gray correlation coefficient matrix obtained by the above formula is R ═ (R)i(j))m×n；
Step 404, determine the positive ideal solution set of the matrix R
Figure FDA0003366731760000051
Negative ideal solution set
Figure FDA0003366731760000052
Figure FDA0003366731760000053
Step 405, calculate inodes to
Figure FDA0003366731760000054
Euclidean distance of
Figure FDA0003366731760000055
Figure FDA0003366731760000056
Step 406, solving the gray relative closeness T of each evaluation node;
Figure FDA0003366731760000057
and T determines the waterlogging risk ranking of each node, and if the T is larger, the waterlogging risk is larger, and a regulation and storage pool should be arranged at the point at first.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114611780A (en) * 2022-03-05 2022-06-10 郑州大学 Method for calculating optimal solution for location selection of emergency refuge for levee breaking and path planning

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114611780A (en) * 2022-03-05 2022-06-10 郑州大学 Method for calculating optimal solution for location selection of emergency refuge for levee breaking and path planning

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