A method for optimizing spatial pattern of impervious surface considering unit difference

Abstract

The application relates to the technical field of urban renewal planning, and discloses a method for optimizing the spatial pattern of impervious surfaces by considering unit differences, which comprises the following steps: step one: based on initial CN values, impervious density, slope and rainfall and other data, a runoff coefficient is calculated, and a runoff minimization target is converted into a linear programming problem; step two: according to the interpretation result of the SCS-CN model, a linear programming condition is set; and according to the actual situation, relevant constraint conditions are set; step three: the result is solved. The application considers the differences in drainage capacity among different hydrological units, realizes the spatial balance of surface water retention, and has the advantages that on one hand, the surface runoff in the high water retention area can be reduced, and on the other hand, the drainage capacity of the area with remaining capacity can be fully utilized.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of urban renewal planning, and particularly relates to a method for optimizing the spatial pattern of impervious surfaces by considering unit differences. BACKGROUND

[0002] Research shows that the rapid expansion of impervious surfaces and the low design standard of drainage pipe networks are the main reasons for the difficulty of rainwater infiltration and discharge, and the rainwater is retained on the ground to form waterlogging. However, the reconstruction of the drainage pipe network requires high economic and social costs, and the efficiency is not high. In particular, the current proposal of "urban renewal" provides a possibility for the reconstruction of the spatial pattern of urban land. Therefore, by means of the opportunity of "urban renewal" planning, the optimization of the spatial pattern of impervious surfaces is an important way to alleviate the risk of urban waterlogging.

[0003] The most similar existing implementation scheme to the application: Zhao Yaolong et al. proposed an impervious surface spatial pattern optimization method coupled with an SCS-CN model and a multivariate linear programming algorithm (hereinafter referred to as MLP-SCS) in 2020. The technical process of the method is as follows: first, a linear objective function is constructed based on the SCS-CN model, and the objective function of minimizing the runoff coefficient is converted into the most direct linear programming objective function (the objective function is shown in formula (1)). Then, the constraint conditions of the multivariate linear programming algorithm are set (the constraint conditions are shown in formula (2) and (3)), and the linear programming is solved through cyclic iteration, and finally the optimal impervious surface obtained by solving is substituted into the SCS-CN model for result evaluation.

[0004] Objective function:

[0005]

[0006] Constraint condition:

[0007]

[0008]

[0009] In the formula, n represents the number of grids participating in optimization, p i represents the impervious density of the i-th grid unit, CN i represents the CN value difference of the i-th grid unit, sum imp represents the sum of the original impervious surface density of the research area, and m represents the constraint of the impervious density in the grid unit.

[0010] However, the scheme has the following disadvantages: the spatial difference of the pipe network drainage capacity between different hydrological units is not considered. The existing impervious surface spatial pattern optimization method assumes that the drainage capacity of the underground pipe network is homogeneous in space, and the model is constructed based on this assumption. However, in reality, when there is a large difference in drainage capacity between different units, it will have a great influence on the optimization result of the spatial pattern of the impervious surface. SUMMARY

[0011] (I) Technical problems solved

[0012] In view of the defects of the prior art, the present application provides an impervious surface spatial pattern optimization method considering unit differences, which improves the MLP-SCS model and solves the defect that it does not consider the difference in drainage capacity between different units, further improves the optimization efficiency of the spatial pattern of the impervious surface, and makes the optimization result more suitable for the actual situation that the hydrological conditions differ between units and the impervious surface transfers between units during urban renewal.

[0013] (II) Technical solutions

[0014] In order to achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0015] An impervious surface spatial pattern optimization method considering unit differences, comprising the following steps:

[0016] Step 1: Based on the initial CN value, impervious density, slope and rainfall, etc. Data, calculate the runoff coefficient, and convert the runoff minimization objective into a linear programming problem;

[0017] Step 2: According to the interpretation result of the SCS-CN model, set the linear programming condition;

[0018] On the basis of the optimization results of the above-mentioned SCS-CN and multivariate linear programming algorithm, considering the difference in pipe network drainage capacity between units, the water retention coefficient of each unit is calculated, then the objective function 1 (the minimum difference in water retention between hydrological units) and the objective function 2 (the minimum amount of impervious surface transfer between hydrological units) are extracted, and according to the actual situation, the relevant constraint conditions are set;

[0019] Step 3: Solve the result;

[0020] The minimum difference in water retention and the minimum amount of impervious surface transfer are used as the objective function of genetic algorithm-2, then an initial population is generated, each chromosome in the population represents a transfer allocation scheme of the impervious surface in the unit. And set the parameters such as population size, crossover probability, mutation probability and iteration number;

[0021] If the number of iterations reaches the maximum evolution generation set, terminate the solution.

[0022] Preferably, in the step two, since the runoff coefficient is "the ratio of surface runoff to rainfall"; similarly, the retention water coefficient is converted into the same dimension, which is "the ratio of surface retention water to rainfall"; therefore, the calculation formula of the retention water coefficient is set as:

[0023]

[0024] In the formula, Q represents the retention water coefficient, Q r represents the runoff coefficient, S represents the area of the hydrological unit, Q d represents the drainage volume, and P represents the rainfall.

[0025] Preferably, in the step two, the objective function 1 of the retention water balance is set as:

[0026]

[0027] In the formula, Q j represents the retention water coefficient of the jth hydrological unit, Q a represents the retention water coefficient of the entire study area, S j represents the area of the jth hydrological unit, and S represents the total area of the study area.

[0028] Preferably, in the step two, the objective function 2 of the impervious surface transfer amount of the hydrological unit is set as:

[0029]

[0030] In the formula, imp j represents the total impervious surface in the jth unit, t j represents the impervious surface change amount of the jth unit, sum imp represents the total impervious surface of the entire study area.

[0031] Preferably, in the step two, according to the requirements of the urban renewal strategy based on stock optimization, the total area of the impervious surface before and after optimization should be as unchanged as possible, therefore, the constraint condition is set as:

[0032]

[0033] In the formula, sum imp represents the total impervious surface of the entire study area, and imp j represents the total impervious surface of the jth unit. Since it is difficult to completely control the total amount unchanged during the optimization process, a threshold of 1% is set for it to search for solutions that meet the conditions faster.

[0034] Preferably, in the second step, the impermeable surface transfer amount between units should not be too large due to the particularity of the impermeable surface, therefore, the change amount of the impermeable surface of a single hydrological unit should also be limited, therefore, the constraint condition is set as:

[0035] 0.9≤t j ≤1.1

[0036] In the formula, t j represents the change amount of the impermeable surface set for the jth unit. In the present scheme, it is controlled between [0.9, 1.1], that is, the change amount of the impermeable surface in a single unit does not exceed 10%.

[0037] (Three) beneficial effects

[0038] Compared with the prior art, the present application provides a method for optimizing the spatial pattern of the impermeable surface considering the differences between units, which has the following beneficial effects: the present application takes into account the differences in drainage capacity between different hydrological units, and realizes the spatial balance of surface water retention; the advantages are: on the one hand, it can reduce the surface runoff in the area with high water retention, on the other hand, it also fully utilizes the drainage capacity of the area with surplus capacity. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 The technical flowchart of the present application. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application.

[0041] Example one:

[0042] Please refer to Figure 1 , a method for optimizing the spatial pattern of the impermeable surface considering the differences between units, comprising the following steps:

[0043] Step one: based on the initial CN value, impermeable density, slope and rainfall data, calculate the runoff coefficient, and convert the runoff minimization objective into a linear programming problem;

[0044] Step two: set the linear programming condition according to the interpretation result of the SCS-CN model;

[0045] On the basis of the optimization results of the above SCS-CN and multiple linear programming algorithm, considering the differences in pipe network drainage capacity between units, calculate the retention water amount coefficient of each unit, then extract the objective function 1 (the difference in retention water amount between hydrological units is minimum) and the objective function 2 (the impermeable surface transfer amount between hydrological units is minimum), and set the relevant constraint conditions according to the actual situation;

[0046] Step three: solve the result;

[0047] The difference in the amount of water retention and the minimum amount of impermeable surface transfer are taken as the objective function of the genetic algorithm-2, and then an initial population is generated, each chromosome in the population representing an impermeable surface transfer allocation scheme in the unit. The population size, crossover probability, mutation probability, and iteration number are set;

[0048] If the number of iterations reaches the maximum evolution generation, the solution is terminated.

[0049] Preferably, in step two, since the runoff coefficient is "the ratio of surface runoff to rainfall"; similarly, the retention water coefficient is converted to the same dimension, which is "the ratio of surface retention water to rainfall"; therefore, the calculation formula of the retention water coefficient is set as:

[0050]

[0051] In the formula, Q represents the retention water coefficient, Q r represents the runoff coefficient, S represents the area of the hydrological unit, Q d represents the drainage amount, and P represents the rainfall.

[0052] Preferably, in step two, the objective function 1 of the retention water balance is set as:

[0053]

[0054] In the formula, Q j represents the retention water coefficient of the jth hydrological unit, Q a represents the retention water coefficient of the entire study area, S j represents the area of the jth hydrological unit, and S represents the total area of the study area.

[0055] Preferably, in step two, the objective function 2 of controlling the impermeable surface transfer amount of the hydrological unit is set as:

[0056]

[0057] In the formula, imp j represents the total amount of impermeable surface in the jth unit, t j represents the change amount of impermeable surface in the jth unit, sum imp represents the total amount of impermeable surface in the entire study area.

[0058] Preferably, in step two, according to the requirements of the urban renewal strategy based on stock optimization, the total area of the impermeable surface before and after optimization should be as constant as possible, therefore, the constraint condition is set as:

[0059]

[0060] where sum imp represents the total amount of impervious surface in the whole study area, imp j represents the total amount of impervious surface in the jth unit. Since it is difficult to completely control the total amount during the optimization process, a threshold of 1% is set to search for solutions that meet the conditions more quickly.

[0061] In step two, the amount of impervious surface transfer between units should not be too large due to the special nature of impervious surface. Therefore, the amount of change in impervious surface in a single hydrological unit should also be limited. Therefore, the constraint is set as:

[0062] 0.9≤t j ≤1.1

[0063] where t j represents the set amount of change in impervious surface in the jth unit. In this scheme, it is controlled between [0.9, 1.1], i.e., the change in impervious surface in a single unit does not exceed 10%.

[0064] Comparison of the method with MLP-SCS:

[0065] The main urban area of Guangzhou City is taken as an example for analysis and comparison. Since the optimization efficiency of runoff by the MLP-SCS model is closely related to the total amount of change in impervious surface, for each optimal solution in the Pareto solution, the runoff optimization rate and the degree of balance improvement of the water retention coefficient under the same total amount of change in impervious surface are calculated, and the results are shown in Table 1:

[0066] Table 1 Comparison with MLP-SCS model

[0067]

[0068] As can be seen from Table 1, compared with the MLP-SCS model without considering unit differences, as the amount of impervious surface transfer between units increases, the optimization rate of the runoff coefficient is slightly lost, but the balance of the water retention coefficient between units is more improved, and the overall risk of waterlogging is reduced. Because the optimization process no longer aims to minimize the runoff coefficient after considering unit differences, but more considers the synergistic effect between regions, the overall impact of rainfall is considered, therefore, even if there is a small amount of loss in the optimization of runoff, better optimization results can be achieved in the macroscopic.

Claims

1. A method for optimizing the spatial pattern of impervious surface considering the difference between cells, characterized in that: Comprising the following steps: Step one: based on the initial CN value, impervious density, slope and rainfall data, calculate the runoff coefficient, and convert the runoff minimization objective into a linear programming problem; Step two: according to the interpretation result of SCS-CN model, set the linear programming condition; On the basis of the optimization results of the above SCS-CN and multiple linear programming algorithm, considering the difference in pipe network drainage capacity between units, the water retention coefficient of each unit is calculated, then the objective function 1 and the objective function 2 are extracted, and according to the actual situation, the relevant constraint conditions are set; In step two, the objective function 1 of water retention balance is set as: ； where Q j represents the storage coefficient of the jth hydrological unit, Q a represents the storage coefficient of the entire study area, S j represents the area of the jth hydrological unit, S represents the total area of the study area; In step two, the objective function 2 of controlling the impervious surface transfer amount of hydrological unit is set as: ； where imp j represents the total impervious surface within the jth cell, t j represents the change in impervious surface for the jth cell, sum imp represents the total impervious surface for the entire study area; Step three: solve the results; The minimum difference of water retention and the minimum of impervious surface transfer are taken as the objective function of genetic algorithm-2, then the initial population is generated, each chromosome in the population represents a transfer allocation scheme of impervious surface in the unit; And set the population size, crossover probability, mutation probability and iteration number parameters; If the number of iterations reaches the maximum evolution generation set, the solution is terminated.

2. The method according to claim 1, wherein: In step two, since the runoff coefficient is "the ratio of surface runoff to rainfall"; Similarly, convert the water retention coefficient to the same dimension, which is "the ratio of surface water retention to rainfall"; Therefore, the calculation formula of the water retention coefficient is set as: ； In the formula, Q represents a retention water amount coefficient, Q r represents a runoff coefficient, S represents a hydrological unit area, Q d represents a drainage amount, and P represents rainfall amount.

3. The impervious surface spatial pattern optimization method considering unit difference according to claim 2, characterized in that: In step two, according to the requirements of the urban renewal strategy of stock optimization, the total area of impervious surface before and after optimization should be as constant as possible, therefore, the constraint condition is set as: ； where sum imp represents the total amount of impervious surface in the whole study area, imp j represents the total amount of impervious surface in the jth cell, as it is difficult to control the total amount completely in the optimization process, a threshold of 1% is set to search for the solution that meets the conditions faster.

4. The impervious surface spatial pattern optimization method of claim 3, wherein: In step two, due to the particularity of impervious surface, the transfer amount of impervious surface between units should not be too large, therefore, the change amount of impervious surface of single hydrological unit should also be limited, therefore, the constraint condition is set as: ； In the formula, t j represents the change amount of the impermeable surface of the jth unit, which is controlled between [0.9, 1.1] in the present scheme, that is, the change amount of the impermeable surface in a single unit accounts for no more than 10%.

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