CN116362551A - Method for evaluating risk level of flood disaster - Google Patents

Method for evaluating risk level of flood disaster Download PDF

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CN116362551A
CN116362551A CN202310631850.0A CN202310631850A CN116362551A CN 116362551 A CN116362551 A CN 116362551A CN 202310631850 A CN202310631850 A CN 202310631850A CN 116362551 A CN116362551 A CN 116362551A
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李德龙
许小华
黄萍
吴晓彬
程先云
谢信东
江顺淅
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Jiangxi Academy Of Water Resources Jiangxi Dam Safety Management Center Jiangxi Water Resources Management Center
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Abstract

The invention discloses a method for evaluating flood disaster risk level, which comprises the following steps: collecting data related to flood disaster risk assessment; constructing a projection pursuit model for flood disaster risk assessment; introducing a mixed frog leaping algorithm, improving the mixed frog leaping algorithm by using an inverse transformation method and improving the mixed frog leaping algorithm by using a variable factor; optimizing the projection pursuit model by utilizing the improved mixed frog leaping algorithm; and (3) defining a grade standard of the flood disaster risk, evaluating the flood disaster risk degree, and verifying the reasonability of the evaluation by using the historical actual measurement data. The invention has the advantages that: the initial value of the hybrid frog-leaping algorithm is more uniform by using the inverse transformation method, the local updating mechanism of the algorithm is improved by using the variable factors, the optimizing capability of the algorithm is enhanced, and a new and feasible method is provided for quickly and accurately evaluating the flood disaster risk problem.

Description

Method for evaluating risk level of flood disaster
Technical Field
The invention relates to the technical field of flood control and disaster reduction, in particular to a method for evaluating flood disaster risk level.
Background
Currently, the occurrence frequency, intensity and damage degree of flood disasters are increasingly greater. The scientific and reasonable division of flood disaster risk level is an important content for flood risk assessment and management, and can provide basis for flood early warning and disaster prevention and reduction work.
The conventional methods for evaluating the risk level of the flood disaster comprise a hierarchical analysis method, a principal component analysis method, a fuzzy comprehensive evaluation method and a gray system evaluation method, wherein the methods can consider the mutual influence among related indexes of the risk of the flood disaster, avoid the unilateral performance of single index evaluation, but usually adopt experience and expert scoring to determine the weight value among the indexes, have strong subjectivity, have inaccurate evaluation results on the risk level of the flood disaster, and are difficult to truly reflect the specific influence degree of each risk index in the flood disaster. The projection pursuit model is a data analysis method directly driven by sample numbers, and the principle is that the high-dimensional data is projected to a low-dimensional subspace first, and then the optimal projection vector reflecting the high-dimensional data structure is searched. In the process of processing the actual problem, objective evaluation can be performed according to the characteristics of the sample data, and the weight of each risk index can be definitely and specifically determined, so that a new way is provided for solving the multi-factor complex evaluation problem.
Whether the projection pursuit method is successful or not is critical to how to reasonably construct and optimize the projection index function. The conventional optimization method requires a large amount of calculation, so that the application of the method is limited to a certain extent. Therefore, domestic scholars also optimize the intelligent optimization algorithm by using various modern heuristic intelligent optimization algorithms, and the current common methods of immune algorithm, real code acceleration genetic algorithm, particle swarm algorithm, chicken swarm algorithm and the like obtain a certain effect. However, when the algorithm processes complex multidimensional models, the algorithm often has the defects of low calculation efficiency, low global optimizing capability and the like, so improvement is necessary to be provided for the algorithm deficiency, and an optimizing method with stronger applicability and optimizing capability is developed. The invention utilizes an inverse transformation method and a variable factor to respectively improve the initial value and the local updating mechanism of the algorithm on the basis of the optimizing capability of the hybrid frog-leaping algorithm, fully absorbs the superiority of the value uniformity and the variable factor of the inverse transformation method, improves the iterative speed of the algorithm, enhances the global optimizing capability, greatly improves the searching efficiency of the algorithm and provides a new method for solving the real and accurate assessment problem of flood disaster risk level. At present, no document is available for improving the inverse transformation method and the transformation factor simultaneously by using the hybrid frog-leaping algorithm, and introducing the hybrid frog-leaping algorithm into flood disaster risk level assessment.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides a method for evaluating the risk level of a flood disaster.
In order to solve the technical problems, the invention adopts the following technical scheme: a method for assessing the risk level of a flood disaster, comprising the following specific steps:
step 1, collecting data related to flood disaster risk assessment;
step 2, constructing a projection pursuit model for flood disaster risk assessment;
step 3, introducing a mixed frog leaping algorithm, improving the mixed frog leaping algorithm by using an inverse transformation method and improving the mixed frog leaping algorithm by using a variable factor;
step 4, optimizing the projection pursuit model by utilizing the improved mixed frog-leaping algorithm to obtain the optimal projection direction and the comprehensive projection value of the projection pursuit model: the optimal projection direction represents the weight value of each index, and the comprehensive projection value represents a one-dimensional comprehensive value obtained by distributing the multidimensional index according to the weight;
and 5, defining a grade standard of the risk of the flood disaster, evaluating the degree of the risk of the flood disaster, and verifying the reasonability of the evaluation by using the historical actual measurement data.
Further, in the step 1, data related to flood disaster risk assessment is collected; the method comprises the following steps:
taking counties as units, collecting 4 kinds of data of disaster-causing factor dangers, disaster-tolerant environment vulnerability, disaster-bearing body exposition and flood control and drainage capacity of each county;
collecting disaster factor dangerous data, namely collecting average rainfall in each county for a plurality of years, and collecting a storm extremum of 1h/6h/24 h;
collecting vulnerability data of a disaster-tolerant environment, namely collecting the number of small river flows, the number of small reservoirs, the number of pond dams and the number of mountain floods in each county;
collecting disaster-bearing body exposure data, namely collecting flood-affected population numbers in each county and 1h/3h early warning indexes;
collecting flood control and drainage capacity data refers to collecting flood control capacity and drainage capacity of each county.
Further, constructing a projection pursuit model for flood disaster risk assessment in the step 2; the specific operation steps are as follows:
step 21, processing the collected data according to a normalization formula, wherein the single index normalization formula is as follows:
Figure SMS_1
wherein X is a normalized value of a single index; x is a single index original value; x is x max An upper limit for the original value of the single index; x is x min A lower limit for a single index original value;
step 22, the normalized values of the data materials are integrated into a one-dimensional projection value, and the projection formula is as follows:
Figure SMS_2
in the method, in the process of the invention,
Figure SMS_3
one-dimensional projection value of the ith county; />
Figure SMS_4
The projection direction vector corresponding to the j index; />
Figure SMS_5
Values normalized for the j index of the i-th county; i=1, 2, …, m; j=1, 2, …, n; m is the number of counties; n is the number of indexes;
step 23, calculating standard deviation and local density of the one-dimensional projection value, wherein the calculation formula is as follows:
Figure SMS_6
in the above, B z For one-dimensional projection value of the ith county
Figure SMS_9
Standard deviation of (2); />
Figure SMS_11
One-dimensional projection value +.>
Figure SMS_13
Average value of (2); />
Figure SMS_8
One-dimensional projection value +.>
Figure SMS_10
Is a local density of (2); r is the window radius of the local density, which typically needs to be determined empirically; />
Figure SMS_12
For the one-dimensional projection value of the ith county and the ith 1 Distances between one-dimensional projection values of county, i and i 1 Is not equal in value; />
Figure SMS_14
Is the ith 1 One-dimensional projection values of counties; />
Figure SMS_7
Is a unit step functionA number; i.e 1 =1, 2, …, m; m is the number of counties;
step 24, constructing a projection index function, wherein the formula is as follows:
Figure SMS_15
wherein H is a projection index function;
step 25, constructing an objective function and constraint conditions of the projection pursuit model, wherein the formula is as follows:
Figure SMS_16
wherein H is max The maximum value of the projection index function is the objective function of the projection pursuit model;
Figure SMS_17
the square of the projection direction vector corresponding to the j-th index is represented.
Further, in the step 3, an inverse transformation method and a variable factor are utilized to improve the hybrid frog-leaping algorithm; the method comprises the steps of carrying out initial value taking on parameters of a projection pursuit model by using an inverse transformation method, and replacing random value taking on the parameters in an initial stage in an original algorithm; the specific operation steps are as follows:
step 31, calculating to obtain a distribution function Y (Y) according to calculation formulas (10), (11) and (12) in the upper and lower limit ranges of the parameter values of the projection pursuit model;
Figure SMS_18
wherein Y (Y) is a parameter Y distribution function of the projection tracking model, and Y represents a parameter value of the projection tracking model; y is min A lower parameter limit for the projection pursuit model; y is max The upper parameter limit of the projection pursuit model is set;
step 32, inverting the distribution function Y (Y), namely obtaining a sampling formula as follows:
Figure SMS_19
in the method, in the process of the invention,
Figure SMS_20
representing the inverse of the distribution function Y (Y), u represents a random number uniformly distributed in the range between 0 and 1.
Further, in the step 3, the mixed frog-leaping algorithm is improved by using a variable factor; the improvement is two improvements, the first improvement is to introduce a variable factor to improve a local updating mechanism in a subgroup in the mixed frog-leaping algorithm; the second improvement is to introduce a variable factor to improve the updating mode of the worst frog in the subgroup in the mixed frog-leaping algorithm.
Further, the calculation formula of the local update mechanism is as follows:
Figure SMS_21
where S represents the step size in the local update mechanism in which frog position is allowed to be changed, S max Representing the maximum step size, s.ltoreq.S, of the frog position allowed to be changed max C is a variable factor, and c is more than or equal to 1 and less than or equal to 5;
Figure SMS_22
representing random number, 0.ltoreq.o->
Figure SMS_23
≤1；
Figure SMS_24
Representing the optimal frog in the subgroup, pw representing the worst frog in the subgroup,/-for the subgroup>
Figure SMS_25
Representing updated frog in the variant factor pair subgroup, P min ≤/>
Figure SMS_26
≤P max ，P min ≤pw≤P max ，P max 、P min Respectively represent the upper and lower limits of frog values in all subgroups.
Further, introducing a variable factor to improve the updating mode of the worst frog in the subgroup in the mixed frog-leaping algorithm; the specific operation method comprises the following steps:
if the worst frog pw in the subgroup is not improved, introducing a variable factor to improve random updating in the vicinity of the worst frog in the subgroup, and further generating a new frog to directly replace the worst frog pw in the original subgroup until the local searching times reach the maximum upper limit; the calculation formula of the improvement of the variable factor on the random update is as follows:
Figure SMS_27
wherein s is 1 Step size s representing frog positions in subgroup which are allowed to be changed 1 ≤S max The method comprises the steps of carrying out a first treatment on the surface of the c is a variable factor, and c is more than or equal to 1 and less than or equal to 5; p (P) min ≤
Figure SMS_28
≤P max ，/>
Figure SMS_29
Representing frogs produced within the population after the update with the variational factor improvement.
Further, in the step 4, the projection tracking model is optimized by utilizing an improved mixed frog-leaping algorithm; the specific operation steps are as follows:
step 41, uniformly taking values of the mixed frog-leaping algorithm within the upper and lower limit ranges of the parameters of the projection pursuit model by using an inverse transformation method; the parameters after uniform value taking are called into a projection pursuit model in a calling mode, and a group of projection directions, one-dimensional projection values and objective function values which are at the beginning are obtained;
step 42, returning the obtained set of projection directions, one-dimensional projection values and objective function values to the mixed frog-leaping algorithm, and obtaining the projection directions, one-dimensional projection values and objective function values after one-time iterative optimization in the mixed frog-leaping algorithm through local updating and optimization iteration of the mixed frog-leaping algorithm;
step 43, after the second iteration, recording the result obtained in the second iteration, comparing and analyzing with the result obtained in the first iteration, and recording a better result; and repeating the iteration loop until the set maximum iteration optimization times are met, stopping iteration, and recording an optimal result to obtain an optimal set of projection directions, one-dimensional projection values and objective function values.
Further, in step 5, a level standard of flood disaster risk is defined, the degree of flood disaster risk is estimated, and the estimated rationality is verified by using the history actual measurement data, and the specific operation steps are as follows:
step 51, using the average value of the one-dimensional projection values of the ith county in the optimized result
Figure SMS_30
Standard deviation B from one-dimensional projection value of ith county z As a hierarchical condition for dividing flood disaster risk, +.>
Figure SMS_31
Is a region of low risk of risk,
Figure SMS_32
is a risk area for wind>
Figure SMS_33
Is a high risk area->
Figure SMS_34
Is a very high risk area;
and 52, selecting the year of the large flood which has occurred in the history, checking the degree of the flood disaster suffered by the relevant area, and comparing with the four risk areas obtained in the step 51 to verify the rationality of the classified grade standard.
The beneficial effects of the invention are as follows:
(1) The invention fully absorbs the advantages of average value and quick value of the inverse transformation method on the basis of keeping the global searching capability of the standard mixed frog-leaping algorithm, reduces the randomness of the initial solution and improves the searching efficiency of the algorithm in the initial stage;
(2) According to the invention, on the basis of maintaining a standard mixed frog-leaping algorithm frame, a variable factor is introduced, so that a local updating mechanism in the mixed frog-leaping algorithm and an updating mode of worst frog in a subgroup are improved, and the global optimizing capability and updating efficiency of the mixed frog-leaping algorithm are improved;
(3) Compared with the conventional flood disaster risk assessment method, the method can objectively evaluate the flood disaster risk factors, overcomes subjectivity of other methods, realizes rapid and automatic determination of the flood disaster risk factor weight by using an improved artificial intelligence optimization method, and provides a new effective method for flood disaster risk assessment research.
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FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a flow chart of a hybrid frog-leaping method utilizing an inverse transformation and a variable factor.
Detailed Description
The present invention is described in detail below with reference to examples, but the present invention is not limited to these examples.
As shown in fig. 1, a method for evaluating risk level of flood disasters comprises the following specific steps:
step 1, collecting data related to flood disaster risk assessment;
step 2, constructing a projection pursuit model for flood disaster risk assessment;
step 3, introducing a mixed frog-leaping algorithm, improving the mixed frog-leaping algorithm by using an inverse transformation method and improving the mixed frog-leaping algorithm by using a variable factor (see figure 2);
step 4, optimizing the projection pursuit model by utilizing the improved mixed frog-leaping algorithm to obtain the optimal projection direction and the comprehensive projection value of the projection pursuit model: the optimal projection direction represents the weight value of each index, and the comprehensive projection value represents a one-dimensional comprehensive value obtained by distributing the multidimensional index according to the weight;
and 5, defining a grade standard of the risk of the flood disaster, evaluating the degree of the risk of the flood disaster, and verifying the reasonability of the evaluation by using the historical actual measurement data.
Further, the step 1 collects data related to flood disaster risk assessment, specifically:
taking a county as a unit, collecting 4 kinds of 13 index data such as disaster-causing factor dangers, disaster-tolerant environment vulnerabilities, disaster-bearing body exposition, flood control and drainage capacity of 100 counties in a certain province; the disaster-causing factor dangerous data are collected to obtain average rainfall, 1h/6h/24h storm extreme value of years in each county; collecting the number of medium and small river flows, the number of small reservoirs, the number of pond dams and the number of mountain floods in each county according to the vulnerability data of the disaster-tolerant environment; collecting flood-affected population numbers and 1h/3h early warning indexes of each county according to the exposure data of the disaster-bearing body; flood control capacity data are collected for flood control capacity and flood drainage capacity grades in each county.
Further, in the step 2, a projection pursuit model for flood disaster risk assessment is constructed, and the specific operation steps are as follows:
step 21, processing the collected data according to a normalization formula, wherein the single index normalization formula is as follows:
Figure SMS_35
wherein X is a normalized value of a single index; x is a single index original value; x is x max An upper limit for the original value of the single index; x is x min A lower limit for a single index original value;
step 22, the normalized values of the data materials are integrated into a one-dimensional projection value, and the projection formula is as follows:
Figure SMS_36
in the method, in the process of the invention,
Figure SMS_37
one-dimensional projection value of the ith county; />
Figure SMS_38
The projection direction vector corresponding to the j index; />
Figure SMS_39
Values normalized for the j index of the i-th county; i=1, 2, …,100; j=1, 2, …,13;
step 23, calculating standard deviation and local density of the one-dimensional projection value, wherein the calculation formula is as follows:
Figure SMS_40
in the above, B z For one-dimensional projection value of the ith county
Figure SMS_42
Standard deviation of (2); />
Figure SMS_44
One-dimensional projection value +.>
Figure SMS_46
Average value of (2); />
Figure SMS_43
One-dimensional projection value +.>
Figure SMS_45
Is a local density of (2); r is the window radius of the local density, which typically needs to be determined empirically; />
Figure SMS_47
For the one-dimensional projection value of the ith county and the ith 1 Distances between one-dimensional projection values of county, i and i 1 Is not equal in value; />
Figure SMS_48
Is the ith 1 One-dimensional projection values of counties; />
Figure SMS_41
Is a unit step function; i.e 1 =1, 2, …,100;100 is the number of counties;
step 24, constructing a projection index function, wherein the formula is as follows:
Figure SMS_49
wherein H is a projection index function;
step 25, constructing an objective function and constraint conditions of the projection pursuit model, wherein the formula is as follows:
Figure SMS_50
wherein H is max The maximum value of the projection index function is the objective function of the projection pursuit model;
Figure SMS_51
the square of the projection direction vector corresponding to the j-th index is represented.
Further, the step 3 is characterized in that an inverse transformation method is utilized to improve the hybrid frog-leaping algorithm; the method comprises the steps of carrying out initial value taking on projection pursuit model parameters by using an inverse transformation method, and replacing random value taking on the parameters in an initial stage in an original algorithm; the specific operation steps are as follows:
step 31, calculating to obtain a distribution function Y (Y) according to calculation formulas (10), (11) and (12) in the upper and lower limit ranges of the parameter values of the projection pursuit model;
Figure SMS_52
wherein Y (Y) is a parameter Y distribution function of the projection tracking model, and Y represents a parameter value of the projection tracking model; y is min A lower parameter limit for the projection pursuit model; y is max The upper parameter limit of the projection pursuit model is set;
step 32, inverting the distribution function Y (Y), namely obtaining a sampling formula as follows:
Figure SMS_53
in the method, in the process of the invention,
Figure SMS_54
representing the inverse of the distribution function Y (Y), u represents a random number uniformly distributed in the range between 0 and 1.
Furthermore, the algorithm is improved by using the variable factors in the step 3, and the method is mainly used for improvement in two places; the 1 st improvement is to introduce a variable factor to improve a local updating mechanism in a subgroup in the algorithm; the calculation formula of the local update mechanism is as follows:
Figure SMS_55
where S represents the step size in the local update mechanism in which frog position is allowed to be changed, S max Representing the maximum step size, s.ltoreq.S, of the frog position allowed to be changed max C is a variable factor, and c is more than or equal to 1 and less than or equal to 5;
Figure SMS_56
representing random number, 0.ltoreq.o->
Figure SMS_57
≤1；
Figure SMS_58
Representing the optimal frog in the subgroup, pw representing the worst frog in the subgroup,/-for the subgroup>
Figure SMS_59
Representing updated frog in the variant factor pair subgroup, P min ≤/>
Figure SMS_60
≤P max ，P min ≤pw≤P max ，P max 、P min Respectively representing the upper limit and the lower limit of frog values in all subgroups; this example takes c=2.
Furthermore, the algorithm is improved by using the variable factor in the step 3, which is mainly used for improvement in two places; the 2 nd improvement is to introduce a variable factor to improve the updating mode of the worst frog in the sub-group in the algorithm; the specific operation method comprises the following steps:
if the worst frog in the subgroup is not improved, introducing a variable factor to improve random updating in the vicinity of the worst frog in the subgroup, and further generating a new frog to directly replace the worst frog pw in the original subgroup until the local searching times reach the maximum upper limit; the calculation formula of the improvement of the variable factor on the random update is as follows:
Figure SMS_61
wherein s is 1 Step size s representing frog positions in subgroup which are allowed to be changed 1 ≤100；
Figure SMS_62
≤/>
Figure SMS_63
≤/>
Figure SMS_64
，
Figure SMS_65
、/>
Figure SMS_66
Respectively represent the upper limit and the lower limit of frog values in all subgroups, < >>
Figure SMS_67
Representing frogs generated within the population after the update with the variational factor improvement; c is a variable factor, and this embodiment takes c=2.
Further, the projection pursuit model is optimized by utilizing the improved mixed frog-leaping algorithm, and the specific operation steps are as follows:
step 41, uniformly taking values of the mixed frog-leaping algorithm within the upper and lower limit ranges of the parameters of the projection pursuit model by using an inverse transformation method; the parameters after uniform value taking are called into a projection pursuit model in a calling mode, and a group of projection directions, one-dimensional projection values and objective function values at the beginning can be obtained;
step 42, returning the obtained set of projection directions, one-dimensional projection values and objective function values to the mixed frog-leaping algorithm, and carrying out partial updating and optimization iteration of the algorithm to obtain the projection directions, one-dimensional projection values and objective function values after one-time iteration optimization in the mixed frog-leaping algorithm;
step 43, after the second iteration, recording the result obtained in the second iteration, comparing and analyzing with the result obtained in the first iteration, and recording a better result; and repeating the iteration loop until the set maximum iteration optimization times are met, stopping iteration, and recording an optimal result, namely an optimal group of projection directions, corresponding one-dimensional projection values and objective function values.
Further, a grade standard of flood disaster risk is defined, the degree of flood disaster risk is estimated, and the estimated rationality is verified by using historical actual measurement data, and the specific operation steps are as follows:
step 51, using the average value of the one-dimensional projection values of the ith county in the optimized result
Figure SMS_68
Standard deviation B from one-dimensional projection value of ith county z As a hierarchical condition for dividing flood disaster risk, +.>
Figure SMS_69
Is a region of low risk of risk,
Figure SMS_70
is a risk area for wind>
Figure SMS_71
Is a high risk area->
Figure SMS_72
Is a very high risk area;
step 52, multiplying 13 one-dimensional projection directions of a single county in the optimized result with 13 corresponding indexes respectively, then accumulating and summing up multiplied values of the single indexes of the single county to obtain a total index value of the single county, wherein the total index value is a value for evaluating the flood risk degree of the county, and comparing the value with the flood disaster risk level condition of step 51 to divide the county into which risk area;
step 53, according to the history, the disaster-affected area of the typical year of a flood disaster in 12 years, such as 1954, 1962, 1998, 2002, 2005, 2006, 2010, 2015, 2016, 2018, 2019, 2020, etc., is analyzed, the degree of the flood disaster suffered by the relevant area is checked, and the degree of the flood disaster is compared with four risk areas obtained by simulation calculation, so that the rationality of the classified grade standard is verified.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the same, but rather, various modifications and variations may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims (9)

1. A method of assessing a risk level of a flood disaster, comprising: the method comprises the following specific steps:
step 1, collecting data related to flood disaster risk assessment;
step 2, constructing a projection pursuit model for flood disaster risk assessment;
step 3, introducing a mixed frog leaping algorithm, improving the mixed frog leaping algorithm by using an inverse transformation method and improving the mixed frog leaping algorithm by using a variable factor;
step 4, optimizing the projection pursuit model by utilizing the improved mixed frog-leaping algorithm to obtain the optimal projection direction and the comprehensive projection value of the projection pursuit model: the optimal projection direction represents the weight value of each index, and the comprehensive projection value represents a one-dimensional comprehensive value obtained by distributing the multidimensional index according to the weight;
and 5, defining a grade standard of the risk of the flood disaster, evaluating the degree of the risk of the flood disaster, and verifying the reasonability of the evaluation by using the historical actual measurement data.
2. A method of assessing a risk level of a flood disaster according to claim 1, wherein:
step 1, collecting data related to flood disaster risk assessment; the method comprises the following steps:
taking counties as units, collecting 4 kinds of data of disaster-causing factor dangers, disaster-tolerant environment vulnerability, disaster-bearing body exposition and flood control and drainage capacity of each county;
collecting disaster factor dangerous data, namely collecting average rainfall in each county for a plurality of years, and collecting a storm extremum of 1h/6h/24 h;
collecting vulnerability data of a disaster-tolerant environment, namely collecting the number of small river flows, the number of small reservoirs, the number of pond dams and the number of mountain floods in each county;
collecting disaster-bearing body exposure data, namely collecting flood-affected population numbers in each county and 1h/3h early warning indexes;
collecting flood control and drainage capacity data refers to collecting flood control capacity and drainage capacity of each county.
3. A method of assessing a risk level of a flood disaster according to claim 2, wherein:
step 2, constructing a projection pursuit model for flood disaster risk assessment; the specific operation steps are as follows:
step 21, processing the collected data according to a normalization formula, wherein the single index normalization formula is as follows:
Figure QLYQS_1
（1）
wherein X is a normalized value of a single index; x is a single index original value; x is x max An upper limit for the original value of the single index; x is x min A lower limit for a single index original value;
step 22, the normalized values of the data materials are integrated into a one-dimensional projection value, and the projection formula is as follows:
Figure QLYQS_2
（2）
in the method, in the process of the invention,
Figure QLYQS_3
for one-dimensional projection value of the ith county；/>
Figure QLYQS_4
The projection direction vector corresponding to the j index; />
Figure QLYQS_5
Values normalized for the j index of the i-th county; i=1, 2, …, m; j=1, 2, …, n; m is the number of counties; n is the number of indexes;
step 23, calculating standard deviation and local density of the one-dimensional projection value, wherein the calculation formula is as follows:
Figure QLYQS_6
（3）
Figure QLYQS_7
（4）
Figure QLYQS_8
（5）
Figure QLYQS_9
（6）
in the above, B z For one-dimensional projection value of the ith county
Figure QLYQS_12
Standard deviation of (2); />
Figure QLYQS_14
One-dimensional projection value +.>
Figure QLYQS_16
Average value of (2); />
Figure QLYQS_11
One-dimensional projection value +.>
Figure QLYQS_13
Is a local density of (2); r is the window radius of the local density, which typically needs to be determined empirically; />
Figure QLYQS_15
For the one-dimensional projection value of the ith county and the ith 1 Distances between one-dimensional projection values of county, i and i 1 Is not equal in value; />
Figure QLYQS_17
Is the ith 1 One-dimensional projection values of counties; />
Figure QLYQS_10
Is a unit step function; i.e 1 =1, 2, …, m; m is the number of counties;
step 24, constructing a projection index function, wherein the formula is as follows:
Figure QLYQS_18
（7）
wherein H is a projection index function;
step 25, constructing an objective function and constraint conditions of the projection pursuit model, wherein the formula is as follows:
Figure QLYQS_19
（8）
Figure QLYQS_20
（9）
wherein H is max The maximum value of the projection index function is the objective function of the projection pursuit model;
Figure QLYQS_21
the square of the projection direction vector corresponding to the j-th index is represented.
4. A method of assessing a risk level of a flood disaster according to claim 3, wherein:
in the step 3, an inverse transformation method and a variable factor are utilized to improve the mixed frog leaping algorithm; the method comprises the steps of carrying out initial value taking on parameters of a projection pursuit model by using an inverse transformation method; the specific operation steps are as follows:
step 31, calculating to obtain a distribution function Y (Y) according to calculation formulas (10), (11) and (12) in the upper and lower limit ranges of the parameter values of the projection pursuit model;
Figure QLYQS_22
（10）
Figure QLYQS_23
（11）
Figure QLYQS_24
（12）
wherein Y (Y) is a parameter Y distribution function of the projection tracking model, and Y represents a parameter value of the projection tracking model; y is min A lower parameter limit for the projection pursuit model; y is max The upper parameter limit of the projection pursuit model is set;
step 32, inverting the distribution function Y (Y), namely obtaining a sampling formula as follows:
Figure QLYQS_25
（13）
in the method, in the process of the invention,
Figure QLYQS_26
representing the inverse of the distribution function Y (Y), u represents a random number uniformly distributed in the range between 0 and 1.
5. The method for evaluating the risk level of a flood disaster according to claim 4, wherein:
in the step 3, a variable factor is utilized to improve the mixed frog leaping algorithm; the improvement is two improvements, the first improvement is to introduce a variable factor to improve a local updating mechanism in a subgroup in the mixed frog-leaping algorithm; the second improvement is to introduce a variable factor to improve the updating mode of the worst frog in the subgroup in the mixed frog-leaping algorithm.
6. The method for evaluating the risk level of a flood disaster according to claim 5, wherein:
the calculation formula of the local update mechanism is as follows:
Figure QLYQS_27
（14）
Figure QLYQS_28
（15）
where S represents the step size in the local update mechanism in which frog position is allowed to be changed, S max Representing the maximum step size, s.ltoreq.S, of the frog position allowed to be changed max C is a variable factor, and c is more than or equal to 1 and less than or equal to 5;
Figure QLYQS_29
representing random number, 0.ltoreq.o->
Figure QLYQS_30
≤1；/>
Figure QLYQS_31
Representing the optimal frog in the subgroup, pw representing the worst frog in the subgroup,/-for the subgroup>
Figure QLYQS_32
Representing updated frog in the variant factor pair subgroup, P min ≤/>
Figure QLYQS_33
≤P max ，P min ≤pw≤P max ，P max 、P min Respectively represent the upper and lower limits of frog values in all subgroups.
7. The method for evaluating the risk level of a flood disaster according to claim 6, wherein:
introducing a variable factor to improve the updating mode of the worst frog in the subgroup in the mixed frog-leaping algorithm; the specific operation method comprises the following steps:
if the worst frog pw in the subgroup is not improved, introducing a variable factor to improve random updating in the vicinity of the worst frog in the subgroup, and further generating a new frog to directly replace the worst frog pw in the original subgroup until the local searching times reach the maximum upper limit; the calculation formula of the improvement of the variable factor on the random update is as follows:
Figure QLYQS_34
（16）
Figure QLYQS_35
（17）
wherein s is 1 Step size s representing frog positions in subgroup which are allowed to be changed 1 ≤S max The method comprises the steps of carrying out a first treatment on the surface of the c is a variable factor, and c is more than or equal to 1 and less than or equal to 5; p (P) min ≤
Figure QLYQS_36
≤P max ，/>
Figure QLYQS_37
Representing frogs produced within the population after the update with the variational factor improvement.
8. The method for evaluating the risk level of a flood disaster according to claim 7, wherein:
in the step 4, the projection pursuit model is optimized by utilizing an improved mixed frog-leaping algorithm; the specific operation steps are as follows:
step 41, uniformly taking values of the mixed frog-leaping algorithm within the upper and lower limit ranges of the parameters of the projection pursuit model by using an inverse transformation method; the parameters after uniform value taking are called into a projection pursuit model in a calling mode, and a group of projection directions, one-dimensional projection values and objective function values which are at the beginning are obtained;
step 42, returning the obtained set of projection directions, one-dimensional projection values and objective function values to the mixed frog-leaping algorithm, and obtaining the projection directions, one-dimensional projection values and objective function values after one-time iterative optimization in the mixed frog-leaping algorithm through local updating and optimization iteration of the mixed frog-leaping algorithm;
step 43, after the second iteration, recording the result obtained in the second iteration, comparing and analyzing with the result obtained in the first iteration, and recording a better result; and repeating the iteration loop until the set maximum iteration optimization times are met, stopping iteration, and recording an optimal result to obtain an optimal set of projection directions, one-dimensional projection values and objective function values.
9. The method of assessing the risk level of a flood disaster according to claim 8, wherein:
in step 5, defining a grade standard of flood disaster risk, evaluating the degree of flood disaster risk, and verifying the evaluated rationality by using historical actual measurement data, wherein the specific operation steps are as follows:
step 51, using the average value of the one-dimensional projection values of the ith county in the optimized result
Figure QLYQS_38
Standard deviation B from one-dimensional projection value of ith county z As a hierarchical condition for dividing the risk of flood disasters, (0, ">
Figure QLYQS_39
-B z ) Is a low risk region, (-)>
Figure QLYQS_40
-B z， />
Figure QLYQS_41
) Is the risk area of stroke, (-)>
Figure QLYQS_42
，/>
Figure QLYQS_43
+B z ) Is a high risk area, (-)>
Figure QLYQS_44
+B z， + -infinity) is a very high risk area;
and 52, selecting the year of the large flood which has occurred in the history, checking the degree of the flood disaster suffered by the relevant area, and comparing with the four risk areas obtained in the step 51 to verify the rationality of the classified grade standard.
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005124695A2 (en) * 2004-06-15 2005-12-29 Daimlerchrysler Ag Methods for generating a spatial representation
CN101673369A (en) * 2009-09-28 2010-03-17 北京工业大学 Projection pursuit-based method for evaluating flooding risk of drainage pipe network
US20180357610A1 (en) * 2017-06-12 2018-12-13 Hefei University Of Technology Method and system for collaborative scheduling of production and transportation based on shuffled frog leaping and path relinking algorithms
CN109858647A (en) * 2018-12-21 2019-06-07 河海大学 A kind of Regional Flooding Hazard Risk Assessment and predictor method coupling GIS and GBDT algorithm
WO2021003768A1 (en) * 2019-07-08 2021-01-14 大连理工大学 Rural grass-roots flood early-warning method based on antecedent precipitation and upstream-downstream topological relationship
CN112287539A (en) * 2020-10-28 2021-01-29 国网湖北省电力有限公司电力科学研究院 Power facility flood disaster risk assessment method considering reservoir influence
CN114741987A (en) * 2022-04-18 2022-07-12 浙江省水利河口研究院(浙江省海洋规划设计研究院) Flood probability forecasting model considering absolute error fitting residual distribution of flood forecasting model

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005124695A2 (en) * 2004-06-15 2005-12-29 Daimlerchrysler Ag Methods for generating a spatial representation
CN101673369A (en) * 2009-09-28 2010-03-17 北京工业大学 Projection pursuit-based method for evaluating flooding risk of drainage pipe network
US20180357610A1 (en) * 2017-06-12 2018-12-13 Hefei University Of Technology Method and system for collaborative scheduling of production and transportation based on shuffled frog leaping and path relinking algorithms
CN109858647A (en) * 2018-12-21 2019-06-07 河海大学 A kind of Regional Flooding Hazard Risk Assessment and predictor method coupling GIS and GBDT algorithm
WO2021003768A1 (en) * 2019-07-08 2021-01-14 大连理工大学 Rural grass-roots flood early-warning method based on antecedent precipitation and upstream-downstream topological relationship
CN112287539A (en) * 2020-10-28 2021-01-29 国网湖北省电力有限公司电力科学研究院 Power facility flood disaster risk assessment method considering reservoir influence
CN114741987A (en) * 2022-04-18 2022-07-12 浙江省水利河口研究院(浙江省海洋规划设计研究院) Flood probability forecasting model considering absolute error fitting residual distribution of flood forecasting model

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
YINGHAI LI等: "《Novel Multiobjective Shuffled Frog Leaping Algorithm with Application to Reservoir Flood Control Operation》", TECHNICALPAPERS *
司存友等: "《洪水预报经验模型的混合蛙跳优化率定》", 水利信息化 *
崔东文;: "鸡群优化算法-投影寻踪洪旱灾害评估模型", 水利水电科技进展, no. 02 *
李德龙;许小华;黄萍;雷声;张秀平;孙瑞刚;: "基于改进智能优化算法的投影寻踪模型在洪水评估中的应用研究", 水利水电技术, no. 12 *

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