A Method for Establishing a Simulation Model of the Risk of Dammed Lake Breaching Considering Human Intervention

Abstract

The present invention discloses a method for establishing a simulation model of the risk of dammed lake breach considering artificial intervention. The method includes establishing a quantitative model of the impact of artificial intervention measures on the peak flood of dammed lake breach and the risk population; establishing a loss function of disaster-bearing bodies under artificial intervention conditions; quantitatively evaluating the impact of artificial measures on the disaster chain of dammed lake breach and predicting risks through the quantitative model and the loss function. The present invention supports the comparison and selection of multi-scenario emergency rescue plans under different artificial intervention intensities, provides more scientific technical support for dammed lake emergency rescue, significantly improves the prediction accuracy of risk losses under artificial intervention conditions, and is applicable to the emergency rescue of various dammed lakes in alpine valleys, with wide engineering applicability.

Description

Technical Field

[0001] The present invention belongs to the technical field of emergency disposal of barrier lakes and disaster risk assessment, and particularly relates to a method for establishing a simulation model for the breach risk of barrier lakes considering artificial intervention. Background Art

[0002] The flood caused by the breach of a barrier lake is characterized by strong suddenness, wide destruction range, and complex disaster chains. Most previous studies on the risk of flood caused by the breach of a barrier lake have focused on static assessments in aspects such as life and property, social economy, and ecological environment safety, often ignoring the dynamic impact of artificial intervention measures such as drainage channels and emergency water project dispatching on the risk of flood caused by the breach of a barrier lake.

[0003] There is an urgent need to construct a dynamic assessment model for the risk of flood caused by the breach of a barrier lake considering artificial intervention measures on the basis of accurately identifying the evolution process of the disaster chain of flood caused by the breach of a barrier lake, and quantitatively evaluate the impact of the implementation of artificial intervention measures on the risk of flood caused by the breach of a barrier lake. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for establishing a simulation model for the breach risk of a barrier lake considering artificial intervention, so as to solve the problem of how to scientifically predict the breach risk of a barrier lake in the presence of artificial intervention such as drainage by a drainage channel, water project dispatching, and emergency transfer.

[0005] To achieve the above purpose, the technical solution of the present invention is as follows:

[0006] A method for establishing a simulation model for the breach risk of a barrier lake considering artificial intervention, the method comprising:

[0007] Establishing a quantitative model for the impact of artificial intervention measures on the peak flood of the barrier lake breach and the risk population;

[0008] Establishing a loss function of the disaster-bearing body under artificial intervention conditions;

[0009] Quantitatively evaluating the impact of artificial measures on the disaster chain of the barrier lake breach and predicting the risk through the quantitative model and the loss function.

[0010] Furthermore, the quantitative model for the impact of the artificial intervention measures on the peak flood of the barrier lake breach and the risk population is as shown in formula (1):

[0011] (1)

[0012] In the formula: Q p is the maximum peak flood of the barrier lake under non-artificial intervention conditions, m 3 / s, by adjusting the excavation depth, width, and slope of the drainage channel, the peak discharge of the breach can be reduced to Q p’ , m 3 / s, I M1 is the intervention intensity of the drainage channel drainage measure, I M2 is the intervention intensity of the water project scheduling, Q p ’’ is the maximum breach flood peak of the barrier lake after taking the artificial intervention measure of water project scheduling, m 3 / s, ρ p is the risk population density under the condition of non-artificial intervention of the barrier lake, person / km 2 , and the risk population can be reduced to ρ p ’ , I M3 is the intervention intensity of the emergency transfer;

[0013] Furthermore, the loss function of the disaster-bearing body under the artificial intervention condition is as shown in Equation (2):

[0014] (2)

[0015] In the formula: L 1 (F), L 2 (F) and L 3 (F) respectively correspond to the loss functions of the three types of disaster-bearing bodies of population, economy, and ecology under the artificial intervention condition, L 01 (F), L 02 (F) and L 03 (F) respectively correspond to the loss functions of the three types of disaster-bearing bodies of population, economy, and ecology under the non-artificial intervention condition, F is the flood intensity, m 3 / s, β n is the loss reduction coefficient corresponding to taking the artificial measure n , γ is the influence coefficient of the artificial intervention measure on the flood intensity, and e is the natural constant.

[0016] Through Q p ’’ , ρ p ’ value and L 1(F), L 2 (F) and L 3 (F) Quantitatively evaluate and predict the risk of the impact of artificial measures on the dam-break disaster chain of barrier lakes through function values.

[0017] Furthermore, the loss reduction coefficient β n and the intensity influence coefficient γ are calculated as follows:

[0018] Construct a loss correction function of the disaster-bearing body under artificial intervention based on historical real-case data, extract the actual intervention intensity and loss data, and optimize using the particle swarm algorithm β n and γ until convergence, denoted as β 优n and γ 优 as shown in (3):

[0019] (3)

[0020] The β 优n and γ 优 are respectively used as the optimal solutions of β n and γ in formula (2).

[0021] A device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the construction method of the risk simulation model.

[0022] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the construction method of the risk simulation model is implemented.

[0023] The technical solution of the present invention has the following technical effects:

[0024] 1. The present invention proposes a quantitative mapping relationship between artificial intervention measures, disaster-causing factors of barrier lakes, and disaster-bearing bodies, realizing the dynamic coupling of "artificial intervention measures - disaster-causing factors - losses of disaster-bearing bodies".

[0025] 2. The present invention proposes a loss function of the disaster-bearing body under artificial intervention and corrects the loss curve based on real historical cases, significantly improving the prediction accuracy of risk losses under artificial intervention conditions.

[0026] 3. The method of the present invention supports the comparison and selection of multi-scenario emergency rescue plans under different intensities of manual intervention, providing more scientific technical support for the emergency rescue of barrier lakes.

[0027] 4. The present invention is applicable to the emergency rescue of various barrier lakes in alpine valleys and has wide engineering applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Flow chart of the method for establishing a barrier lake breach risk simulation model considering manual intervention of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific examples described herein are part of the embodiments of the present invention, rather than all of the embodiments, and are not used to limit the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0030] The present invention is applicable to the quantitative assessment of the impact of artificial measures such as drainage channel drainage, water project scheduling, and emergency transfer on the disaster chain and the dynamic prediction of risks in the emergency disposal of barrier lakes. The method for constructing the simulation model of the present invention includes:

[0031] 1) Establish a quantitative model for the impact of artificial intervention measures on the breach peak flood and risk population of barrier lakes

[0032] Establish a quantitative model for the impact of drainage channel drainage, water project scheduling, and emergency transfer on the breach peak flood and risk population of barrier lakes. As shown in Equation (1).

[0033] (1)

[0034] In the formula: Q p is the maximum breach peak flood under non-artificial intervention conditions of the barrier lake, m 3 / s, Q p The value can be calculated and predicted through existing methods; the main function of the drainage channel drainage measure is to control the maximum breach peak flood. By adjusting parameters such as the excavation depth, width, and slope of the drainage channel, the peak discharge of the breach can be reduced to Q p ’ m 3 / s, I M1 is the intervention intensity of the drainage channel drainage measure, and generally takes values from 0 to 1 according to engineering experience. The main function of water project scheduling is to "create peaks to eliminate risks and control breaches", that is, for the flood peak volumeQ p For smaller barrier lakes, the purpose is to accelerate the erosion of the breach by increasing the upstream water inflow, and for the peak flood volume Q p For larger barrier lakes, the purpose is to control the maximum peak flood volume by reducing the upstream water inflow. I M2 is the intervention intensity of water project scheduling. According to engineering experience, for the peak flood volume Q p For larger barrier lakes, it generally takes values from 0 to 1, and for the peak flood volume Q p For smaller barrier lakes, it generally takes values from 1 to 3. Q p ’’ is the maximum breach peak flood of the barrier lake after taking artificial intervention measures of water project scheduling, m 3 / s. ρ p is the risk population density under the condition of non-artificial intervention of the barrier lake, people / km 2 , and through emergency transfers such as population evacuation, the risk population can be reduced to ρ p ’ , I M3 is the intervention intensity of emergency transfer. According to engineering experience, it generally takes values from 0 to 1.

[0035] In the present invention, the input basic data includes the barrier lake topography, storage capacity curve, downstream river channel DEM, artificial intervention plans such as diversion channel design parameters, water project scheduling measures, and population transfer plans, and the distribution data of disaster-bearing bodies such as population density, economic assets, and ecological sensitive areas. The barrier lake topography, storage capacity curve, and downstream river channel DEM are used to calculate the breach discharge and inundation loss under the condition of non-artificial intervention through existing technologies.

[0036] In the present invention, according to the parameters such as the excavation depth, width, and slope of the proposed diversion channel, the intervention intensity of the diversion channel drainage measure is determined I M1 ; according to the proposed water project scheduling measures, the intervention intensity of water project scheduling is determined I M2 ; according to the proposed personnel evacuation plan, the intervention intensity of emergency transfer is determined I M3 .

[0037] 2) Establish a loss function of disaster-bearing bodies under artificial intervention

[0038] For the three types of disaster-bearing bodies of population, economy, and ecology corresponding to the barrier lake breach risk, a dynamic correction model of the loss curve of disaster-bearing bodies considering artificial intervention conditions is constructed. As shown in Equation (2).

[0039] (2)

[0040] Wherein: L 1 (F), L 2 (F) and L 3 (F) respectively correspond to the loss functions of the three types of disaster-bearing bodies of population, economy, and ecology under artificial intervention conditions, L 01 (F), L 02 (F) and L 03 (F) respectively correspond to the loss functions of the three types of disaster-bearing bodies of population, economy, and ecology under non-artificial intervention conditions, L 01 (F), L 02 (F) and L 03 (F) are calculated and predicted by existing methods. F is the flood intensity, m 3 / s, β n is the loss reduction coefficient corresponding to the adoption of artificial measures n ; γ is the influence coefficient of artificial intervention measures on flood intensity; e is the natural constant.

[0041] Construct a loss correction function of disaster-bearing bodies under artificial intervention conditions based on real historical cases

[0042] Select real historical cases of emergency disposal of barrier lakes such as Baige and Tangjiashan, extract actual intervention intensity and loss data, and optimize using the particle swarm optimization algorithm β n and γ until convergence, denoted as β 优n and γ 优 as shown in (3).

[0043] (3)

[0044] Based on the different influences of artificial intervention measures on the disaster-causing factors and disaster-bearing bodies of barrier lakes, the present invention studies the influence of artificial intervention measures on the barrier lake outburst flood disaster chain through the β 优n and γ 优 are respectively used as in formula (2) βn and γ optimal solutions, obtained through Formulas (1) and (3) Q p ’ , Q p ’’ , ρ p ’ and L 1 (F), L 2 (F) , L 3 (F) values to evaluate the risk of artificial intervention measures for the breach of summer barrier lakes. The present invention takes the magnitude of artificial intervention intensity as a control index, corrects the loss curves of different disaster-bearing body units such as population, economy, and ecological environment under different artificial intervention intensities, and verifies the corrected loss curves of disaster-bearing body units through historical cases of emergency disposal of barrier lakes. Finally, a dynamic simulation model for the risk of barrier lake breach flood considering artificial intervention is proposed to provide technical support for barrier lake emergency rescue.

[0045] Combined with the actual requirements of emergency rescue, the present invention uses Monte Carlo simulation to generate multiple sets of barrier lake breach scenarios under different artificial measure intervention conditions, that is, different intervention intensity indicators, and combines the loss correction function of disaster-bearing bodies under artificial intervention conditions to output the heat maps of population, economic, and ecological losses and risk areas in each scenario, providing technical support for the final decision-making of emergency rescue. Through the above steps, the implementation of a simulation model for the risk of barrier lake breach considering artificial intervention of the present invention is completed.

[0046] The present invention also discloses a device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the construction method of the risk simulation model.

[0047] The present invention also discloses a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the construction method of the risk simulation model is implemented.

[0048] The above embodiments are merely examples given for clear illustration and are not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. And the obvious changes or modifications derived therefrom still fall within the protection scope of the present invention.

Claims

1. A method for establishing a simulation model of the risk of dam-break of barrier lakes considering human intervention, characterized in that: The method includes: Establishing a quantitative model for the impact of artificial intervention measures on the peak flood of the barrier lake breach and the risk population; Establishing a loss function of the disaster-bearing body under artificial intervention conditions; Quantitatively evaluating the impact of artificial measures on the disaster chain of the barrier lake breach and predicting risks through the calculation results of the quantitative model and the loss function; The quantitative model for the impact of the artificial intervention measures on the peak flood of the barrier lake breach and the risk population is as shown in Equation (1): （1） Where: Q p is the maximum peak discharge of the dammed lake under non-artificial intervention, m 3 / s. By adjusting the excavation depth, width, and slope of the drainage channel, the peak discharge of the breach can be reduced to Q p ’ , m 3 / s, I M1 is the intervention intensity of the drainage measure of the drainage channel, I M2 is the intervention intensity of the water project scheduling, Q p ’’ is the maximum peak discharge of the dammed lake after taking artificial intervention measures of water project scheduling, m 3 / s, ρ p is the risk population density of the dammed lake under non-artificial intervention, people / km 2 , and the risk population can be reduced to ρ p ’ , I M3 is the intervention intensity of the emergency transfer; Through Q p ’’ and ρ p ’ values, conduct a quantitative assessment of the impact of artificial measures on the dam-break disaster chain of the barrier lake and risk prediction; The loss function of the disaster-bearing body under the artificial intervention conditions is as shown in Equation (2): （2） In the formula: L 1 (F), L 2 (F) and L 3 (F) respectively correspond to the loss functions of the three types of disaster-bearing bodies of population, economy, and ecology under artificial intervention conditions, L 01 (F), L 02 (F) and L 03 (F) respectively correspond to the loss functions of the three types of disaster-bearing bodies of population, economy, and ecology under non-artificial intervention conditions, F is the flood intensity, m 3 / s, β n is the loss reduction coefficient corresponding to the adoption of artificial measures n ; γ is the influence coefficient of artificial intervention measures on flood intensity, and e is the natural constant; By L 1 (F), L 2 (F) and L 3 (F) Artificial measures are taken on the function values to quantitatively evaluate the impact of the dammed lake breach disaster chain and predict the risks.

2. The method for establishing a simulation model of the risk of dammed lake breach considering human intervention according to claim 1, wherein The loss reduction coefficient β n and the strength influence coefficient γ are calculated as follows: Construct a correction function for the losses of disaster-affected bodies under artificial intervention based on historical real-case data, extract the actual intervention intensity and loss data, and optimize using the particle swarm algorithm β n and γ , until convergence, denoted as β 优n and γ 优 , as shown in (3): （3） The said β 优n and γ 优 are respectively used as the optimal solutions of β n and γ in formula (2).

3. A device, characterized in that, Including: At least one processor; And a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1 to 2.

4. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method according to any one of claims 1 to 2.

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