A method and apparatus for quantitative assessment of flash flood disaster risk and loss considering cascading effects

Abstract

This application discloses a method and apparatus for quantitatively assessing the risk and loss of flash flood disasters considering cascading effects, relating to the field of disaster prevention and mitigation technology. The method simulates flood evolution by constructing a hydrodynamic model that includes a baseline and multiple coupled operating conditions, outputs simulation results, and calculates risk indicators under each operating condition. It accurately quantifies the cascading risk amplification coefficient by comparing the indicator values ​​of the baseline and coupled operating conditions. Furthermore, it establishes a grading standard based on the cascading risk amplification coefficient and identifies key risk intervals. Finally, by embedding the amplification coefficient into the loss assessment model, it accurately calculates the loss in the key risk intervals, achieving precise quantification and scientific assessment of the entire process of cascading effects from physical mechanisms to economic losses.

Description

Technical Field

[0001] This application relates to the field of disaster prevention and mitigation technology, and in particular to a method and apparatus for quantitatively assessing the risk and loss of flash flood disasters considering cascading effects. Background Technology

[0002] Flash floods, as a common natural disaster, pose a serious threat to people's lives and property and regional economic development. During a flash flood, there is a significant cascading effect. Traditional flash flood risk assessment methods mainly analyze single disaster factors, failing to fully consider the cascading amplification effect caused by the coupling of multiple factors. This makes it impossible to accurately assess the risk and loss from flash floods and to meet the needs of precise disaster prevention and mitigation.

[0003] Therefore, there is an urgent need to establish a quantitative assessment method for the risk loss of flash floods that can accurately quantify cascading effects and scientifically evaluate risk losses. Summary of the Invention

[0004] The purpose of this application is to provide a method and device for quantitative assessment of flash flood disaster risk and loss considering cascading effects, so as to achieve accurate quantification of cascading effects and scientific assessment of risk and loss, and provide technical support for disaster prevention and mitigation.

[0005] To achieve the above objectives, this application provides the following solution:

[0006] Firstly, this application provides a method for quantitatively assessing the risk and loss of flash flood disasters considering cascading effects, including:

[0007] Obtain basic data for the study area;

[0008] Based on the aforementioned basic data, a hydrodynamic model is constructed, multiple scenario calculation conditions are set, the flood evolution process under different conditions is simulated, and the simulation results are output. The calculation conditions include a baseline condition and at least one coupled condition, and the coupled condition considers at least one cascaded risk factor.

[0009] Based on the simulation results, the risk index values ​​for each working condition are calculated, and the cascade risk amplification coefficient for each risk index is calculated based on the risk index values ​​for the baseline working condition and the coupled working condition.

[0010] Based on the aforementioned cascaded risk amplification coefficient, a risk amplification effect classification standard is established to identify key risk amplification intervals;

[0011] Construct a quantitative loss assessment model that considers cascading effects;

[0012] For the identified key risk amplification range, the risk loss assessment result is output according to the loss quantitative assessment model.

[0013] Optionally, the basic data includes topographic data, land use data, and disaster-bearing distribution data.

[0014] Optionally, the coupled working conditions include multiple confluence working conditions, bridge water obstruction working conditions, and composite working conditions.

[0015] Optionally, the cascaded risk factors include the convergence effect of multiple branches, the water-blocking effect of bridges, and the combined effect of flood frequencies.

[0016] Optionally, the risk indicators include the flooded area, flood depth, affected population, and economic losses.

[0017] Optionally, based on the cascaded risk amplification coefficient, a risk amplification effect classification standard is established to identify key risk amplification intervals, specifically including:

[0018] The flood-inundated area is divided into multiple intervals according to water depth. Based on the cascade risk amplification coefficient of each interval and combined with the disaster-bearing body density data, the intervals that meet the set conditions are identified as key risk amplification intervals. The set conditions include: the disaster-bearing body density is higher than a preset density threshold, and the cascade risk amplification coefficient of economic loss is higher than a set coefficient threshold.

[0019] Optionally, after performing the step "dividing the flood-inundated area into multiple intervals according to water depth, and identifying the intervals that meet the set conditions as key risk amplification intervals based on the cascade risk amplification coefficient of each interval and combined with the disaster-bearing body density data", the quantitative assessment method for flash flood disaster risk loss considering cascade effects further includes: performing spatial heterogeneity analysis on the key risk amplification intervals based on the basic risk value and the cascade risk amplification coefficient to identify risk area types. The basic risk value refers to the initial risk level under the baseline conditions. The risk area types include high-risk concentrated areas, highly sensitive amplification areas, and low-risk stable areas. The high-risk concentrated areas are areas where the basic risk value is higher than a first threshold and the cascade risk amplification coefficient is within a first range. The highly sensitive amplification areas are areas where the basic risk value is lower than a second threshold and the cascade risk amplification coefficient is higher than a third threshold. The low-risk stable areas are areas where the basic risk value is lower than the second threshold and the cascade risk amplification coefficient is lower than a fourth threshold.

[0020] Optionally, the risk loss assessment results include the economic losses of various disaster-bearing entities and the total economic losses.

[0021] In a second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the quantitative assessment method for flash flood disaster risk loss considering cascading effects described in the first aspect above.

[0022] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the quantitative assessment method for flash flood disaster risk loss considering cascading effects described in the first aspect above.

[0023] According to the specific embodiments provided in this application, this application has the following technical effects:

[0024] This application provides a method and apparatus for quantitatively assessing the risk and loss of flash flood disasters considering cascading effects. The method constructs a complete technical logic chain of "data support - scenario comparison - coefficient quantification - hierarchical identification - model evaluation": First, by acquiring high-precision basic data and constructing a hydrodynamic model, a scenario matrix is ​​set up including a baseline condition and multiple coupled conditions (covering multiple confluences, bridge water obstruction, and cascading risk factors), providing a precise comparative basis for cascading effect analysis, while outputting key simulation results such as inundation range and water depth; then, based on the risk index values ​​(inundation area, inundation depth, affected population, and economic loss) of the baseline and coupled conditions, the cascading risk amplification coefficient is calculated, thus reducing the risk amplification factor that was originally difficult to quantify. The multi-factor coupling cascade effect is transformed into a calculable dimensionless coefficient, enabling precise measurement of the cascade effect. Subsequently, by establishing a risk amplification effect classification standard and identifying key risk amplification intervals, the core area with the most significant cascade effect is precisely located, avoiding assessment generalization. Finally, a quantitative loss assessment model considering the cascade effect is constructed for the key risk intervals, combining the cascade risk amplification coefficient with the basic loss and value coefficient of the disaster-bearing body, achieving an organic connection from cascade effect quantification to risk loss calculation, and ultimately outputting scientific risk loss assessment results. This comprehensively solves the problems of single-factor analysis, difficulty in quantifying cascade effects, and inaccurate loss assessment in traditional methods, achieving precise quantification of cascade effects and scientific risk loss assessment. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart illustrating a method for quantitatively assessing the risk and loss of flash flood disasters considering cascading effects, as described in Embodiment 1 of this application.

[0027] Figure 2 This is a comparison chart of the loss amounts under various working conditions for a design flood with P=1% in Example 1 of this application.

[0028] Figure 3This is a comparison chart of the loss amount in different areas under the design flood of P=1% in the embodiments of this application.

[0029] Figure 4 This is a schematic diagram of the structure of a computer device provided in Embodiment 2 of this application. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] The cascading effect refers to the nonlinear risk amplification phenomenon generated by the interaction of multiple risk factors (such as multiple branches converging, bridge obstruction, and terrain changes) during flash floods. Its characteristic is a risk superposition effect of 1+1>2.

[0032] Research has revealed that traditional methods for assessing flash flood risks have several shortcomings, making it difficult to meet the needs of precise disaster prevention and mitigation. These shortcomings include:

[0033] (1) Cascade effect not considered: Traditional methods are mostly based on a single disaster factor for analysis, ignoring the cascade risk amplification effect caused by the coupling of multiple factors, resulting in the assessment results failing to reflect the actual disaster risk level.

[0034] (2) Lack of quantitative model for cascading effect: In the existing technology, there is no mature quantitative model that can accurately calculate the cascading risk amplification effect, and it is impossible to clarify the specific impact of the cascading effect on disaster risk.

[0035] (3) Lack of loss quantification methods: It is difficult to convert the cascading risk amplification effect into specific economic losses, and it is impossible to accurately predict the direct economic losses caused by disasters, which is not conducive to subsequent disaster relief funding planning and resource allocation.

[0036] (4) Insufficient understanding of spatial heterogeneity: There is a lack of research on the spatial heterogeneity characteristics of risk evolution, the accuracy of assessment methods is low, and it is impossible to accurately identify the risk differences in different regions.

[0037] (5) Difficulty in identifying critical risk zones: There is a lack of effective methods for identifying critical risk zones, making it difficult to locate the risk zones and spatial locations with the most significant cascading effects, resulting in a lack of targeted disaster prevention and mitigation efforts.

[0038] In response, this embodiment constructs a cascade risk amplification coefficient quantification model and a loss quantification assessment system to achieve accurate quantification of the cascade risk amplification effect and scientific assessment of risk loss.

[0039] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, this application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] Example 1

[0041] This embodiment provides a method for quantitatively assessing the risk and loss of flash flood disasters considering cascading effects, including:

[0042] Obtain basic data for the study area;

[0043] Based on the aforementioned basic data, a hydrodynamic model is constructed, multiple scenario calculation conditions are set, the flood evolution process under different conditions is simulated, and the simulation results are output. The calculation conditions include a baseline condition and at least one coupled condition, and the coupled condition considers at least one cascaded risk factor.

[0044] Based on the simulation results, the risk index values ​​for each working condition are calculated, and the cascade risk amplification coefficient for each risk index is calculated based on the risk index values ​​for the baseline working condition and the coupled working condition.

[0045] Based on the aforementioned cascaded risk amplification coefficient, a risk amplification effect classification standard is established to identify key risk amplification intervals;

[0046] Construct a quantitative loss assessment model that considers cascading effects;

[0047] For the identified key risk amplification range, the risk loss assessment result is output according to the loss quantitative assessment model.

[0048] The following is combined with Figure 1 The quantitative assessment method for flash flood disaster risk loss considering cascading effects in this embodiment is described in detail.

[0049] like Figure 1 As shown, the quantitative assessment method for flash flood disaster risk loss considering cascading effects includes:

[0050] S1. Basic Data Acquisition and Hydrodynamic Modeling

[0051] Acquire high-precision topographic data, land use data, and disaster-bearing body distribution data for the study area.

[0052] High-precision topographic data is used to extract watershed hydrological parameters (such as confluence paths, catchment areas, and river networks). A two-dimensional shallow water equation hydrodynamic model (i.e., a two-dimensional hydrodynamic model) is constructed based on watershed hydrological parameters and land use data. Land use data is used to determine the roughness parameters of different underlying surfaces in the hydrodynamic model (roughness coefficients of different underlying surfaces such as water bodies, cultivated land, forest land, and built-up land), which directly affects the accuracy of flood evolution simulation. Disaster-bearing body distribution data (including spatial distribution and value information of residential buildings, infrastructure, agricultural land, etc.) is used for loss assessment calculations in subsequent steps. The data is overlaid with the inundation range and water depth output by the hydrodynamic model to calculate the exposure degree of various disaster-bearing bodies.

[0053] UAV oblique photography technology is used to acquire high-precision terrain data with a resolution better than 10cm, which is used to construct an accurate digital elevation model. DOM, DSM, and DEM data are acquired by setting predetermined flight altitude and overlap parameters.

[0054] A two-dimensional hydrodynamic model is constructed, and multiple scenario calculation conditions are set to provide a comparative basis for cascade effect analysis: The purpose of constructing the two-dimensional hydrodynamic model is to simulate the flood evolution process under different scenarios and output simulation results such as inundation range, water depth, and flow velocity, so as to provide basic data for the calculation of the cascade risk amplification coefficient in step S2.

[0055] The multi-scenario working condition settings include:

[0056] ① Baseline operating conditions: Design flood conditions with different return periods;

[0057] ② Multiple tributary confluence condition: Time adjustment is used to achieve synchronous arrival of flood peaks from multiple tributaries;

[0058] ③ Bridge water obstruction conditions: Set up bridge water obstruction conditions with different degrees of obstruction;

[0059] ④ Complex operating conditions: scenarios involving a combination of multiple risk factors.

[0060] S2, Construction of a Quantitative Model for Cascaded Risk Amplification Coefficient

[0061] Identify key cascading risk factors and, through comparative analysis of changes in specific risk indicators under different scenarios (risk indicators include quantifiable indicators such as inundation area, inundation depth, affected population, and economic losses), establish a quantitative model of the cascading risk amplification coefficient to quantify the amplification effect of each risk factor.

[0062]

[0063] in, Let i be the cascading risk amplification coefficient for the i-th indicator. Let i be the value of the i-th risk indicator under the coupled scenario. Let be the value of the i-th risk indicator under the baseline scenario.

[0064] By spatially overlaying the inundation range and water depth distribution map output by the hydrodynamic model in step S1 with the disaster-bearing body distribution map in a GIS (Geographic Information System), it is possible to determine which disaster-bearing bodies are located in the inundation area and what their corresponding inundation depth is. By combining the vulnerability curves or loss rate functions of various types of disaster-bearing bodies (i.e., the proportion of asset value loss at different water depths), the economic loss of each type of disaster-bearing body can be calculated.

[0065] Key cascading risk factors include:

[0066] ①Multiple tributary confluence effect: By adjusting the arrival time of tributary flood peaks, multiple tributary flood peaks are simultaneously incorporated into the main stream, and the amplification effect of multiple tributary confluence on the risk of the main stream is analyzed.

[0067] ② Bridge water obstruction effect: Set up bridge cross-sectional shrinkage with different degrees of obstruction (50% and 90% of flow area shrinkage) and analyze the amplification effect of bridge water obstruction on upstream risks;

[0068] ③ Flood frequency combination effect: Set up combination scenarios of floods with different return periods and analyze the risk amplification characteristics under the composite scenarios;

[0069] The cascading risk amplification factor was determined through a comparative analysis of the baseline scenario and the coupled scenario.

[0070] S3. Cascade Risk Amplification Effect Classification Assessment

[0071] Based on the risk amplification coefficient distribution characteristics of actual calculation results, a cascade risk amplification effect classification standard is established. Multiple water depth classification thresholds are set, and the risk transmission characteristics of different water depth intervals are systematically analyzed. The specific process is as follows: the inundated area is divided into multiple intervals according to water depth (such as 0-0.05m, 0.05-0.3m, 0.3-0.5m, 0.5-1.0m, 1.0-2.0m, >2.0m). The inundation area, the number of affected disaster-bearing bodies, and the economic losses of each water depth interval under the baseline scenario and coupled scenario are statistically analyzed. The cascade risk amplification coefficient of each water depth interval is calculated, and the water depth interval with the most significant risk amplification effect is determined through comparative analysis.

[0072] The water depth ranges identified as having the most significant risk amplification effect are designated as key areas for quantitative loss assessment and are prioritized for allocating disaster prevention and mitigation resources. These ranges represent the areas with the most pronounced cascading effects and require detailed loss assessment and deployment of protective measures.

[0073] Based on the risk transmission patterns in different water depth ranges, a nonlinear relationship model between water depth and loss rate is established to identify key risk amplification ranges. The identified key risk amplification ranges are defined as areas with a risk amplification coefficient λ>1.5 and a high density of disaster-bearing bodies. These areas simultaneously meet two conditions: (1) the risk amplification coefficient caused by the cascading effect exceeds the set threshold; (2) there are a large number of disaster-bearing bodies (residential buildings, infrastructure, etc.) in the area.

[0074] It is important to note that a comprehensive judgment method is used when identifying critical risk amplification zones: First, the risk amplification coefficient of each risk indicator (flooded area, affected population, economic loss, etc.) is calculated; the economic loss risk amplification coefficient is used as the main judgment indicator; when the economic loss risk amplification coefficient λ>1.5 and the density of disaster-bearing bodies in the area is high, it is judged as a critical risk amplification zone (other risk indicators are used as auxiliary references to help comprehensively assess the risk situation).

[0075] Spatial heterogeneity analysis was performed on the identified key risk amplification intervals to identify risk region types, including:

[0076] ① High-risk concentration type: This refers to areas with high basic risk (significant losses under the baseline scenario) and moderate amplification factor (1.2 < λ < 1.5). These areas are inherently high-risk zones, and the cascading effect will further exacerbate the risk. Basic risk refers to the initial risk level under the baseline scenario, obtained through a comprehensive evaluation of indicators such as inundation depth and disaster-bearing body density under the baseline scenario.

[0077] ② Highly sensitive amplification area: refers to areas with low basic risk and high amplification factor;

[0078] ③ Low-risk stable type: refers to areas with low basic risk and low amplification factor.

[0079] This step establishes a tightly linked identification process through refined water depth classification, enabling a leap from physical mechanism analysis to precise risk management. First, by comparing the rate of change of economic losses in different water depth ranges (e.g., 0.3-0.5m, 1.0-2.0m) under baseline and coupled operating conditions, the water depth range with the most significant risk amplification effect is identified. This reveals the physical laws governing the cascading effect on floods and the core damage threshold. Next, by overlaying these physical laws with the spatial distribution of disaster-bearing bodies, areas that simultaneously meet the dual conditions of being "in a significantly amplified water depth range" and having "high disaster-bearing body density" are identified, thus pinpointing key risk amplification ranges and achieving a crucial transformation of risk from a "physical dimension" to a "socio-economic impact dimension." Finally, based on the combined characteristics of the "basic risk level" and "risk amplification coefficient" of each key area, they are categorized into types such as "high-risk concentrated type" and "highly sensitive amplification type," thereby clarifying the essential causes of risk and providing a direct basis for formulating differentiated prevention and control strategies. The entire process is progressive and gradually focuses on key aspects, with water depth classification serving as the underlying quantitative foundation, ensuring the scientific rigor and accuracy of the analysis.

[0080] S4. Quantitative Assessment and Output of Risk Loss

[0081] For the key risk amplification range identified in step S3, a quantitative loss assessment model considering cascading effects is established. This model directly quantifies the cascading risk amplification effect as a loss amplification coefficient, achieving an organic connection from risk quantification to loss assessment.

[0082]

[0083] in, The total direct economic loss, For the basic loss of the k-th unit of the j-th disaster-bearing body, This is the corresponding risk amplification factor. It represents the value coefficient of the disaster-bearing body and outputs risk loss assessment results.

[0084] For each scenario (including multiple confluence conditions, bridge water obstruction conditions, and complex conditions), risk area type identification and quantitative loss assessment are required. The quantitative loss assessment includes the following calculations:

[0085] ① Damage to residential buildings:

[0086] in, This represents the economic loss (in ten thousand yuan) suffered by residents. Indicates the number of affected houses (buildings). This indicates the unit price of the house (ten thousand yuan / building). Indicates the percentage of damage to the building. This represents the cascading risk amplification factor for housing losses;

[0087] ② Loss of family property:

[0088] in, This indicates the economic loss of family property (in ten thousand yuan). Indicates the number of affected households. This represents the average value of a family's assets (in ten thousand yuan per household). Indicates the property loss rate (%).

[0089] A cascading risk amplification factor representing the loss of family property;

[0090] ③Agricultural losses:

[0091] in, This represents agricultural economic losses (in ten thousand yuan). Indicates the area of ​​affected crops (in mu). This represents the output value per unit area (ten thousand yuan / mu). Indicates the percentage of crop damage. The cascading risk amplification factor representing agricultural losses;

[0092] ④ Infrastructure losses:

[0093] in, This represents the economic loss to infrastructure (in ten thousand yuan). This indicates the value of infrastructure assets (in ten thousand yuan). Indicates the degree of infrastructure damage (%). This represents the cascading risk amplification factor for infrastructure losses;

[0094] The cascaded risk amplification coefficient λ for various types of losses is determined by the ratio of economic losses of the disaster-bearing body under the coupled scenario and the baseline scenario. The calculation takes into account the different degrees of damage caused to the disaster-bearing body by different inundation depths.

[0095] The quantitative assessment method for flash flood disaster risk loss considering cascading effects in this embodiment achieves the following: ① Innovatively, it achieves precise quantification of cascading effects: a quantification method for cascading risk amplification coefficient based on scenario comparison analysis is established. ② It identifies key mechanisms and intervals of risk amplification: the system reveals that specific water depth intervals are the core intervals of cascading risk amplification, providing a scientific basis for precise prevention and control. ③ It pioneers a graded assessment system for cascading risk amplification effects: a graded standard for cascading risk amplification effects is established, providing support for developing differentiated risk management strategies for different regions. ④ It improves the accuracy and reliability of flash flood risk assessment: by conducting independent risk assessments and loss calculations for each scenario, the disaster risk characteristics under different conditions can be comprehensively grasped, providing scientific support for the development of emergency plans.

[0096] Taking a certain watershed as the research object, the watershed area is 152 km² 2 The method described in this embodiment is used for cascading risk assessment.

[0097] (1): Basic data acquisition and modeling

[0098] Data acquisition: Oblique photography was conducted using an APS-130 five-lens camera mounted on a drone. The flight altitude was set at 130m, and the overlap in both the flight direction and the lateral direction was 80%. DOM, DSM, and DEM data with a resolution of 5cm were acquired.

[0099] Hydrodynamic modeling: A two-dimensional hydrodynamic model was constructed using MIKE21 Flow Model FM, covering an area of ​​9.71 km². 2 The system consists of 8787 triangular computational grids and 4627 computational nodes, with grid side lengths of 10-20m in key areas.

[0100] Parameter settings: Roughness is set to 0.025 for water area, 0.055 for cultivated land, 0.07 for forest land, and 0.09 for building land.

[0101] (2): Construction of a quantitative model for cascaded risk amplification coefficient

[0102] Identify and quantify key risk factors:

[0103] ① Multiple tributary confluence effect: The flood peak of the tributaries arrives at the confluence section 6 minutes earlier;

[0104] ② Bridge water-blocking effect: Two scenarios are set: half-blocking (50% of the flow area) and full-blocking (90% blockage);

[0105] ③ Flood frequency combination effect: five combinations of return periods, P=1%, 2%, 5%, 10%, and 20%;

[0106] Each risk factor was quantified through a comparative analysis of the baseline scenario and the coupled scenario.

[0107] Based on the actual calculation results, a cascading risk amplification effect classification standard was established. Based on the calculation results of this embodiment, the risk amplification coefficient distribution is as follows: the flooded area amplification coefficient is 1.14 (slight amplification), the affected population amplification coefficient is 1.25 (moderate amplification), and the economic loss amplification coefficient is 1.56 (significant amplification).

[0108] (3): Cascade risk amplification effect classification assessment

[0109] ① Taking a flood with a P=1% risk level as an example, risk indicators are compared and assessed for each scenario:

[0110] Baseline scenario: Inundation area 2.89 km² 2 The maximum water depth was 4.50m, affecting 5,300 people and causing economic losses of 91.72 million yuan.

[0111] Bridge completely blocked scenario: 3.28 km² submerged area. 2 The maximum water depth was 5.28 meters, affecting 6,500 people and causing economic losses of 125.88 million yuan.

[0112] Multiple converging scenarios: 3.15 km² submerged area. 2 The maximum water depth was 4.98 meters, affecting 5,900 people and causing economic losses of 108.45 million yuan.

[0113] Composite scenario (bridge full resistance + multiple branches converging): inundation area 3.30 km² 2 The maximum water depth was 5.29m, affecting 6,600 people and causing economic losses of 143.11 million yuan.

[0114] ② Calculation and classification of risk amplification factors for each scenario:

[0115] Bridge complete obstruction scenario:

[0116] Submerged area magnification factor: λA = 3.28km 2 / 2.89km 2 = 1.13

[0117] Affected population magnification factor: λP = 0.65 million people / 0.53 million people = 1.23

[0118] Economic loss amplification factor: λE = 125.88 million yuan / 91.72 million yuan = 1.37

[0119] Multiple convergence scenarios:

[0120] Submerged area magnification factor: λA = 3.15km 2 / 2.89km 2 = 1.09

[0121] Affected population magnification factor: λP = 0.59 million people / 0.53 million people = 1.11

[0122] Economic loss amplification factor: λE = 108.45 million yuan / 91.72 million yuan = 1.18

[0123] Composite scenario (bridge total resistance + multiple branches converging):

[0124] Flooded area magnification factor: λA = 3.30km 2 / 2.89km 2 = 1.14

[0125] Affected population magnification factor: λP = 0.66 million people / 0.53 million people = 1.25

[0126] Economic loss amplification factor: λE = 143.11 million yuan / 91.72 million yuan = 1.56

[0127] Based on the aforementioned risk amplification factor, the numerical range of λ in this embodiment is classified as follows: slight amplification (1.0≤λ<1.2), moderate amplification (1.2≤λ<1.5), significant amplification (1.5≤λ<2.0), and extremely strong amplification (λ≥2.0).

[0128] ③ Identification of Key Risk Amplification Zones: In this embodiment, water depth grading thresholds of 0.05m, 0.3m, 0.5m, 1.0m, and 2.0m were set. It was found that the 1.0-2.0m water depth range under the composite scenario (total bridge resistance + multiple branch convergence) was the core zone for cascading risk amplification. Economic losses in this zone increased from 31.54 million yuan to 84.43 million yuan, an increase of 168%. Figure 2 As shown, this water depth range therefore becomes the focus of the loss assessment in step 4.

[0129] (4): Quantitative assessment and output of risk loss

[0130] ① Spatial heterogeneity analysis (taking a complex scenario as an example):

[0131] For detailed results of the spatial heterogeneity analysis, please refer to Figure 3 Among them, the high-risk concentrated type is Baxi Village, with a basic scenario loss of 48.96 million yuan and a composite scenario loss of 80.64 million yuan after cascading effects, with an amplification factor of 1.65; the highly sensitive amplification types include Yangjiao Village (basic scenario loss of 14.04 million yuan, composite scenario loss of 26.83 million yuan after cascading effects, with an amplification factor of 1.91), Songyan Village (basic scenario loss of 4.8 million yuan, composite scenario loss of 7.56 million yuan after cascading effects, with an amplification factor of 1.57) and Xiecun Village (basic scenario loss of 1.63 million yuan, composite scenario loss of 2.72 million yuan after cascading effects, with an amplification factor of 1.66); the remaining administrative villages are all low-risk stable types.

[0132] ② Quantitative assessment of classification loss (taking a complex scenario as an example):

[0133] Based on the quantitative assessment of losses, the following comparisons were made between the composite scenario and the baseline scenario for various types of losses: residential housing losses increased from 56.21 million yuan to 93.36 million yuan (magnification factor 1.66); household property losses increased from 17.93 million yuan to 28.93 million yuan (magnification factor 1.61); agricultural losses increased from 15.27 million yuan to 17.63 million yuan (magnification factor 1.15); infrastructure losses increased from 2.31 million yuan to 3.19 million yuan (magnification factor 1.38); and total losses increased from 91.72 million yuan to 143.11 million yuan (total magnification factor 1.56).

[0134] The total losses for each scenario are compared as follows: the baseline scenario has a total loss of 91.72 million yuan, the bridge obstruction scenario has a total loss of 125.88 million yuan (magnification factor 1.37), the multiple confluence scenario has a total loss of 108.45 million yuan (magnification factor 1.18), and the composite scenario has a total loss of 143.11 million yuan (magnification factor 1.56). Through independent assessment of each scenario, the composite scenario was identified as having the highest risk amplification effect, with bridge obstruction being the primary cascading risk amplification factor. Among various disaster-bearing entities, residential buildings were most significantly affected by the cascading effect, followed by household property and infrastructure, while the cascading amplification effect on agricultural losses was relatively small.

[0135] This embodiment successfully identified bridge water obstruction as the main cascading risk amplification factor through intelligent quantitative analysis of cascading risks. It quantified the risk amplification effect under different scenarios, established a quantitative assessment system for losses considering cascading effects, and achieved accurate quantitative assessment of flash flood disaster risk losses, providing important technical support for flash flood disaster prevention and control decision-making and emergency management.

[0136] Example 2

[0137] This embodiment provides a computer device, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores data related to a quantitative assessment method for flash flood risk loss considering cascading effects. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a quantitative assessment method for flash flood risk loss considering cascading effects as described in Example 1.

[0138] Those skilled in the art will understand that Figure 4 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0139] Example 3

[0140] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for quantitatively assessing the risk and loss of flash flood disasters considering cascading effects, as described in Embodiment 1 above.

[0141] Example 4

[0142] This embodiment provides a computer program product, including a computer program that, when executed by a processor, implements a quantitative assessment method for the risk loss of flash flood disasters considering cascading effects, as described in Embodiment 1 above.

[0143] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0144] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0145] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0146] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0147] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for quantitatively assessing the risk and loss of flash flood disasters considering cascading effects, characterized in that, The cascade effect refers to the nonlinear risk amplification phenomenon caused by the interaction of multiple risk factors during flash floods, which manifests as a risk superposition effect. The quantitative assessment method for flash flood disaster risk loss considering cascading effects includes: Obtain basic data for the study area; Based on the aforementioned basic data, a hydrodynamic model is constructed, and multiple scenario calculation conditions are set to simulate the flood evolution process under different conditions and output simulation results. The calculation conditions include a baseline condition and at least one coupled condition, and the coupled condition considers at least one cascaded risk factor. The coupled condition includes a multi-branch confluence condition, a bridge water obstruction condition, and a composite condition. Based on the simulation results, the risk index values ​​for each working condition are calculated, and the cascaded risk amplification coefficient for each risk index is calculated based on the risk index values ​​of the baseline working condition and the coupled working condition; the formula for calculating the cascaded risk amplification coefficient is as follows: ;in, Let be the cascading risk amplification coefficient for the i-th indicator. Let i be the value of the i-th risk indicator under the coupled scenario. Let i be the value of the i-th risk indicator under the baseline operating condition; Based on the aforementioned cascaded risk amplification coefficient, a risk amplification effect grading standard is established to identify key risk amplification intervals; specifically including: The flood-inundated area is divided into multiple intervals according to water depth. Based on the cascade risk amplification coefficient of each interval and combined with the disaster-bearing body density data, the intervals that meet the set conditions are identified as key risk amplification intervals. The set conditions include: the disaster-bearing body density is higher than a preset density threshold, and the cascade risk amplification coefficient of economic loss is higher than a set coefficient threshold. Based on the basic risk value and the cascaded risk amplification coefficient, spatial heterogeneity analysis is performed on the key risk amplification interval to identify the risk region type. A quantitative loss assessment model considering cascading effects is constructed; this model quantifies the cascading risk amplification effect into a loss amplification coefficient, calculated using the following formula: ； in, The total direct economic loss, For the basic loss of the k-th unit of the j-th disaster-bearing body, This is the corresponding risk amplification factor. The value coefficient of the disaster-bearing body; For the identified key risk amplification range, the risk loss assessment result is output according to the loss quantitative assessment model.

2. The method for quantitative assessment of flash flood disaster risk and loss considering cascading effects according to claim 1, characterized in that, The basic data includes topographic data, land use data, and disaster-bearing distribution data.

3. The method for quantitative assessment of flash flood disaster risk and loss considering cascading effects according to claim 1, characterized in that, The cascaded risk factors include the convergence effect of multiple branches, the water-blocking effect of bridges, and the combined effect of flood frequencies.

4. The method for quantitative assessment of flash flood disaster risk and loss considering cascading effects according to claim 1, characterized in that, The risk indicators include the flooded area, flood depth, affected population, and economic losses.

5. The method for quantitative assessment of flash flood disaster risk and loss considering cascading effects according to claim 1, characterized in that, The basic risk value refers to the initial risk level under the baseline operating condition. The risk area types include high-risk concentrated areas, high-sensitivity amplification areas, and low-risk stable areas. The high-risk concentrated areas are areas where the basic risk value is higher than the first threshold and the cascaded risk amplification coefficient is within the first range. The high-sensitivity amplification areas are areas where the basic risk value is lower than the second threshold and the cascaded risk amplification coefficient is higher than the third threshold. The low-risk stable areas are areas where the basic risk value is lower than the second threshold and the cascaded risk amplification coefficient is lower than the fourth threshold.

6. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for quantitative assessment of flash flood disaster risk loss considering cascading effects as described in any one of claims 1-5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the quantitative assessment method for flash flood disaster risk loss considering cascading effects as described in any one of claims 1-5.

Images