Multi-dimensional toughness evaluation method for flood storage and detention area

Abstract

The invention discloses a flood storage and detention area multi-dimensional toughness evaluation method, which comprises the steps of determining an evaluation target, and performing index data selection on the evaluation target through multi-source data acquisition and area customization extension, and carrying out weight distribution on the selected index data by utilizing a subjective weighting method, an objective weighting method or a combined weighting method to obtain a comprehensive score, and carrying out toughness grading and output decision making on the flood storage and detention area according to the comprehensive score and a drainage basin flood control planning target. The comprehensive toughness of the flood storage and detention areas is dynamically evaluated through multi-dimensional indexes, and the method is suitable for flood control planning, ecological management and regional sustainable development decision making. The technical problems that in the prior art, single-dimension limitation exists, defects exist in static evaluation, and the regional adaptability is poor are solved.

Description

Technical Field

[0001] This invention belongs to the field of disaster risk management and water conservancy engineering technology, specifically involving a multi-dimensional resilience assessment method for flood storage and detention areas, which is applicable to flood control planning, ecological governance and regional sustainable development decision-making. Background Technology

[0002] Traditional flood storage and detention area assessments primarily focus on flood control capabilities (such as reservoir capacity and dike standards), neglecting ecological resilience, social adaptability, and intelligent regulation capabilities. For example, patent application number ZW0020207420 discloses a multi-dimensional resilience assessment method for flood storage and detention areas. This method identifies assessment objectives, selects indicator data for these objectives through multi-source data collection and regional customization, and uses subjective, objective, or combined weighting methods to assign weights to the selected indicator data to obtain a comprehensive score. Based on the comprehensive score and the watershed flood control planning objectives, the method performs resilience classification and outputs a decision regarding the flood storage and detention area. The method includes: Step 1, identifying assessment objectives; Step 2, selecting indicator data for these objectives through multi-source data collection and regional customization; Step 3, assigning weights to the selected indicator data using subjective, objective, or combined weighting methods to obtain a comprehensive score; and Step 4, performing resilience classification and outputting a decision regarding the flood storage and detention area based on the comprehensive score and the watershed flood control planning objectives.

[0003] The existing technology has the following defects: (1) Single-dimensional limitation: It only evaluates flood control or economic indicators and does not integrate ecological, social and other system elements; (2) Static evaluation defects: It adopts fixed weight models (such as the analytic hierarchy process) and cannot reflect the dynamic resilience changes in the evolution of floods; (3) Poor regional adaptability: The general model is difficult to be compatible with the needs of special areas such as sedimentation type (lower Yellow River) and groundwater sensitive type (North China Plain). Summary of the Invention

[0004] The purpose of this invention is to provide a multidimensional resilience assessment method for flood storage and detention areas, which can solve the problem of complex system coupling evaluation in flood storage and detention areas and provide quantitative basis for comprehensive management.

[0005] To achieve the above objectives, the present invention is implemented using the following technical solutions.

[0006] A multidimensional resilience assessment method for flood storage and detention areas includes:

[0007] Step 1: Determine the evaluation objectives.

[0008] Step 2: Select indicator data for the evaluation target through multi-source data collection and regional customized expansion.

[0009] Step 3: Use subjective weighting, objective weighting, or combined weighting methods to assign weights to the selected indicator data to obtain a comprehensive score.

[0010] Step 4: Based on the comprehensive score and the watershed flood control planning objectives, classify the resilience of flood storage and detention areas and make output decisions.

[0011] The weight allocation includes: based on the multidimensional characteristics of the assessment objectives, including flood control safety, ecological restoration, socio-economic factors, smart regulation, and special objectives, combined with a regionally customized and expanded indicator system, constructing a three-dimensional adaptive weight allocation mechanism of "objective-indicator-data"; dynamically selecting appropriate weighting methods based on the availability, quantification level, relevance, and regional type of indicator data, including sediment control, groundwater sensitivity, and water resource utilization; for combined weighting, adopting a differentiated fusion strategy of "subjective weight-objective weight", or first processing highly relevant indicators through dimensionality reduction algorithms before weighting, ensuring that the weight allocation accurately matches the assessment scenario, regional characteristics, and data conditions; the weight allocation results can be updated according to real-time data during flood evolution to achieve dynamic resilience assessment and avoid the limitations of static weights.

[0012] The aforementioned multidimensional resilience assessment method for flood storage and detention areas, wherein the assessment objectives are defined, include: flood control safety, ecological restoration, socio-economic development, intelligent regulation and control, and special objectives.

[0013] The specific objectives include: spatial optimization, sediment resource utilization, groundwater recharge, flood resource utilization, cold-region agriculture, and cross-border coordination.

[0014] The aforementioned multidimensional resilience assessment method for flood storage and detention areas includes the following indicator data: flood control indicators, ecological indicators, socio-economic indicators, and intelligent regulation indicators.

[0015] The flood control indicators include: flood diversion response time, dike compliance rate, personnel evacuation efficiency, waterlogging receding efficiency, and inundation depth control rate.

[0016] The ecological indicators include: wetland retention rate, water quality compliance rate, vegetation coverage rate, species diversity index, and fish population recovery rate.

[0017] The socio-economic indicators include: per capita compensation amount, industrial transformation rate, employment security rate, post-disaster GDP recovery period, and medical resource coverage.

[0018] The intelligent control indicators include: monitoring station density, flood forecast accuracy, early warning system delay, data update frequency, data sharing rate, and digital twin platform integration.

[0019] The aforementioned multidimensional resilience assessment method for flood storage and detention areas, in which the regional customized expansion refers to dynamically adding indicator data according to the type of flood storage and detention area.

[0020] If the flood storage and detention area type is sediment management type, the corresponding added indicator data includes annual sediment deposition, sediment resource utilization rate, sediment monitoring density, sediment deposition rate, sediment discharge efficiency, and riverbed uplift threshold; if the flood storage and detention area type is flood storage and detention area type, the corresponding added indicator data includes groundwater recharge rate, groundwater level rise, permeability coefficient variability, funnel area reduction, and water quality compliance rate; if the flood storage and detention area type is water resource utilization type, the corresponding added indicator data includes flood resource utilization rate and irrigation guarantee rate.

[0021] The aforementioned multidimensional resilience assessment method for flood storage and detention areas utilizes subjective weighting, objective weighting, or a combination of weighting methods to assign weights to selected indicator data to obtain a comprehensive score. The subjective weighting, objective weighting, and combination weighting methods are shown in Tables 1, 2, and 3, respectively.

[0022] Table 1 Subjective Empowerment Method

[0023]

[0024] Table 2 Objective Empowerment Method

[0025]

[0026] Table 3 Combination Weighting Method

[0027]

[0028] The subjective weighting methods include: Analytic Hierarchy Process (AHP), Delphi method, and fuzzy AHP.

[0029] The objective weighting methods include: entropy method, principal component analysis method, coefficient of variation method, and data envelopment analysis method.

[0030] The combined analysis methods include: subjective-objective weighted combination, CRITIC method, and fuzzy combination method.

[0031] The aforementioned multidimensional resilience assessment method for flood storage and detention areas includes two approaches: the subjective weighting method, which allocates weights based on expert judgment of the importance of indicators; and the objective weighting method, which determines weight allocation based on the statistical characteristics of indicator data. The combined analysis method improves the rationality of weight allocation by integrating the advantages of both methods.

[0032] The aforementioned multidimensional resilience assessment method for flood storage and detention areas includes the following selection methods for subjective weighting, objective weighting, or a combination of weighting methods: If data is scarce, such as in newly constructed flood storage and detention areas lacking long-term monitoring data, the Delphi method or analytic hierarchy process (AHP) should be used preferentially. If data is abundant and indicators are easily quantifiable, such as in flood storage and detention areas that have been operating for many years, the entropy method or CRITIC method should be used preferentially. If a balance between scientific rigor and practicality is required, the AHP-entropy method combination should be used preferentially. If there are many indicators with strong correlations, principal component analysis should be used to reduce dimensionality first, and then subjective methods should be combined to determine the final weights.

[0033] The core formula of the Delphi method is as follows:

[0034] (1) Initial value of single indicator weight

[0035]

[0036] in, : represents the score given by the i-th expert to the j-th indicator in the k-th round, where n is the total number of indicators;

[0037] (2) Final weight

[0038]

[0039] Where m is the number of experts participating in the final round, and k ∗ To converge the rounds, w ij Let be the final weight of the j-th indicator.

[0040] The core formula of the Analytic Hierarchy Process (AHP) is as follows:

[0041] (1) Judgment matrix construction and consistency test

[0042] Let the judgment matrix be... ; where a pq Let p be the importance ratio of indicators p to q, satisfying a pq =1 / a qp The core formula is as follows:

[0043] (2) Weight calculation

[0044] p=1,2,3…n

[0045]

[0046] (3) Consistency check

[0047]

[0048]

[0049] Where λmax is the largest eigenvalue of the judgment matrix, RI is the average random consistency index, n=1-10 has a standard value, and the matrix satisfies consistency when CR<0.1.

[0050] The core formula of the entropy method is as follows:

[0051] (1) Indicator standardization

[0052] Positive indicators:

[0053] Negative indicators:

[0054] Where, x ij For the original value of the j-th indicator of the i-th sample, max(x) j min(x) j ) represents the extreme value of the j-th index;

[0055] (2) Indicator weight

[0056]

[0057] Where ε is a minimum value to avoid the denominator being 0, and is usually taken as 0.0001, and m is the number of samples;

[0058] (3) Entropy and weight

[0059]

[0060]

[0061] Among them, e j The entropy value of the j-th index (0≤e) j ≤1), 1−e j The difference coefficient is denoted as .

[0062] The core formula of the CRITIC method is as follows:

[0063] (1) Standardization of indicators, positive / negative standardization formulas of the same entropy value method;

[0064] (2) Calculation of information content

[0065]

[0066] in, The j-th indicator is the standardized mean.

[0067] (3) Conflict calculation

[0068]

[0069]

[0070] Where, r jk Let C be the correlation coefficient between index j and k. j The conflict of index j;

[0071] (4) Overall weight

[0072]

[0073] The AHP-entropy method combined weighting formula is as follows:

[0074] (1) Normalization of subjective and objective weights

[0075] Let the subjective weight of AHP be... The objective weight of the entropy method is The formula is as follows:

[0076]

[0077]

[0078] (2) Combination weights

[0079]

[0080] in, This is a subjective weighting coefficient, typically set to 0.5, but can be adjusted to 0.3-0.7 depending on scientific and practical requirements. For combined weights.

[0081] The formula for principal component analysis dimensionality reduction combined with subjective method is as follows:

[0082] (1) Core formula for principal component dimensionality reduction

[0083] Covariance matrix:

[0084]

[0085] Eigenvalues ​​and eigenvectors:

[0086]

[0087] Where λ is the eigenvalue and ξ is the corresponding eigenvector;

[0088] Principal component extraction: Select the first k eigenvalues ​​λ1≥λ2≥...≥λ k If the cumulative variance contribution rate is ≥85%, principal components are constructed:

[0089]

[0090] (2) Determine the final weight by combining subjective methods

[0091] =Weight of the t-th principal component calculated by AHP method

[0092] ·

[0093] Where, ξ tj w is the eigenvector component corresponding to the original index j of the t-th principal component. j The final weights of the original indicators.

[0094] The aforementioned multidimensional resilience assessment method for flood storage and detention areas includes resilience grading levels of: high resilience, medium-high resilience, medium resilience, medium-low resilience, and low resilience. This resilience grading is used to provide a basis for management decisions.

[0095] The resilience levels of flood storage and detention areas are classified as shown in Table 4:

[0096] Table 4 Flood Detention Area Division

[0097]

[0098] The aforementioned multidimensional resilience assessment method for flood storage and detention areas refers to the development of targeted measures for flood storage and detention areas of different levels, including planning and construction, emergency management, and long-term improvement.

[0099] If the resilience level of the flood storage and detention area is high, the decision-making focus is on consolidating existing resilience, preventing functional degradation, and playing a demonstrative role. Specific measures include establishing a routine monitoring mechanism, such as monitoring dike conditions and ecosystem changes, regularly updating early warning models, promoting the experience of "resilient communities," such as providing emergency skills training for residents and establishing neighborhood mutual assistance networks, and exploring an integrated "flood control + ecology" model, such as utilizing wetlands for flood detention while simultaneously developing ecotourism. If the resilience level is medium to high, the decision-making focus is on addressing weaknesses, such as improving system synergy if a flood storage and detention area has low ecological resilience. Specific measures include targeted strengthening of weak indicators, such as increasing wetland area and vegetation coverage, improving cross-regional linkage mechanisms, such as coordinating with upstream reservoirs and downstream river channels, and appropriately improving economic resilience, such as guiding industries in low-lying areas to transform towards risk-resistant models. If the resilience level is medium... If the risk level is medium to low, the focus of the decision-making process is to comprehensively enhance key functions and reduce the possibility of the risk level shifting from "medium risk" to "high risk." Specific measures include upgrading core engineering facilities, such as raising the standards of dikes and adding emergency drainage pumps; improving the emergency response system, such as optimizing evacuation routes and increasing the capacity of flood shelters; and increasing financial investment, such as establishing a special fund for rapid post-disaster recovery. If the risk level is medium to low, the focus of the decision-making process is to focus on "emergency preparedness and risk avoidance" to rapidly improve the ability to withstand risks in the short term. Specific measures include urgently reinforcing dangerous sections of dikes, such as implementing seepage prevention measures for dikes and dredging rivers; simplifying evacuation procedures, such as establishing a "one-on-one" support mechanism for the elderly, the infirm, and the disabled; and restricting high-risk activities, such as prohibiting the construction of new residential areas or factories in low-lying areas. If the watershed is of low resilience, the decision-making focus should be on including it in the priority list for watershed transformation and adjusting its functional positioning if necessary. Specific measures include developing an overall reconstruction plan, such as relocating residents to other areas and rebuilding high-standard dikes; suspending non-essential development activities and converting it into an "ecological storage and retention area" to reduce the exposure of population and economic activities; and establishing a cross-departmental mechanism for tackling key issues, such as joint efforts among water conservancy, civil affairs, and finance departments to ensure funding and implementation.

[0100] The beneficial effects of this invention are as follows: This invention provides a multi-dimensional resilience assessment method for flood storage and detention areas, solving the technical problems of limitations in single-dimensional approaches, defects in static assessments, and poor regional adaptability in existing technologies. By constructing a resilience assessment system for flood storage and detention areas, this invention addresses the issues of incomplete focus and imperfect assessments in previous studies, achieving a scientific and objective assessment of flood storage and detention areas and enabling categorized and graded policy implementation. The weight allocation in this invention is not a universally applicable choice, but rather deeply integrated with the dimensional coupling relationship of the assessment objectives (such as the priority difference between flood control and ecology) and the characteristics of newly added indicators customized for the region (such as sedimentation volume indicators for sediment control), thus solving the problem of the disconnect between traditional weighting and regional needs. This invention establishes a corresponding rule for "data quality - indicator characteristics - weighting method" (subjective method for scarce data, objective method for abundant data, and dimensionality reduction before weighting for multiple and related indicators), and supports weight updates based on real-time monitoring data during floods, overcoming the shortcomings of static weighting. The core logic of combined weighting is clearly defined: subjective weights ensure the actual importance of indicators (e.g., priority of emergency response time), while objective weights reflect the inherent patterns of the data (e.g., the dispersion of dike compliance rates). Complementary advantages are achieved through weighted fusion or fuzzy operators, and secondary weighting after dimensionality reduction is supported to solve the problem of interference from multi-indicator correlations. For customized extended indicators (e.g., groundwater recharge rate, flood resource utilization rate), this invention uses a weight coordination mechanism in combined weighting to balance the weight ratio of new indicators with basic indicators, preventing new indicators from being ignored or having excessive weight, and adapting to the assessment needs of different types of flood storage and detention areas. Attached Figure Description

[0101] Figure 1 This is a schematic diagram of the process steps and indicators of a multidimensional resilience assessment method for flood storage and detention areas in Example 1. Detailed Implementation

[0102] The following will describe in detail the multidimensional resilience assessment method for flood storage and detention areas of this patent, with reference to the accompanying drawings.

[0103] Example 1: As Figure 1 As shown, this embodiment provides a multi-dimensional resilience assessment method for flood storage and detention areas, including: determining the assessment target; selecting indicator data for the assessment target through multi-source data collection and regional customized expansion, wherein the indicators are set; assigning weights to the selected indicator data using subjective weighting, objective weighting, or combined weighting methods to obtain a comprehensive score; and classifying the resilience of the flood storage and detention area and outputting a decision based on the comprehensive score and the watershed flood control planning target.

[0104] The weight allocation includes: based on the multidimensional characteristics of the assessment objectives, including flood control safety, ecological restoration, socio-economic factors, smart regulation, and special objectives, combined with a regionally customized and expanded indicator system, constructing a three-dimensional adaptive weight allocation mechanism of "objective-indicator-data"; dynamically selecting appropriate weighting methods based on the availability, quantification level, relevance, and regional type of indicator data, including sediment control, groundwater sensitivity, and water resource utilization; for combined weighting, adopting a differentiated fusion strategy of "subjective weight-objective weight", or first processing highly relevant indicators through dimensionality reduction algorithms before weighting, ensuring that the weight allocation accurately matches the assessment scenario, regional characteristics, and data conditions; the weight allocation results can be updated according to real-time data during flood evolution to achieve dynamic resilience assessment and avoid the limitations of static weights.

[0105] The aforementioned multidimensional resilience assessment method for flood storage and detention areas, wherein the assessment objectives are defined, include: flood control safety, ecological restoration, socio-economic development, intelligent regulation and control, and special objectives.

[0106] The aforementioned multidimensional resilience assessment method for flood storage and detention areas includes specific objectives such as spatial optimization, sediment resource utilization, groundwater recharge, flood resource utilization, cold-region agriculture, and cross-border coordination.

[0107] The aforementioned multidimensional resilience assessment method for flood storage and detention areas includes the following indicator data: flood control indicators, ecological indicators, socio-economic indicators, and smart regulation indicators. The flood control indicators include: flood diversion response time, dike compliance rate, personnel evacuation efficiency, waterlogging receding efficiency, and inundation depth control rate. The ecological indicators include: wetland retention rate, water quality compliance rate, vegetation coverage rate, species diversity index, and fish population recovery rate. The socio-economic indicators include: per capita compensation amount, industrial transformation rate, employment security rate, post-disaster GDP recovery cycle, and medical resource coverage. The smart regulation indicators include: monitoring station density, flood forecast accuracy, early warning system delay, data update frequency, data sharing rate, and digital twin platform integration degree.

[0108] The aforementioned multidimensional resilience assessment method for flood storage and detention areas, wherein the regional customized expansion refers to dynamically adding indicator data according to the type of flood storage and detention area; if the flood storage and detention area type is sediment control type, the corresponding added indicator data includes annual sediment deposition, sediment resource utilization rate, sediment monitoring density, sediment deposition rate, sediment discharge efficiency, and riverbed uplift threshold; if the flood storage and detention area type is groundwater sensitive type, the corresponding added indicator data includes groundwater recharge rate, groundwater level rise, permeability coefficient variability, funnel area reduction, and water quality compliance rate;

[0109] If the flood storage and detention area is classified as a water resource utilization area, the corresponding added indicator data include flood resource utilization rate and irrigation guarantee rate.

[0110] The aforementioned multidimensional resilience assessment method for flood storage and detention areas involves using subjective weighting, objective weighting, or a combination of weighting methods to assign weights to selected indicator data to obtain a comprehensive score. The subjective weighting methods include: analytic hierarchy process (AHP), Delphi method, and fuzzy AHP. The objective weighting methods include: entropy method, principal component analysis, coefficient of variation method, and data envelopment analysis. The combination analysis methods include: subjective-objective weighted combination, CRITIC method, and fuzzy combination method.

[0111] The aforementioned multidimensional resilience assessment method for flood storage and detention areas uses the objective weighting method to determine the weight allocation through the statistical characteristics of the indicator data; and the combined analysis method improves the rationality of the weight allocation by integrating the advantages of the two methods.

[0112] The aforementioned multidimensional resilience assessment method for flood storage and detention areas includes the following selection methods for subjective weighting, objective weighting, or combined weighting: if data is scarce, such as newly built flood storage and detention areas lacking long-term monitoring data, the Delphi method or analytic hierarchy process (AHP) should be used first; if data is sufficient and indicators are easily quantifiable, such as flood storage and detention areas that have been operating for many years, the entropy method or CRITIC method should be used first; if a balance between scientific rigor and practicality is required, the AHP-entropy method combination should be used first; if there are many indicators and they are highly correlated, the dimensionality should be reduced first using principal component analysis, and then the final weights should be determined by combining subjective methods.

[0113] The core formula of the Delphi method is as follows:

[0114] (1) Initial value of single indicator weight

[0115]

[0116] in, : represents the score given by the i-th expert to the j-th indicator in the k-th round, where n is the total number of indicators;

[0117] (2) Final weight

[0118]

[0119] Where m is the number of experts participating in the final round, and k ∗ To converge the rounds, w ij Let be the final weight of the j-th indicator;

[0120] The core formula of the Analytic Hierarchy Process (AHP) is as follows:

[0121] (1) Judgment matrix construction and consistency test

[0122] Let the judgment matrix be... ; where a pq Let p be the importance ratio of indicators p to q, satisfying a pq =1 / aqp The core formula is as follows:

[0123] (2) Weight calculation

[0124] p=1,2,3…n

[0125]

[0126] (3) Consistency check

[0127]

[0128]

[0129] Where λmax is the largest eigenvalue of the judgment matrix, RI is the average random consistency index, n=1-10 has a standard value, and the matrix satisfies consistency when CR<0.1;

[0130] The core formula of the entropy method is as follows:

[0131] (1) Indicator standardization

[0132] Positive indicators:

[0133] Negative indicators:

[0134] Where, x ij For the original value of the j-th indicator of the i-th sample, max(x) j min(x) j ) represents the extreme value of the j-th index;

[0135] (2) Indicator weight

[0136]

[0137] Where ε is a minimum value to avoid the denominator being 0, and is usually taken as 0.0001, and m is the number of samples;

[0138] (3) Entropy and weight

[0139]

[0140]

[0141] Among them, e j The entropy value of the j-th index (0≤e) j ≤1), 1−e j The coefficient of variation;

[0142] The core formula of the CRITIC method is as follows:

[0143] (1) Standardization of indicators, positive / negative standardization formulas of the same entropy value method;

[0144] (2) Calculation of information content

[0145]

[0146] in, The j-th indicator is the standardized mean.

[0147] (3) Conflict calculation

[0148]

[0149]

[0150] Where, r jk Let C be the correlation coefficient between index j and k. j The conflict of index j;

[0151] (4) Overall weight

[0152]

[0153] The AHP-entropy method combined weighting formula is as follows:

[0154] (1) Normalization of subjective and objective weights

[0155] Let the subjective weight of AHP be... The objective weight of the entropy method is The formula is as follows:

[0156]

[0157]

[0158] (2) Combination weights

[0159]

[0160] in, This is a subjective weighting coefficient, typically set to 0.5, but can be adjusted to 0.3-0.7 depending on scientific and practical requirements. For combined weights;

[0161] The formula for principal component analysis dimensionality reduction combined with subjective method is as follows:

[0162] (1) Core formula for principal component dimensionality reduction

[0163] Covariance matrix:

[0164]

[0165] Eigenvalues ​​and eigenvectors:

[0166]

[0167] Where λ is the eigenvalue and ξ is the corresponding eigenvector;

[0168] Principal component extraction: Select the first k eigenvalues ​​λ1≥λ2≥...≥λ k With a cumulative variance contribution rate ≥ 85%, principal components are constructed.

[0169]

[0170] (2) Determine the final weight by combining subjective methods

[0171] =Weight of the t-th principal component calculated by AHP method

[0172] ·

[0173] Where, ξ tj w is the eigenvector component corresponding to the original index j of the t-th principal component. j The final weights of the original indicators.

[0174] The aforementioned multidimensional resilience assessment method for flood storage and detention areas includes resilience grading levels of: high resilience, medium-high resilience, medium resilience, medium-low resilience, and low resilience; the resilience grading is used to provide a basis for management decisions.

[0175] The aforementioned multidimensional resilience assessment method for flood storage and detention areas does not employ a universally applicable weight allocation. Instead, it is deeply coupled with the dimensions of the assessment objectives, such as the priority differences between flood control and ecology, and the characteristics of newly added indicators customized for the region, such as sedimentation volume indicators for sediment control. This approach aims to address the problem of the disconnect between traditional weighting and regional needs.

[0176] The aforementioned multidimensional resilience assessment method for flood storage and detention areas refers to the decision output, which involves developing targeted measures for flood storage and detention areas of different levels, including planning and construction, emergency management, and long-term improvement. The specific steps are as follows:

[0177] (1) Combining the comprehensive score range and characteristic description corresponding to the resilience level of the flood storage and detention area, and the core indicators after regional customization and expansion, such as sediment discharge efficiency for sediment control type and groundwater recharge rate for groundwater sensitive type, the weak dimensions and key shortcomings of the system are located.

[0178] (2) The targeted measures are broken down into layers of “short-term emergency response - medium-term construction - long-term improvement”. The short-term focus is on risk prevention and emergency response, the medium-term focus is on improving core functions, and the long-term focus is on sustainable adaptation and synergistic optimization.

[0179] (3) Match corresponding technologies or management measures to the shortcomings indicators, including upgrading engineering facilities and restoring the ecosystem at the planning and construction level, optimizing response processes and ensuring resources at the emergency management level, and focusing on industrial transformation and mechanism innovation at the long-term improvement level;

[0180] (4) Based on the weight ratio of indicators, the cost of implementing measures, and the expected disaster reduction benefits, the priority of implementing measures is quantified and ranked, and low-cost, high-return measures corresponding to high-weight shortcomings are given priority.

[0181] (5) Establish a mechanism for tracking the implementation effect of measures, update indicator data and resilience scores based on subsequent monitoring data, and iteratively optimize targeted measures in a synchronous manner to achieve dynamic adaptation of decision output to the actual state of flood storage and detention areas.

[0182] Example 2: Taking the Yongding River flood storage and detention area in the Beijing-Tianjin-Hebei region as an example, the multidimensional resilience assessment method for flood storage and detention areas of the present invention is applied.

[0183] Regional Background: The Yongding River flood storage and detention area in the Beijing-Tianjin-Hebei region is a core component of the Haihe River Basin flood control system. Spanning eight districts and counties including Fangshan District in Beijing, Langfang District in Hebei, and Wuqing District in Tianjin, it encompasses the floodplains of the Yongding River main stream, the flood storage and detention areas of the Daqing River tributaries, and related wetlands, covering a total area of ​​1260 km², serving a resident population of 483,000 and 652,000 mu of cultivated land. The region has a temperate monsoon climate with an average annual precipitation of 580 mm, 75% of which is concentrated in the flood season from June to September. Historically, it has experienced catastrophic floods in 1939 and 1963, resulting in significant flood control pressure. Simultaneously, the region is located in the core area of ​​the North China Plain groundwater funnel zone, with a multi-year average groundwater level depth of 15-25 m. It also serves multiple functions, including ecological corridor restoration in the Beijing-Tianjin-Hebei region and inter-regional water resource allocation, making it a typical composite flood storage and detention area combining groundwater sensitivity and water resource utilization.

[0184] The specific steps are as follows:

[0185] 1. Define the assessment objectives

[0186] Based on regional characteristics and planning requirements, a two-tiered evaluation system of "core objectives + special objectives" is constructed:

[0187] Core Objectives

[0188] Flood control safety: Ensure the orderly storage and retention of floodwaters during the flood season to reduce casualties and property losses;

[0189] Ecological restoration: enhances the stability of wetland ecosystems and improves the quality of watershed water environment;

[0190] Socioeconomic: Enhance regional disaster resilience and post-disaster recovery capabilities, and safeguard people's livelihoods and industrial stability;

[0191] Intelligent regulation and control: Enhance the ability to integrate multi-source data and coordinate scheduling across regions.

[0192] Special Targets

[0193] Groundwater recharge: Targeting the funnel-shaped area of ​​the North China Plain, improve the efficiency of flood infiltration and groundwater recharge;

[0194] Flood resource utilization: realizing the transformation of floodwaters during the flood season into water use for production and ecology;

[0195] Cross-regional coordination: Ensuring coordinated adaptation of flood control, ecological, and water use needs among Beijing, Tianjin, and Hebei.

[0196] 2. Selection of indicator data (multi-source data collection + regional customized expansion)

[0197] (1) Collection of basic indicators (based on multi-source data)

[0198] Fourteen basic indicators were obtained through multi-source data integration. The data sources include the Yongding River Management Bureau monitoring system, the Beijing-Tianjin-Hebei Water Resources Statistical Annual Report (2023), the Ministry of Ecology and Environment's basin monitoring data, and local statistical bulletins. The specific indicators and values ​​are as follows:

[0199] Table 5. Basic Indicator Collection (Data Sources and Specific Values)

[0200]

[0201] (2) Regional customized extended indicators (adapted to Yongding River type)

[0202] Combining the characteristics of the "groundwater sensitive + water resource utilization" composite type, six new extended indicators have been added:

[0203] Table 6 Regional Customization Expansion Indicators

[0204]

[0205] 3. Weight Allocation (AHP-Entropy Method Combination Weighting)

[0206] (1) Selection of weighting method

[0207] The Yongding River flood storage and detention area in the Beijing-Tianjin-Hebei region has been in operation for over 60 years, with continuous and complete monitoring data (sample size ≥ 15 years). The indicators cover multiple dimensions, including engineering, ecology, society, and intelligent regulation. It is necessary to simultaneously consider the importance of core indicators based on expert experience (such as flood control safety and groundwater recharge) and the objective laws of the data. Therefore, the AHP-entropy method is used for weighting, with the subjective weight coefficient α set to 0.5 (to balance scientificity and practicality).

[0208] (2) Subjective weight calculation (AHP method)

[0209] (1) Expert composition and judgment matrix construction

[0210] Twelve experts (including one academician, five professor-level senior engineers, and six senior engineers from local water conservancy departments) in the fields of water conservancy engineering, ecological restoration, regional water resources management, and emergency management were invited to conduct pairwise importance comparisons of five criteria layers ("flood control safety, ecological restoration, socio-economic development, intelligent regulation, and special objectives") and their 20 subordinate indicator layers, constructing a judgment matrix (taking the criteria layer as an example):

[0211]

[0212] (2) Consistency check

[0213] The largest eigenvalue λmax is calculated to be 5.13 using the root method. The consistency index CI is calculated to be (5.13-5) / (5-1)=0.0325. The average random consistency index RI is calculated to be 1.12 (n=5). The consistency ratio CR=CI / RI=0.029<0.1, which meets the consistency requirements.

[0214] (3) Subjective weighting results (criteria layer + core indicator layer)

[0215]

[0216] (3) Calculation of objective weights (entropy method)

[0217] The 20 indicators (14 basic and 6 extended) were standardized in a positive / negative manner.

[0218] Positive indicators (such as dike compliance rate, groundwater recharge rate): Formula 7 is used.

[0219] Negative indicators (such as post-disaster GDP recovery period, early warning system delay): Formula 8 is used.

[0220] Entropy and difference coefficient calculation:

[0221]

[0222] Objective weighting results:

[0223]

[0224] (4) Combination weight fusion

[0225] Using formulas 16-18, with the subjective weight coefficient α=0.5, the final weight is obtained through fusion:

[0226] Groundwater recharge rate: 0.085 (0.5×0.075 + 0.5×0.095)

[0227] Flood diversion response time: 0.088 (0.5 × 0.085 + 0.5 × 0.092)

[0228] Flood resource utilization rate: 0.077 (0.5×0.063 + 0.5×0.090)

[0229] The compliance rate of the dikes is 0.083 (0.5×0.078 + 0.5×0.087).

[0230] Water quality compliance rate: 0.070 (0.5×0.062 + 0.5×0.078)

[0231] (5) Calculation of comprehensive score

[0232] The overall score is calculated as Σ (standardized index value × combined weight), and the final calculated overall resilience value of the Yongding River flood storage and detention area is 0.736.

[0233] 4. Resilience Classification and Decision Output

[0234] (1) Toughness grading

[0235] Based on the comprehensive score range [0.6, 0.8), the flood storage and detention area is judged to be of medium to high resilience. Its characteristics are described as follows: the core flood control function is stable, and the intelligent regulation and socio-economic resilience are good. However, there are still shortcomings in groundwater recharge efficiency and ecosystem integrity, and the cross-regional coordination mechanism needs to be further optimized.

[0236] (2) Targeted decision output

[0237] (1) Short-term emergency response (within 1 year): Focus on risk prevention and control

[0238] Optimize the flood diversion response process: Based on the digital twin platform, integrate monitoring data from Beijing, Tianjin and Hebei to reduce the flood diversion response time to within 35 minutes;

[0239] Strengthen seepage prevention in key areas: Implement seepage prevention and reinforcement projects on 32km of dikes in Fangshan and Wuqing sections to improve the dike compliance rate to over 95%;

[0240] Improve the relocation and assistance mechanism: Establish a "one-to-one" emergency relocation ledger for the elderly, the infirm and the disabled, increasing the efficiency of personnel relocation to over 96%.

[0241] (2) Medium-term construction (1-3 years): Addressing core weaknesses

[0242] Groundwater recharge project: Three new ecological infiltration sites were added in the Yongding River floodplain, along with a 12km flood diversion and infiltration channel, increasing the groundwater recharge rate to 22%.

[0243] Wetland ecological restoration: The wetland area downstream of Guanting Reservoir was expanded by 4.5 km², and a composite wetland system of "flood storage + purification + infiltration" was constructed, increasing the wetland retention rate to 85%.

[0244] Water resource allocation optimization: A unified flood control mechanism for the Beijing-Tianjin-Hebei region during the flood season has been established, increasing the flood resource utilization rate to 30% and ensuring irrigation coverage at over 90%.

[0245] (3) Long-term improvement (3-5 years): Achieve synergistic optimization

[0246] Industrial transformation and upgrading: guide high-risk industries in low-lying areas to transform into ecotourism and green agriculture, and increase the industrial transformation rate to over 55%;

[0247] Smart platform upgrade: Improve the digital twin platform, integrate groundwater dynamic monitoring and ecological flow regulation modules, and increase the platform integration rate to 85%;

[0248] Cross-regional mechanism innovation: Establish an ecological compensation fund for flood storage and detention areas in the Beijing-Tianjin-Hebei region to achieve a balanced distribution of flood control, ecological and water use benefits across regions.

[0249] Implementation effect

[0250] (1) Quantitative benefit prediction

[0251] Enhanced resilience: After implementing the above measures, the overall resilience value is expected to increase to 0.81 in 2027, reaching a high resilience level;

[0252] Disaster losses reduced: The number of people affected by floods decreased by 105,000, and direct economic losses decreased by more than 40%;

[0253] Ecological benefits: The area of ​​the groundwater funnel zone shrinks by an average of 15% per year, the groundwater level rises by an average of 0.5m per year, and the water quality compliance rate of the basin remains stable at over 90%.

[0254] Economic benefits: Flood resource utilization saves 230 million yuan in water resource costs annually, and increases ecotourism revenue by 25% annually, directly supporting the implementation of the "Second Phase Plan for Comprehensive Management and Ecological Restoration of the Yongding River".

[0255] (2) Method suitability verification

[0256] This embodiment uses regionally customized extended indicators and combined weighting methods to accurately match the "groundwater sensitive + water resource utilization" characteristics of the Yongding River, shortening the assessment cycle from the traditional 12 days to 5 hours. This verifies the regional adaptability, dynamism, and decision-making targeting of the assessment method, and can be extended to the resilience assessment and governance practices of similar flood storage and detention areas in the North China Plain.

Claims

1. A multidimensional resilience assessment method for flood storage and detention areas, characterized in that, include: Define the evaluation objectives; The evaluation target is selected by means of multi-source data collection and regional customized expansion, and the indicators are set; the selected indicator data are weighted by subjective weighting method, objective weighting method or combined weighting method to obtain a comprehensive score; Based on the comprehensive score and the watershed flood control planning objectives, the resilience classification and output decision of flood storage and detention areas are carried out; The weight allocation includes: based on the multidimensional characteristics of the assessment objectives, including flood control safety, ecological restoration, socio-economic factors, smart regulation, and special objectives, combined with a regionally customized and expanded indicator system, constructing a three-dimensional adaptive weight allocation mechanism of "objective-indicator-data"; dynamically selecting appropriate weighting methods based on the availability, quantification level, relevance, and regional type of indicator data, including sediment control, groundwater sensitivity, and water resource utilization; for combined weighting, adopting a differentiated fusion strategy of "subjective weight-objective weight", or first processing highly relevant indicators through dimensionality reduction algorithms before weighting, ensuring that the weight allocation accurately matches the assessment scenario, regional characteristics, and data conditions; the weight allocation results can be updated according to real-time data during flood evolution to achieve dynamic resilience assessment and avoid the limitations of static weights.

2. The multidimensional resilience assessment method for flood storage and detention areas according to claim 1, characterized in that, The assessment objectives are defined, including: flood control safety, ecological restoration, socio-economic aspects, intelligent regulation, and special objectives.

3. The multidimensional resilience assessment method for flood storage and detention areas according to claim 2, characterized in that, The specific objectives include: spatial optimization, sediment resource utilization, groundwater recharge, flood resource utilization, cold-region agriculture, and cross-border coordination.

4. The multidimensional resilience assessment method for flood storage and detention areas according to claim 1, characterized in that, The indicator data includes: flood control indicators, ecological indicators, socio-economic indicators, and intelligent regulation indicators; The flood control indicators include: flood diversion response time, dike compliance rate, personnel evacuation efficiency, waterlogging drainage efficiency, and inundation depth control rate. The ecological indicators include: wetland retention rate, water quality compliance rate, vegetation coverage rate, species diversity index, and fish population recovery rate. The socio-economic indicators include: per capita compensation amount, industrial transformation rate, employment security rate, post-disaster GDP recovery period, and medical resource coverage. The intelligent control indicators include: monitoring station density, flood forecast accuracy, early warning system delay, data update frequency, data sharing rate, and digital twin platform integration.

5. The multidimensional resilience assessment method for flood storage and detention areas according to claim 1, characterized in that, The aforementioned regional customized expansion refers to dynamically adding indicator data based on the type of flood storage and detention area; If the flood storage and detention area is of the sediment control type, the corresponding added indicator data include annual sediment deposition, sediment resource utilization rate, sediment monitoring density, sediment deposition rate, sediment discharge efficiency, and riverbed uplift threshold. If the flood storage and detention area is a groundwater sensitive type, the corresponding added indicator data include groundwater recharge rate, groundwater level rise, permeability coefficient variability, funnel area reduction, and water quality compliance rate. If the flood storage and detention area is classified as a water resource utilization area, the corresponding added indicator data include flood resource utilization rate and irrigation guarantee rate.

6. The multidimensional resilience assessment method for flood storage and detention areas according to claim 1, characterized in that, The selected indicator data are weighted using subjective weighting, objective weighting, or a combination of weighting methods to obtain a comprehensive score. The subjective weighting methods include: Analytic Hierarchy Process (AHP), Delphi method, and Fuzzy AHP. The objective weighting methods include: entropy method, principal component analysis method, coefficient of variation method, and data envelopment analysis method; The combined analysis methods include: subjective-objective weighted combination, CRITIC method, and fuzzy combination method.

7. The multidimensional resilience assessment method for flood storage and detention areas according to claim 6, characterized in that, The objective weighting method determines the allocation of weights based on the statistical characteristics of the indicator data; the combined analysis method improves the rationality of weight allocation by integrating the advantages of the two methods.

8. The multidimensional resilience assessment method for flood storage and detention areas according to claim 6, characterized in that, The selection methods for subjective weighting, objective weighting, or combined weighting include: if data is scarce, such as newly built flood storage and detention areas lacking long-term monitoring data, the Delphi method or analytic hierarchy process (AHP) should be used first; if data is sufficient and indicators are easy to quantify, such as flood storage and detention areas that have been operating for many years, the entropy method or CRITIC method should be used first; if a balance between scientific rigor and practicality is required, the combination of AHP and entropy method should be used first; if there are many indicators and they are highly correlated, the dimensionality should be reduced first by principal component analysis, and then the final weights should be determined by combining subjective methods. The core formula of the Delphi method is as follows: (1) Initial values ​​of single indicator weights; ; in, : represents the score given by the i-th expert to the j-th indicator in the k-th round, where n is the total number of indicators; (2) Final weight; ; Where m is the number of experts participating in the final round, and k ∗ To converge the rounds, w ij Let be the final weight of the j-th indicator; The core formula of the Analytic Hierarchy Process (AHP) is as follows: (1) Determine the matrix construction and consistency check; Let the judgment matrix be... ; where a pq Let p be the importance ratio of indicators p to q, satisfying a pq =1 / a qp The core formula is as follows: (2) Weight calculation; ,p=1,2,3………..n ; ; (3) Consistency check; ; ; Where λmax is the largest eigenvalue of the judgment matrix, RI is the average random consistency index, n=1-10 has a standard value, and the matrix satisfies consistency when CR<0.1; The core formula of the entropy method is as follows: (1) Indicator standardization; Positive indicators: ; Negative indicators: ; Where, x ij For the original value of the j-th indicator of the i-th sample, max(x) j min(x) j ) represents the extreme value of the j-th index; (2) Indicator weighting; ; Where ε is a minimum value to avoid the denominator being 0, and is usually taken as 0.0001, and m is the number of samples; (3) Entropy and weight; ; ; Among them, e j The entropy value of the j-th index (0≤e) j ≤1), 1−e j The coefficient of variation; The core formula of the CRITIC method is as follows: (1) Standardization of indicators, positive / negative standardization formulas of the same entropy value method; (2) Information content calculation; ; in, The j-th indicator is the standardized mean. (3) Conflict calculation; ; ; Where, r jk Let C be the correlation coefficient between index j and k. j The conflict of index j; (4) Overall weight; ; The AHP-entropy method combined weighting formula is as follows: (1) Normalization of subjective and objective weights; Let the subjective weight of AHP be... The objective weight of the entropy method is The formula is as follows: ; ; (2) Combined weights; ; in, This is a subjective weighting coefficient, typically set to 0.5, but can be adjusted to 0.3-0.7 depending on scientific and practical requirements. For combined weights; The formula for principal component analysis dimensionality reduction combined with subjective method is as follows: (1) Core formula for principal component dimensionality reduction; Covariance matrix: ; Eigenvalues ​​and eigenvectors: ; Where λ is the eigenvalue and ξ is the corresponding eigenvector; Principal component extraction: Select the first k eigenvalues ​​λ1≥λ2≥...≥λ k If the cumulative variance contribution rate is ≥85%, principal components are constructed. ; (2) Determine the final weights using a combination of subjective methods; =Weight of the t-th principal component calculated by AHP method · ; Where, ξ tj w is the eigenvector component corresponding to the original index j of the t-th principal component. j The final weights of the original indicators.

9. The multidimensional resilience assessment method for flood storage and detention areas according to claim 1, characterized in that, The resilience grading levels include: high resilience, medium-high resilience, medium resilience, medium-low resilience, and low resilience; the resilience grading is used to provide a basis for management decisions.

10. The multidimensional resilience assessment method for flood storage and detention areas according to claim 1, characterized in that, The decision-making output refers to the development of targeted measures for flood storage and detention areas at different levels, including planning and construction, emergency management, and long-term improvement. The specific steps are as follows: (1) Combining the comprehensive score range and characteristic description corresponding to the resilience level of the flood storage and detention area, and the core indicators after regional customization and expansion, such as sediment discharge efficiency for sediment control type and groundwater recharge rate for groundwater sensitive type, the weak dimensions and key shortcomings of the system are located. (2) The targeted measures are broken down into layers of "short-term emergency response - medium-term construction - long-term improvement". The short-term focus is on risk prevention and emergency response, the medium-term focus is on improving core functions, and the long-term focus is on sustainable adaptation and synergistic optimization. (3) Match corresponding technologies or management measures to the shortcomings indicators, including upgrading engineering facilities and restoring the ecosystem at the planning and construction level, optimizing response processes and ensuring resources at the emergency management level, and focusing on industrial transformation and mechanism innovation at the long-term improvement level; (4) Based on the weight ratio of indicators, the cost of implementing measures, and the expected disaster reduction benefits, the priority of implementing measures is quantified and ranked, and low-cost, high-return measures corresponding to high-weight shortcomings are given priority. (5) Establish a mechanism for tracking the implementation effect of measures, update indicator data and resilience scores based on subsequent monitoring data, and iteratively optimize targeted measures in a synchronous manner to achieve dynamic adaptation of decision output to the actual state of flood storage and detention areas.

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