A method for urban waterlogging resilience assessment based on multi-agent and system dynamics coupled modeling

Abstract

The application discloses a kind of urban waterlogging resilience evaluation methods based on multi-agent and system dynamics coupling modeling, applied to urban water, disaster prevention and mitigation and emergency research technical field, including: obtaining the parameter value of each item of urban waterlogging formation factor, determine subject object, and according to attribute characteristics, the mathematical expression of abstract behavior is obtained, and the behavior decision result is obtained;Confirm the positive and negative feedback relationship between each index of system dynamics model, and apply the behavior decision result to the model, carry out the interactive simulation of micro decision and macro phenomenon, obtain the urban waterlogging resilience evaluation coupling model;Wherein, system dynamics model includes: social resilience subsystem, economic resilience subsystem, facility resilience subsystem, environmental resilience subsystem;Based on urban waterlogging resilience evaluation coupling model, different scene policies are designed to explore the best management scheme for urban waterlogging resilience construction.

Description

Technical Field

[0001] This invention relates to the fields of urban water management, disaster prevention and mitigation, and emergency research technology, and in particular to an urban waterlogging resilience assessment method based on multi-agent and system dynamic coupling modeling. Background Technology

[0002] Against the backdrop of global warming, extreme weather events are becoming more frequent. The operation of cities generates significant amounts of domestic and industrial heat, causing atmospheric instability and leading to urban heat island and rain island effects, increasing the uncertainty and likelihood of urban storm events. With urbanization and the continuous expansion of urban areas, the risk of urban flooding is constantly increasing. Therefore, strengthening urban resilience to disaster prevention and risk reduction is a crucial current objective.

[0003] Urban flood resilience reflects a city's ability to adapt to and recover from flooding disasters. In other words, it refers to the ability of an urban system to maintain basic vitality, minimize losses, and rapidly adapt, recover, and develop when impacted by flooding. Urban flooding is characterized by short duration and spatial distribution, and is a phenomenon caused by multiple factors, including human activities and the physical environment. Human activities include social and economic factors such as emergency management measures for flooding events and investment in urban maintenance funds. The physical environment includes natural conditions and urban infrastructure conditions such as heavy rainfall events, land cover type, drainage network construction, and building density. The stronger a city's adaptability and recovery capacity to flooding events, the higher its level of flood resilience. Reducing the probability of urban flooding and minimizing the losses caused by flooding are the main objectives of urban flood resilience construction. Currently, research on improving urban flood resilience both domestically and internationally encompasses two aspects: research on non-engineering measures, such as enhancing government joint prevention and control technologies and improving residents' risk and disaster perception capabilities; and research on engineering measures, such as low-impact development technologies, water-sensitive urban design, and sponge city construction technologies, which utilize methods like increasing drainage networks and constructing rain gardens, along with methods such as hydrodynamic models, GIS technology, and linear weighted combination mapping to create hazard and susceptibility maps. By assessing the level of urban flood resilience, weaknesses in urban flood disaster response can be identified, providing a scientific basis for policy formulation. This, in turn, improves the city's disaster adaptability and recovery capabilities, helps cities rationally plan and optimize resource allocation, and build high-standard, resilient urban systems.

[0004] Therefore, how to provide a method for urban flooding resilience assessment based on multi-agent and system dynamics coupling modeling that can comprehensively consider the influencing factors of urban flooding and the causal relationships between these factors, achieve scientific and refined urban flooding resilience assessment, accurately identify the weak links in urban flooding disaster response, provide a scientific basis for policy formulation, and thus improve the city's disaster adaptability and recovery capacity, help the city to rationally plan and optimize resource allocation, and build a high-standard and resilient urban system is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, this invention proposes an urban flood resilience assessment method based on multi-agent and system dynamics coupled modeling. From a combined micro and macro perspective, it evaluates the level of urban flood resilience construction, and by setting different policy scenarios, explores the key factors and weak links affecting urban flood adaptability and recovery capacity, providing government decision-makers with more refined urban flood resilience construction and management plans.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for assessing urban flood resilience based on multi-agent and system dynamic coupling modeling includes:

[0008] Step 1: Obtain the parameter values ​​of various factors contributing to urban flooding, determine the main object of urban flooding resilience construction, and abstract the mathematical expression of behavior based on the attribute characteristics of the main object to obtain the behavioral decision results of the main object;

[0009] Step 2: Confirm the positive and negative feedback relationships among the various indicators in the system dynamics model of urban flood resilience construction level, and apply the behavioral decision-making results to the system dynamics model to conduct interactive simulation of micro-decision and macro-phenomenon, so as to obtain the coupled model of urban flood resilience assessment; wherein, the system dynamics model includes: social resilience subsystem, economic resilience subsystem, infrastructure resilience subsystem, and environmental resilience subsystem;

[0010] Step 3: Conduct sensitivity tests on the urban flood resilience assessment coupling model to identify key factors affecting urban flood resilience construction. By designing different scenario policies and analyzing the assessment results of different scenarios, explore the optimal management scheme affecting urban flood resilience construction.

[0011] Optionally, in step 1, the abstract behavior is expressed mathematically based on the attribute characteristics of the main object, specifically as follows:

[0012] The subjects are classified according to their attribute characteristics, and the initial state and behavior rules of the subjects in different attribute categories are determined in turn. Decision statistical analysis of the subjects is carried out to obtain simulation results based on multiple subjects.

[0013] Optionally, in step 2, the positive and negative feedback relationships among the various indicators in the system dynamics model of urban flood resilience are confirmed, and the behavioral decision-making results are applied to the system dynamics model to conduct an interactive simulation of micro-decision-making and macro-phenomena, specifically:

[0014] The positive and negative feedback relationships between different subsystem models in the system dynamics model and the mathematical function relationships of each indicator factor are determined. The simulation results of multiple agents are set into the mathematical functions related to the factors in the system dynamics model. The model optimization is verified based on historical data. The simulation results based on the system dynamics model are set into the relevant behavioral states after the subject object attribute classification, so as to realize the interactive simulation of micro-decision and macro-phenomenon.

[0015] Optionally, in step 3, a sensitivity test is performed on the urban flood resilience assessment coupling model to identify the key factors affecting urban flood resilience construction, specifically:

[0016] By adjusting the behavior rules of the main object and the parameters of the subsystem model in the system dynamics model, the future changes of urban waterlogging resilience construction are predicted, and the important factors affecting urban waterlogging resilience construction are analyzed.

[0017] Optionally, in step 3, different scenario policies are designed, and the evaluation results of different scenarios are analyzed to explore the optimal management scheme affecting urban flood resilience construction. Specifically:

[0018] Based on the existing parameter values, predict the trend of urban flooding resilience changes;

[0019] Based on existing historical data, by modifying the parameter values ​​in mathematical functions or modifying behavioral designs to set different scenario policies, we can compare and analyze the changing trends of urban flooding resilience under different scenarios and make suggestions for improving urban flooding resilience.

[0020] Based on changing trends and recommendations, practical measures should be taken to improve urban flood resilience, thereby reducing economic losses caused by flooding and enhancing the city's resilience level.

[0021] As can be seen from the above technical solution, compared with the prior art, this invention proposes an urban flooding resilience assessment method based on multi-agent and system dynamics coupled modeling. Through coupled modeling based on the micro-level dynamic evolution of multiple agents and the macro-level dynamic evolution based on the system dynamics model, the urban flooding resilience assessment can comprehensively consider the influencing factors of urban flooding and the causal relationships between these factors. This achieves a scientific and refined assessment of urban flooding resilience, accurately identifies the weak links in urban flooding disaster response, provides a scientific basis for policy formulation, thereby improving the city's disaster adaptability and recovery capacity, helping cities to rationally plan and optimize resource allocation, and build high-standard, resilient urban systems. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0024] Figure 2 A schematic diagram illustrating the coupling model of this invention.

[0025] Figure 3 This is a schematic diagram of the urban flooding resilience coupling model of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Example 1:

[0028] Embodiment 1 of this invention discloses a method for assessing urban flood resilience based on multi-agent and system dynamic coupling modeling, such as... Figure 1 As shown, it includes:

[0029] Step 1: Obtain the parameter values ​​of various factors contributing to urban flooding, determine the main object of urban flooding resilience construction, and abstract the mathematical expression of behavior based on the attribute characteristics of the main object to obtain the behavioral decision results of the main object.

[0030] Multi-agent modeling, also known as Agent-Based Modeling (ABM), is a computational model entirely based on the behavior of individuals at the micro-level. At the micro-level, agents with different attributes and behaviors are used as agents, and their behaviors are designed under different conditions to simulate the decision-making of micro-agents and the resulting group phenomena. Through the behavioral interactions between agents at the micro-level, complex phenomena at the macro-level emerge intuitively. This method has been applied to multiple fields, such as urban solid waste management, transportation, infectious disease spread, and population evacuation in almost all types of disasters, to achieve long-term planning and management or real-time emergency management measures.

[0031] To address urban flooding, this study investigates key factors influencing urban flood resilience at the micro-level using multi-agent modeling. Real-time emergency management of urban flooding is modeled using the flooding event period as the simulation timeframe. A model framework based on residents with different vulnerability attributes and evacuation routes with varying degrees of congestion provides support for better emergency evacuation management in urban communities. By setting different proportions of decision-makers, evacuation response speeds, and shelter capacity, the study examines the cascading impact mechanism of flooding disasters on population behavior characteristics and urban emergency evacuation, providing a basis for decision-making in urban flooding emergency response. Given the varying attitudes and response times people have to disaster warnings, an agent-based modeling framework was developed to evaluate the impact of residents' behavioral responses to warnings and residential density on the effectiveness of warnings. The results show that the effectiveness of disaster warnings is significantly limited in high-density residential areas, highlighting the importance of considering human behavioral heterogeneity when simulating warning response systems.

[0032] Using an annual time step allows for the study of long-term planning measures to alleviate urban flooding. Modeling with different attributes of government and households as subjects can simulate proactive government responses to disasters and rational household flood control measures, demonstrating a significant effect on reducing annual flood losses. Simulation modeling with other participants can support government collaboration in addressing flooding issues. Using insurance companies as subjects, and based on the impact mechanism of disaster insurance policies on flood risk, can design adjustments to insurance premiums and coverage, analyzing optimal insurance policies in flood disaster management and providing crucial support for local governments to mitigate disaster losses. Using real estate developers as subjects, can simulate the migration and relocation behavior of households with different income levels and attitudes towards disasters, combining the number of households inside and outside flood zones under different scenarios to calculate the utility value of housing, providing developers with insights for rationally adjusting housing prices in different risk areas.

[0033] Disaster-affected residents, government departments such as the Ministry of Emergency Management, the Ministry of Transport, and the National Health Commission, as well as other relevant participants such as online media and enterprises, can all be considered as micro-agents in urban flooding simulation modeling. Starting from the perspective of flood management managers or participants, changing behaviors can greatly contribute to the efficient implementation of flood management policies, reduce losses caused by flooding, and improve urban flood resilience.

[0034] The mathematical expression of abstract behavior based on the attribute characteristics of the subject object is as follows:

[0035] The subjects are classified according to their attribute characteristics, and the initial state and behavior rules of the subjects in different attribute categories are determined in turn. Decision statistical analysis of the subjects is carried out to obtain simulation results based on multiple subjects.

[0036] Step 2: Confirm the positive and negative feedback relationships among the various indicators in the system dynamics model of urban flood resilience construction level, and apply the behavioral decision-making results to the system dynamics model to conduct interactive simulation of micro-decision and macro-phenomenon, so as to obtain the urban flood resilience assessment coupling model; wherein, the system dynamics model includes: social resilience subsystem, economic resilience subsystem, infrastructure resilience subsystem, and environmental resilience subsystem.

[0037] System Dynamics Model (SDM) consists of inventory, flow, variables, and feedback loops. Based on feedback control theory, it was established by Forrester of MIT in 1956 and has become an independent and complete discipline. This method combines the control principle of information feedback with the logic of causality to establish a simulation model of the system. The factors have indirect influences, not simple linear relationships. This model is often used to deal with complex system problems at the macroscopic level, exploring the dynamic evolution process of systems, such as urban resource management, environmental issues, regional economic evolution, population forecasting, and land use type evolution.

[0038] Given the complexity of urban flooding, system dynamics models can quantitatively simulate and assess trends in urban flooding at the macro level. Based on SLR (Systemic Lack of Reference Scale) analysis, this study extracted indicators for the urban flooding disaster risk system from literature review, establishing a system structure for urban flooding disasters consisting of four parts: disaster-causing factors, disaster-inducing environment, disaster-bearing bodies, and disaster prevention and mitigation. By combining this with multi-agent system simulation framework theory, a dynamic model of urban stormwater and flood resilience system considering different rainstorm scenarios was established. This model quantitatively analyzes the complex structure and dynamic behavior of the urban stormwater and flood resilience system, providing a reference for the construction of resilient cities.

[0039] The system dynamics model for studying urban flooding considers a comprehensive range of indicators and studies the causal relationships between these indicators, forming a positive and negative feedback structure. This makes the macroscopic prediction of the evolution process more scientific and highly credible.

[0040] Multi-agent modeling (MAM) and system dynamics models are both classic simulation modeling techniques. However, MAM focuses on modeling micro-level agents, while system dynamics models are primarily used to explore dynamic evolution at the macro-level. ABM offers advantages such as good scalability, flexibility, and convenient interaction, while SDM features clear factor relationships and nonlinearity. Combining the micro and macro levels allows for a deeper exploration of the weak links in systems engineering problems, contributing to the development of more efficient and refined management solutions.

[0041] Urban flooding is a complex, multi-factorial phenomenon resulting from the combined effects of the physical environment and social activities. In urban flooding, there is an interaction between the adaptive behaviors of governments and residents and the urban environment's risk of flooding during heavy rains; the two influence each other. Currently, urban flooding research lacks a holistic study of social behavior and the physical environment. This study aims to explore the mechanisms of mutual influence and key factors in urban flooding by combining micro-level and subjective perspectives. By integrating behavioral methods from the social sciences with quantitative models from the natural sciences, the study aims to enable micro-level actors to adapt their behavior to macro-level changes, thereby refining the reduction of urban flooding risks and losses.

[0042] The positive and negative feedback relationships among various indicators in the system dynamics model of urban flood resilience were identified, and the behavioral decision-making results were applied to the system dynamics model to simulate the interaction between micro-level decisions and macro-level phenomena, such as... Figure 2 As shown, specifically:

[0043] The positive and negative feedback relationships between different subsystem models in the system dynamics model and the mathematical function relationships of each indicator factor are determined. The simulation results of multiple agents are set into the mathematical functions related to the factors in the system dynamics model. The model optimization is verified based on historical data. The simulation results based on the system dynamics model are set into the relevant behavioral states after the subject object attribute classification, so as to realize the interactive simulation of micro-decision and macro-phenomenon.

[0044] A coupled model for assessing urban flooding resilience is established, with government departments and residents as two micro-entities, and economic resilience, social resilience, infrastructure resilience, and environmental resilience as four subsystems in the system dynamics model. For example... Figure 3 As shown.

[0045] Step 3: Conduct sensitivity tests on the urban flood resilience assessment coupling model to identify key factors affecting urban flood resilience construction. By designing different scenario policies and analyzing the assessment results of different scenarios, explore the optimal management scheme affecting urban flood resilience construction.

[0046] Sensitivity tests were conducted on the coupled model for urban flood resilience assessment to identify key factors influencing urban flood resilience construction, specifically:

[0047] By adjusting the behavior rules of the main object and the parameters of the subsystem model in the system dynamics model, the future changes of urban waterlogging resilience construction are predicted, and the important factors affecting urban waterlogging resilience construction are analyzed.

[0048] By designing different scenario policies and analyzing the assessment results of different scenarios, this study explores the optimal management scheme affecting urban flood resilience construction. Specifically:

[0049] Based on the existing parameter values, predict the trend of urban flooding resilience changes;

[0050] Based on existing historical data, by modifying the parameter values ​​in mathematical functions or modifying behavioral designs to set different scenario policies, we can compare and analyze the changing trends of urban flooding resilience under different scenarios and make suggestions for improving urban flooding resilience.

[0051] Based on changing trends and recommendations, practical measures should be taken to improve urban flood resilience, thereby reducing economic losses caused by flooding and enhancing the city's resilience level.

[0052] This invention can also provide methodological support for other types of disasters.

[0053] This invention discloses a method for assessing urban flood resilience based on coupled modeling of multi-agent and system dynamics. By coupling modeling based on the micro-level evolution of multiple agents and the macro-level dynamic evolution of a system dynamics model, the method comprehensively considers the influencing factors of urban flooding and the causal relationships between them. This enables a scientific and refined assessment of urban flood resilience, accurately identifying weaknesses in urban flood disaster response, providing a scientific basis for policy formulation, thereby improving the city's disaster adaptability and recovery capabilities, helping cities to rationally plan and optimize resource allocation, and build high-standard, resilient urban systems.

[0054] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0055] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for assessing urban flood resilience based on multi-agent and system dynamic coupling modeling, characterized in that, include: Step 1: Obtain the parameter values ​​of various factors contributing to urban flooding, determine the main object of urban flooding resilience construction, and abstract the mathematical expression of the behavior based on the attribute characteristics of the main object to obtain the behavioral decision results of the main object; Step 2: Confirm the positive and negative feedback relationships among the various indicators in the system dynamics model of urban flood resilience construction level, and apply the behavioral decision results to the system dynamics model to conduct interactive simulation of micro-decision and macro-phenomenon, thereby obtaining the coupled model for urban flood resilience assessment; wherein, the system dynamics model includes: social resilience subsystem, economic resilience subsystem, infrastructure resilience subsystem, and environmental resilience subsystem; Step 3: Conduct sensitivity tests on the urban flood resilience assessment coupling model to identify the key factors affecting urban flood resilience construction. By designing different scenario policies and analyzing the assessment results of different scenarios, explore the optimal management scheme affecting urban flood resilience construction. The positive and negative feedback relationships among various indicators in the system dynamics model of urban flood resilience construction level are confirmed, and the behavioral decision results are applied to the system dynamics model to conduct interactive simulation of micro-decision and macro-phenomenon, specifically: The positive and negative feedback relationships between different subsystem models in the system dynamics model and the mathematical function relationships of each indicator factor are determined. The simulation results of the multi-subject are set into the mathematical functions related to the factors in the system dynamics model. The model optimization is tested based on historical data. The simulation results based on the system dynamics model are set into the relevant behavioral states after the subject object attribute classification, so as to realize the interactive simulation of micro-decision and macro-phenomenon. Sensitivity tests were conducted on the aforementioned urban flood resilience assessment coupling model to identify key factors influencing urban flood resilience construction, specifically: By adjusting the behavior rules of the main object and the parameters of the subsystem models in the system dynamics model, the future changes in urban waterlogging resilience construction are predicted, and the important factors affecting urban waterlogging resilience construction are analyzed. By designing different scenario policies and analyzing the assessment results of different scenarios, this study explores the optimal management scheme affecting urban flood resilience construction. Specifically: Based on the existing parameter values, predict the trend of urban flooding resilience changes; Based on existing historical data, by modifying the parameter values ​​in mathematical functions or modifying behavioral designs to set different scenario policies, we can compare and analyze the changing trends of urban flooding resilience under different scenarios and make suggestions for improving urban flooding resilience. Based on changing trends and recommendations, practical measures should be taken to improve urban flood resilience, thereby reducing economic losses caused by flooding and enhancing the city's resilience level.

2. The urban flood resilience assessment method based on multi-agent and system dynamic coupling modeling according to claim 1, characterized in that, In step 1, the abstract behavior is expressed mathematically based on the attribute characteristics of the subject object, specifically as follows: The subjects are classified according to their attribute characteristics, and the initial state and behavior rules of the subjects in different attribute categories are determined in turn. Decision statistical analysis of the subjects is then performed to obtain simulation results based on multiple subjects.

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