Evaluation of Factors Found to Influence Urban Flood Resilience in China
Abstract
:1. Introduction
2. Research Method
2.1. Selection of Research Methods
2.2. Establishing an ISM Model
2.3. ANP Method
2.4. TOPSIS Method
3. Case Study
3.1. Description of the Study Area
3.2. Sources of Data
3.3. Establishing Evaluation Index System
Goal Layer | First-Level Indicators | Secondary Indicators | Labels | Explanation | References |
---|---|---|---|---|---|
Evaluation index system of urban flood resilience | Social resilience | Vulnerable groups | N1 | The proportion of people over 60 years old and under 15 years old indicates vulnerable groups. | [49] |
Population density | N2 | Population per square kilometer. Reflects the density of population distribution. | [21,51] | ||
Medical service capacity | N3 | Reflects the efficiency and level of medical services. It can be measured by the number of health institutions per 10,000 people, the number of health technicians, and the number of hospital beds. | [20,29,40] | ||
Emergency rescue capability | N4 | The capability of emergency rescue and disaster relief under emergencies. | [1,52] | ||
Economic resilience | Fixed-asset investment | N5 | The workload of construction and purchase of fixed-asset activities. | [29] | |
Per capita disposable income | N6 | The sum of final consumption expenditure and savings. | [21,56] | ||
Disaster prevention capital investment | N7 | Total capital investment against various disasters. | [40] | ||
Ecological environment resilience | Green coverage rate of built district | N8 | The proportion of green areas and built-up areas. | [18,57] | |
Average annual rainfall | N9 | The average annual rainfall in a region. | [40,58] | ||
Spatial structure of land use | N10 | The spatial location of various types of land in the region and their combined pattern. | [59] | ||
Infrastructure resilience | Drainage pipe density in built-up area | N11 | The density of drainage pipeline distribution. | [62,63] | |
Per capita road area | N12 | The per capita road area occupied by urban population is expressed by the ratio of the total area of urban roads to the total urban population. | [56] | ||
Capability of sewage treatment | N13 | The capacity of a sewage treatment plant (or treatment plant) to treat sewage volume every day and night. | [58,62] |
3.4. Index System Structure Division
4. Results and Analysis
4.1. Analysis of Index Level Results
4.2. Analysis of ANP Results
4.3. Analysis of Flood Resilience in the Three Cities
4.4. Suggestions and Measures
5. Discussion
6. Conclusions
- There are many factors that affect the resilience of urban flooding, and each factor also affects each other. The explanatory structural model diagram intuitively reflects the influence mechanism of indicators. In this study, the direct influencing factors include per capita road area (N12), drainage pipeline density (N11), sewage treatment capacity (N13), green coverage rate of built-up areas (N8), disaster prevention capital investment (N7), and the spatial structure of land use (N10). The influencing factors of the middle layer include medical service capacity (N3), emergency rescue capacity (N4), per capita disposable income (N6), fixed-asset investment (N5), and population density (N2). The average annual rainfall (N9) and vulnerable groups (N1) are the deep and fundamental influencing factors and the root causes of urban flood problems.
- The importance of correctly identifying indicators is of great significance for evaluating flood resilience. The ANP method used in this study scores the importance of indicators with the help of experts’ knowledge and experience, ensuring the results are more in line with reality. The results of the ANP model show that the key indicators affecting urban flood resilience are average annual rainfall (N9), fixed-asset investments (N5), emergency rescue capability (N4), vulnerable groups (N1), and disaster prevention funding (N7).
- The levels of flood resilience of Zhengzhou, Xi’an, and Jinan were evaluated. According to the research results, the resilience level of Xi’an is the best, followed by Jinan, and the flood resilience of Zhengzhou is relatively weak. The economic resilience and infrastructure resilience of Xi’an need to be enhanced; for Jinan City, the resilience performance of the four dimensions is moderate, and there is no particularly poor dimension. However, Zhengzhou’s social resilience and ecological environment resilience levels are poor and need to be consolidated and improved urgently.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
N1 | N2 | N3 | N4 | N5 | N6 | N7 | N8 | N9 | N10 | N11 | N12 | N13 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
N1 | 0.0000 | 0.1629 | 0.2053 | 0.1650 | 0.0000 | 0.1375 | 0.1375 | 0.0000 | 0.0000 | 0.0000 | 0.1667 | 0.2222 | 0.1667 |
N2 | 0.1387 | 0.0000 | 0.0815 | 0.1650 | 0.0000 | 0.1092 | 0.0000 | 0.0000 | 0.1465 | 0.0000 | 0.0000 | 0.0000 | 0.1667 |
N3 | 0.0000 | 0.0646 | 0.0000 | 0.0000 | 0.1250 | 0.0866 | 0.1092 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
N4 | 0.2774 | 0.1026 | 0.1293 | 0.0000 | 0.1250 | 0.0000 | 0.0866 | 0.0000 | 0.1465 | 0.0000 | 0.0833 | 0.1111 | 0.0000 |
N5 | 0.1818 | 0.0000 | 0.3635 | 0.0000 | 0.0000 | 0.1667 | 0.0000 | 0.2929 | 0.1294 | 0.2761 | 0.2500 | 0.0000 | 0.3333 |
N6 | 0.1818 | 0.1922 | 0.0000 | 0.0000 | 0.1250 | 0.0000 | 0.0000 | 0.0000 | 0.0494 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
N7 | 0.0000 | 0.0961 | 0.0000 | 0.2883 | 0.1250 | 0.1667 | 0.0000 | 0.0000 | 0.0283 | 0.1381 | 0.0000 | 0.3333 | 0.0000 |
N8 | 0.0000 | 0.2068 | 0.0000 | 0.0000 | 0.0777 | 0.3333 | 0.0000 | 0.0000 | 0.1953 | 0.1953 | 0.0000 | 0.0000 | 0.1111 |
N9 | 0.0000 | 0.0000 | 0.0000 | 0.2068 | 0.1234 | 0.0000 | 0.3333 | 0.2761 | 0.0000 | 0.3905 | 0.2500 | 0.0000 | 0.2222 |
N10 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0490 | 0.0000 | 0.0000 | 0.1381 | 0.0976 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
N11 | 0.1469 | 0.0583 | 0.1102 | 0.0454 | 0.0650 | 0.0000 | 0.0000 | 0.1465 | 0.1381 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
N12 | 0.0000 | 0.0583 | 0.0000 | 0.0721 | 0.1032 | 0.0000 | 0.3333 | 0.1465 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
N13 | 0.0735 | 0.0583 | 0.1102 | 0.0572 | 0.0819 | 0.0000 | 0.0000 | 0.0000 | 0.0690 | 0.0000 | 0.2500 | 0.3333 | 0.0000 |
N1 | N2 | N3 | N4 | N5 | N6 | N7 | N8 | N9 | N10 | N11 | N12 | N13 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
N1 | 0.09024 | 0.09024 | 0.09024 | 0.09024 | 0.09024 | 0.09024 | 0.09024 | 0.09024 | 0.09024 | 0.09024 | 0.09024 | 0.09024 | 0.09024 |
N2 | 0.06813 | 0.06813 | 0.06813 | 0.06813 | 0.06813 | 0.06813 | 0.06813 | 0.06813 | 0.06813 | 0.06813 | 0.06813 | 0.06813 | 0.06813 |
N3 | 0.03446 | 0.03446 | 0.03446 | 0.03446 | 0.03446 | 0.03446 | 0.03446 | 0.03446 | 0.03446 | 0.03446 | 0.03446 | 0.03446 | 0.03446 |
N4 | 0.09177 | 0.09177 | 0.09177 | 0.09177 | 0.09177 | 0.09177 | 0.09177 | 0.09177 | 0.09177 | 0.09177 | 0.09177 | 0.09177 | 0.09177 |
N5 | 0.12825 | 0.12825 | 0.12825 | 0.12825 | 0.12825 | 0.12825 | 0.12825 | 0.12825 | 0.12825 | 0.12825 | 0.12825 | 0.12825 | 0.12825 |
N6 | 0.05201 | 0.05201 | 0.05201 | 0.05201 | 0.05201 | 0.05201 | 0.05201 | 0.05201 | 0.05201 | 0.05201 | 0.05201 | 0.05201 | 0.05201 |
N7 | 0.0872 | 0.0872 | 0.0872 | 0.0872 | 0.0872 | 0.0872 | 0.0872 | 0.0872 | 0.0872 | 0.0872 | 0.0872 | 0.0872 | 0.0872 |
N8 | 0.08144 | 0.08144 | 0.08144 | 0.08144 | 0.08144 | 0.08144 | 0.08144 | 0.08144 | 0.08144 | 0.08144 | 0.08144 | 0.08144 | 0.08144 |
N9 | 0.13113 | 0.13113 | 0.13113 | 0.13113 | 0.13113 | 0.13113 | 0.13113 | 0.13113 | 0.13113 | 0.13113 | 0.13113 | 0.13113 | 0.13113 |
N10 | 0.03032 | 0.03032 | 0.03032 | 0.03032 | 0.03032 | 0.03032 | 0.03032 | 0.03032 | 0.03032 | 0.03032 | 0.03032 | 0.03032 | 0.03032 |
N11 | 0.06356 | 0.06356 | 0.06356 | 0.06356 | 0.06356 | 0.06356 | 0.06356 | 0.06356 | 0.06356 | 0.06356 | 0.06356 | 0.06356 | 0.06356 |
N12 | 0.06481 | 0.06481 | 0.06481 | 0.06481 | 0.06481 | 0.06481 | 0.06481 | 0.06481 | 0.06481 | 0.06481 | 0.06481 | 0.06481 | 0.06481 |
N13 | 0.07669 | 0.07669 | 0.07669 | 0.07669 | 0.07669 | 0.07669 | 0.07669 | 0.07669 | 0.07669 | 0.07669 | 0.07669 | 0.07669 | 0.07669 |
Appendix B
First-Level Indicators | Secondary Indicators | Score Column | Supplementary | ||||
---|---|---|---|---|---|---|---|
Extremely Important | Important | General Importance | Not Important | Least Important | |||
Social resilience | Vulnerable groups | □ | □ | □ | □ | □ | |
Population density | □ | □ | □ | □ | □ | ||
Medical service capacity | □ | □ | □ | □ | □ | ||
Public awareness of disaster prevention | □ | □ | □ | □ | □ | ||
Emergency rescue capability | □ | □ | □ | □ | □ | ||
Economic resilience | Fixed-asset investments | □ | □ | □ | □ | □ | |
Per capita disposable income | □ | □ | □ | □ | □ | ||
Local revenue | □ | □ | □ | □ | □ | ||
Disaster prevention capital investment | □ | □ | □ | □ | □ | ||
Ecological environment resilience | Green coverage rate of built-up district | □ | □ | □ | □ | □ | |
Average annual rainfall | □ | □ | □ | □ | □ | ||
Harmless disposal rate of domestic waste | □ | □ | □ | □ | □ | ||
Spatial structure of land use | □ | □ | □ | □ | □ | ||
Infrastructure resilience | Drainage pipe density in built-up area | □ | □ | □ | □ | □ | |
Internet penetration | □ | □ | □ | □ | □ | ||
Per capita road area | □ | □ | □ | □ | □ | ||
Capability of sewage treatment | □ | □ | □ | □ | □ |
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No. | Description | Methods | Advantages and Disadvantages | Origin |
---|---|---|---|---|
1 | Identifying factors influencing flood resilience | DEMATEL -ANP | Indicator causality and weights can be derived, but the evaluation results are more subjective and cannot reveal the structural problems of the indicators | [36] |
2 | The relationship between factors in the three dimensions of stress, state, and response was measured | Fuzzy -DMATEL | The results can show causal influence relationships between indicators, and the results rely on expert judgment and do not integrate objective data considerations | [37] |
3 | Explored how urban systems affect urban flood resilience | SEM | Clearly explains the strength of the relationship between the factors, but the evaluation results are too subjective | [29] |
4 | Quantitative analysis of sustainability assessment of urban systems, comparing the results of FI and DEA methods | FI, DEA | FI facilitates the assessment of dynamic changes in the system, DEA is suitable for comprehensive evaluation with many inputs and outputs, and both methods ignore the influence of indicator weights | [27] |
5 | Ranking cities for disaster resilience based on objective data | VIKOR -GRA | Objective data are fully utilized, but the results may deviate from reality due to the limited selection of indicators and ignoring the experience of experts | [21] |
6 | Link between flood risk and resilience through case studies | ECPM | Objective quantification of urban flood risk and resilience, but no analysis of structural issues | [28] |
7 | The resilience of urban road traffic network (URTN) was explored using the entropy method and G1 method | EM-G1 | The combination of subjective and objective measures does not require consistency testing, but it cannot determine the impact of changes in a single indicator on the overall resilience of the URTN | [38] |
8 | The hierarchy of 13 influencing factors was analyzed | ISM-ANP | The analysis of indicator hierarchy and importance is more adequate, and the ISM-ANP model relies on the personal experience, knowledge, and professional judgment of decision makers and lacks objective and realistic analysis | [39] |
9 | The flood resilience of 31 key flood control cities was assessed | EM -TOPSIS | The assessment results are more objective and do not reflect the path of impact factors | [40] |
10 | Research on the influence mechanism and importance level of indicators of urban flood resilience to assess the level of urban flood resilience | ISM-ANP -TOPSIS | ISM can clearly reflect the influence mechanism of impact factors compared with other multi-objective decision making; ANP-TOPSIS combines subjective and objective data, and the evaluation results will not be detached from reality while making up for the defects of other methods, which are subjective | This research |
Background | Option | Number | Background | Option | Number | |
---|---|---|---|---|---|---|
Experts (n = 28) | Age | 30–39 | 5 | Highest academic credentials | Undergraduate | 3 |
40–49 | 17 | Master’s | 19 | |||
≥50 | 6 | Ph.D. | 6 | |||
Work unit | Colleges and universities | 10 | Expertise or research field | Urban resilience | 8 | |
Government departments | 6 | Flood management | 9 | |||
State-owned enterprises | 4 | Risk assessment | 6 | |||
Research institutes | 8 | Flood control and disaster reduction | 5 |
Reachable Set | Antecedent Set | |
---|---|---|
1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13 | 1 | 1 |
2, 4, 6, 7, 8, 10, 11, 12, 13 | 1, 2 | 2 |
3, 7, 8, 10, 11, 12, 13 | 1, 3, 5, 9 | 3 |
4, 7, 8, 10, 11, 12, 13 | 1, 2, 4, 5, 9 | 4 |
3, 4, 5, 6, 7, 8, 10, 11, 12, 13 | 1, 5, 9 | 5 |
6, 7, 8, 10, 11, 12, 13 | 1, 2, 5, 6, 9 | 6 |
7, 8, 10, 11, 12, 13 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 | 7, 8, 10, 11, 12, 13 |
7, 8, 10, 11, 12, 13 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 | 7, 8, 10, 11, 12, 13 |
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 | 9 | 9 |
7, 8, 10, 11, 12, 13 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 | 7, 8, 10, 11, 12, 13 |
7, 8, 10, 11, 12, 13 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 | 7, 8, 10, 11, 12, 13 |
7, 8, 10, 11, 12, 13 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 | 7, 8, 10, 11, 12, 13 |
7, 8, 10, 11, 12, 13 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 | 7, 8, 10, 11, 12, 13 |
First-Level Indicators | Weight | Secondary Indicators | Intra-Group Weight | Index Weight | Ranking |
---|---|---|---|---|---|
Social resilience | 0.285 | Vulnerable groups | 0.317 | 0.090 | 4 |
Population density | 0.239 | 0.068 | 8 | ||
Medical service capacity | 0.121 | 0.034 | 12 | ||
Emergency rescue capability | 0.322 | 0.092 | 3 | ||
Economic resilience | 0.267 | Fixed-asset investment | 0.480 | 0.128 | 2 |
Per capita disposable income | 0.194 | 0.052 | 11 | ||
Disaster prevention capital investment | 0.326 | 0.087 | 5 | ||
Ecological environment resilience | 0.243 | Green coverage rate of built-up districts | 0.335 | 0.081 | 6 |
Average annual rainfall | 0.540 | 0.131 | 1 | ||
Spatial structure of land use | 0.125 | 0.030 | 13 | ||
Infrastructure resilience | 0.205 | Drainage pipe density in built-up area | 0.310 | 0.064 | 10 |
Per capita road area | 0.316 | 0.065 | 9 | ||
Capability of sewage treatment | 0.374 | 0.077 | 7 |
Research Object | Ranking | |||
---|---|---|---|---|
Jinan city | 0.189 | 0.194 | 0.506 | 2 |
Zhengzhou city | 0.213 | 0.183 | 0.463 | 3 |
Xi’an city | 0.189 | 0.212 | 0.529 | 1 |
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Xu, W.; Yu, Q.; Proverbs, D. Evaluation of Factors Found to Influence Urban Flood Resilience in China. Water 2023, 15, 1887. https://doi.org/10.3390/w15101887
Xu W, Yu Q, Proverbs D. Evaluation of Factors Found to Influence Urban Flood Resilience in China. Water. 2023; 15(10):1887. https://doi.org/10.3390/w15101887
Chicago/Turabian StyleXu, Wenping, Qimeng Yu, and David Proverbs. 2023. "Evaluation of Factors Found to Influence Urban Flood Resilience in China" Water 15, no. 10: 1887. https://doi.org/10.3390/w15101887
APA StyleXu, W., Yu, Q., & Proverbs, D. (2023). Evaluation of Factors Found to Influence Urban Flood Resilience in China. Water, 15(10), 1887. https://doi.org/10.3390/w15101887