Preliminary Study on the Urban Flood Adaptive Capacity Index

Abstract

The increasing frequency and intensity of urban floods due to the climate crisis necessitate effective adaptation. In South Korea, flood vulnerability assessments have focused on preparedness, underscoring the need for adaptive capacity research. This study proposes the Urban Flood Adaptive Capacity Index (UFACI), a Fuzzy Logic-based framework that quantifies urban resilience. Developed from a socio-ecological systems (SES) perspective, the UFACI integrates economic resources, social capital, risk perception, and infrastructure. Fourteen indicators are applied using Fuzzy Logic to address uncertainties and enhance decision-making. The methodology is tested in 12 rainwater pumping station drainage areas in Seoul, providing actionable insights for flood management. This study contributes by shifting the focus from vulnerability to adaptive capacity, offering a systematic, data-driven approach to flood resilience assessment. Unlike conventional methods, the UFACI integrates socio-economic and physical factors, enabling targeted policy interventions and resource allocation. Its application in Seoul demonstrates its practical value, with potential adaptability for broader urban flood risk management.

1. Introduction

In recent years, many cities in South Korea have experienced increased urban flood damage due to abnormal rainfall patterns caused by the climate crisis. In response, South Korea’s Ministry of Environment enacted the Urban Inundation Prevention Act to mitigate the impacts of large-scale floods resulting from climate change and urbanization [1]. The Ministry of the Interior and Safety of South Korea has implemented significant measures, such as raising the target rainfall levels for urban flood prevention infrastructure, which serve as drainage and storage capacity standards during heavy rainfall events [2]. Despite these efforts, major cities like Seoul continue to face annual flooding during the monsoon season (late June to late July in Korea). As a result, urban flooding has become a yearly unavoidable disaster, highlighting the need to shift the focus from purely preventive measures to strategies that enhance urban flood resilience. Meanwhile, Gao et al. [3] analyzed global research trends on urban flood resilience. They pointed out that although the concept of urban resilience is becoming more refined, there remains a lack of quantitative evaluation models and empirical applications, indicating the need for further research. Similarly, Moreira et al. [4] conducted a comprehensive review of the existing literature, categorizing resilience factors from social, ecological, and technological perspectives, and proposed a comprehensive roadmap for future research and policy development. These studies collectively highlight the growing global interest in urban flood vulnerability and resilience, while also emphasizing the current limitations in quantitative assessment and practical policy integration.

In resilience research, previous studies [5,6,7,8] define adaptive capacity as the ability of human systems (e.g., social and economic activities) to maintain or improve the quality of life within a given environment, and consider it a key factor interacting with vulnerability and resilience within a socio-ecological framework. The IPCC [9] defines adaptive capacity as “the ability of systems to adjust to climate change, mitigate potential damages, take advantage of opportunities, or cope with the consequences.” This concept warrants further study in the context of urban flooding, beyond its broader associations with climate change or natural disasters, to assess adaptive capacity in a more targeted manner. Such research would integrate adaptive capacity studies with vulnerability and resilience evaluations for urban flood response. In this regard, several studies in Korea have explored these relationships. Using a vulnerability–resilience indicator, Kim et al. [10] assessed flood vulnerability in the Nakdong River basin. Lee et al. [11] evaluated flood vulnerability in Seoul using indicators for climate exposure, sensitivity, and adaptive capacity combined with an entropy-based weighting method. Lee et al. [12] developed indicators to assess urban resilience to flooding from a water circulation perspective, leveraging the concepts of climate hazards, vulnerability, and adaptive capacity. Eum & Kim [13] assessed flood vulnerability in semi-basement households in Seoul using a vulnerability assessment framework and indicators for exposure, sensitivity, and adaptive capacity. Kang & Lee [14] employed a fuzzy model, GIS, and a vulnerability assessment framework to evaluate flood vulnerability in Seoul. In Korea, research on adaptive capacity in urban flooding often treats the concept as a secondary factor in assessing flood vulnerability and resilience. Internationally, however, studies have explored adaptive capacity in diverse ways. Nhuan, M. T. et al. [15] quantitatively analyzed adaptive capacity for urban households in Da Nang, Vietnam, by selecting indicators tailored to local characteristics and objectives. Araya-Muñoz D. et al. [16] assessed adaptive capacity in Chile’s Concepción Metropolitan Area (CMA) through a structured framework. Thanvisitthpon, N. et al. [17] developed an adaptive capacity assessment framework for flood-prone areas in Thailand and derived an adaptive capacity index for Phetchaburi. Bixler, R. P. et al. [18] conducted surveys in Austin, Texas, USA, to identify factors influencing urban flood adaptive capacity. Engle, N. L. & Lemos, M. C. [19] analyzed water management governance across 18 river basins in Brazil, developing evaluation indicators to examine their relationship with climate change adaptive capacity. These prior studies highlight that while research on adaptive capacity evaluation in urban flooding in Korea remains limited, international efforts have actively examined adaptive capacity within the broader scope of climate change. However, intensive research focused on adaptive capacity specifically for urban flooding is still needed globally.

This study proposes a method to quantitatively evaluate urban flood adaptive capacity in South Korea from a socio-ecological perspective by establishing adaptive capacity evaluation indicators and deriving the Urban Flood Adaptive Capacity Index (UFACI) using Fuzzy Logic. The structure of this work is as follows: Section 2 describes the methodology of this study, including the definition of UFACI, the selection of evaluation indicators, and the application of Fuzzy Logic. Section 3 presents the results of the UFACI assessment applied to 12 drainage areas served by rainwater pumping stations in Seoul. Finally, Section 4 concludes by discussing the study’s findings and implications.

Despite the growing international focus on adaptive capacity in relation to climate change, its quantitative assessment, tailored explicitly to urban flooding, remains limited, especially in South Korea. This study fills this gap by proposing a new Urban Flood Adaptive Capacity Index (UFACI) that assesses adaptive capacity from a socio-ecological viewpoint using Fuzzy Logic. This research establishes relevant evaluation indicators and applies the model to 12 drainage areas in Seoul, providing a practical and adaptable framework for measuring urban flood adaptive capacity and contributing both methodologically and empirically to urban flood resilience research.

2. Methodology

Prior studies on adaptive capacity within socio-ecological systems (SES) were reviewed in the first stage to analyze the definitions and concepts related to disaster adaptive capacity, identifying common themes. This process defined the concept of urban flood adaptive capacity (UFAC). Based on previous studies on urban flood evaluation and considering the unique characteristics of Korean cities and flooding scenarios, the key components of urban flood adaptive capacity were identified, and detailed evaluation indicators were selected. The second stage involved applying the selected evaluation indicators and the Fuzzy Logic method to develop a methodology for deriving a quantified UFACI. Finally, in the third stage, the methodology was validated by applying it to 12 drainage areas served by rainwater pumping stations in Seoul, South Korea, to derive the UFACI results (Figure 1).

2.1. Selecting Urban Flood Adaptive Capacity Evaluation Indicators

Before quantitatively deriving and evaluating the Urban Flood Adaptive Capacity Index (UFACI), which is the core of this study, it is essential to clearly define the concept of adaptive capacity in the context of urban flooding. To achieve this, prior studies addressing adaptive capacity within socio-ecological systems (SES) were reviewed, and definitions and concepts related to disaster adaptive capacity were analyzed. Based on this analysis, the characteristics of urban areas and flooding were carefully considered to establish a comprehensive definition of urban flood adaptive capacity.

2.1.1. Definition of Urban Flood Adaptive Capacity

The key studies defining the concept of adaptive capacity (AC) from a socio-ecological systems (SES) perspective are summarized in

Table 1. Adaptive capacity defined from a socio-ecological systems (SES) perspective.

References	Definition of Adaptive Capacity
IPCC (2001) [9], Veenstra J (2013) [20].	The capacity of human societies and ecosystems to change and adapt in response to the impacts of climate change or to take advantage of new opportunities.
Yohe and Tol (2002) [21], Nelson, D. R. et al. (2007) [22].	The degree to which human or natural systems can absorb shocks without undergoing long-term damage or other significant state changes.
Dalziell, E. P. and McManus, S. T. (2004) [7].	The ability of a system to respond to external changes or shocks and adapt to new conditions.
Adger (2006) [23], Norris et al. (2008) [24], Barnes et al. (2020) [25].	Adaptive capacity is a set of resources and assets—economic, financial, social, informational, and community—that mitigate the effects of hazard exposure and sensitivity or susceptibility to hazard.

. Most of these studies present similar definitions, often linking AC to resilience or vulnerability. First, the IPCC [9] defines AC as ‘the capacity of human societies and ecosystems to change and adapt in response to the impacts of climate change or to take advantage of new opportunities.’ This framework categorizes climate change vulnerability into exposure, sensitivity, and adaptive capacity. Veenstra J. [20] also defines AC based on this definition. Yohe and Tol [21] and Nelson, D. R. et al. [22] define AC as ‘the degree to which human or natural systems can absorb shocks without undergoing long-term damage or other significant state changes.’ Their work emphasizes evaluating socio-economic response capabilities, analyzing various adaptation options to reduce vulnerability effectively, and understanding adaptive processes within a resilience framework, focusing on the interaction between social and ecological systems. Dalziell, E. P. and McManus, S. T. [7] define AC as ‘the ability of a system to respond to external changes or shocks and adapt to new conditions.’ They emphasize that AC should support systems in reverting to their previous state and achieving a new stable state following changes. Additionally, several researchers [23,24,25] define AC as ‘a set of resources and assets—economic or financial, social, informational, and community—that reduce the effects of hazard exposure and sensitivity or susceptibility to the hazard.’ These studies highlight adaptive capacity as an intermediary concept linking resilience and vulnerability, characterized by integrating multidimensional elements such as resources, learning, and social factors. These diverse definitions of AC collectively share a common theme: “the ability of human systems to tolerate and sustain life in given environments.”

Meanwhile,

Table 2. Components for urban flood evaluation [26].

Urban Flood Evaluation Component	Description	Feature	References
Tangible Impacts
Direct impacts	Physical damage to residential, commercial, industrial properties, and infrastructure caused by direct contact with floodwaters. Depth–damage functions are often applied.	Economic, Infrastructure	Penning-Rowsell et al. (2005) [27], Merz et al. (2010) [28].
Indirect impacts	Business interruptions, traffic disruptions, and economic ripple effects (can be analyzed using input–output or CGE models).	Economic, Social, and Infrastructure	Rose and Lim (2002) [29], Hallegatte (2008) [30].
Intangible Impacts
Health	Waterborne diseases (e.g., leptospirosis, diarrhea), mental impacts (e.g., PTSD, stress).	Risk perception	Ahern et al. (2005) [31], Kay and Falconer (2008) [32].
Environmental	Environmental contamination (e.g., soil pollution, water quality degradation) and ecosystem loss.	Risk perception, Infrastructure	Fewtrell et al. (2008) [33].
Psychological impacts	Psychological stress caused by flooding and during recovery.	Risk perception	Galea et al. (2005) [34], Reacher et al. (2004) [35].
Infrastructure Damage
Power and communication damage	Damage to power grids and telecommunication networks, resulting in indirect cascading effects.	Infrastructure	Rinaldi et al. (2001) [36], Emanuelsson et al. (2014) [37].
Transportation	Damage to road and rail networks, costs from traffic congestion and disruptions.	Infrastructure	Chang et al. (2010) [38].
Water supply and sanitation	Damage to water supply, sewage, and sanitation facilities cause public health concerns.	Infrastructure	Scawthorn et al. (2006) [39].
Social and Economic Impacts
Economic costs	Direct costs (asset loss) and indirect costs (productivity losses, economic ripple effects) from flooding.	Economic, Social	Rose (2004) [40].
Social resilience	Population composition (e.g., elderly people ratio, vehicle access) and community resilience.	Social	Cutter et al. (2008) [5], Manyena (2006) [41].

presents the components utilized to evaluate urban flooding. According to Hammond, M. J. et al. [26], urban flooding significantly impacts essential urban services such as energy, water resources, transportation, and housing. The primary factors for evaluating urban flooding are tangible impacts, intangible impacts, infrastructure damage, and socio-economic impacts. These components are considered part of the human system from an SES perspective, commonly reflecting economic, social, risk perception, and infrastructure characteristics. Consequently, by comprehensively analyzing adaptive capacity in the disaster domain and the human system components evaluated in urban flooding, urban flood adaptive capacity is defined as follows: “The ability of human systems (economic, social, risk perception, infrastructure) to tolerate and sustain life in given environments during urban flooding events.”

2.1.2. Selection of Components and Indicators for Evaluating Urban Flood Adaptive Capacity

Urban flood disasters are commonly evaluated using four key factors: economic resources, social capital, risk perception, and infrastructure (Table 1 and Table 2). Therefore, this study selected these factors as the primary components for evaluating urban flood adaptive capacity. To identify appropriate detailed evaluation indicators for each component, the elements of urban flood adaptive capacity were defined by incorporating the characteristics of urban flooding (

Table 3. Definitions of the components of urban flood adaptive capacity.

Component	Definition	Selected Indicator	Code
Economic resources	The financial capacity within a community enhances flood resilience and supports adaptation to urban flooding risks.	Economic efficiency of urban flood prevention facilities (B/C)	A
		Average household income (income decile)	B
		Financial independence rate of administrative institutions (%)	C
Social capital	The collective value of social networks and norms fosters cooperation within the community, enabling preparation for and response to flooding events.	Number of disaster-related institutions (police, fire department)	D
		Number of medical institutions	E
Infrastructure	The physical systems and facilities, such as drainage and water retention systems, are designed to manage flood risks and mitigate the impacts of urban flooding.	Design frequency (return period)	F
		$\mathrm{Total} \mathrm{water} \mathrm{retention} \mathrm{capacity} ({\mathrm{m}}^3$)	G
		$\mathrm{Total} \mathrm{drainage} \mathrm{capacity} ({\mathrm{m}}^3$/min)	H
Risk perception	The community’s awareness, understanding, and preparedness for flood risks influence their behavior and willingness to adopt protective measures.	EQ-5D (Health-related quality of life measure)	I
		Flood damage history evaluation score (points)	J
		Number of residents with storm and flood insurance	K

). Economic resources for urban floods are defined as ‘The financial capacity within a community enhances flood resilience and supports adaptation to urban flooding risks.’ Social capital in urban floods is defined as ‘The collective value of social networks and norms fosters cooperation within the community, enabling preparation for and response to flooding events.’ Infrastructure for urban floods is defined as ‘The physical systems and facilities, such as drainage and water retention systems, are designed to manage flood risks and mitigate the impacts of urban flooding.’ Risk perception in urban floods is defined as ‘The community’s awareness, understanding, and preparedness for flood risks influence their behavior and willingness to adopt protective measures.

The detailed evaluation indicators were selected using two criteria based on these definitions. First, indicators that align with the defined components and have been widely utilized in previous studies were prioritized. Second, indicators with higher relevance and applicability to the context of South Korean cities were chosen. The selected indicators were structured to assess the urban flood adaptive capacity effectively and were utilized as the foundation for the core analysis in this study.

Regarding the economic resources component, Nhuan et al. [15] and Thanvisitthpon, N. et al. [17] used evaluation indicators such as monthly household income, the economic impact of flood depth, and wealth to assess adaptive capacity. Based on these prior studies, this research selected three evaluation indicators for assessing economic resources, considering the ease of data collection and relevance in the Korean context. The first indicator is the economic efficiency of urban flood prevention facilities (B/C), the second is average household income (income decile), and the third is the financial independence rate of administrative institutions (%). These indicators effectively reflect the importance of economic resources in evaluating urban flood adaptive capacity in South Korea. For social capital, Putnam, R. D., and Fukuyama, F. [42,43] defined it as a multidimensional concept encompassing the value of personal networks, norms, cohesion, and trust, which promotes social cooperation and helps participants pursue common goals more effectively. Based on this definition, social capital in an urban context can be interpreted as public services that provide social safety. Thus, this study selected the number of disaster-related institutions (police, fire department) and the number of medical institutions as evaluation indicators for social capital. These indicators contribute to assessing urban communities’ social safety net and emergency response capacity. For the infrastructure component, Thanvisitthpon, N. et al. [17] utilized the condition and maintenance of drainage systems as evaluation indicators for urban flood scenarios. Building on this, this research selected design frequency (return period), total water retention capacity (${\mathrm{m}}^3$), and total drainage capacity (${\mathrm{m}}^3$/min) as evaluation indicators from the perspective of urban flood prevention infrastructure. Regarding the risk perception component, Raaijmakers et al. [44] argued that flood risk perception should be conceptualized as the relationship between perception, concern, and preparedness. Based on this concept, this study selected EQ-5D (Health-Related Quality of Life Index), the flood damage history evaluation score, and the number of residents with storm and flood insurance as evaluation indicators. In particular, the flood damage history evaluation score was calculated using the evaluation method recommended by Korea’s Enforcement Decree of the Countermeasures Against Natural Disasters Act.

2.2. Establishment of UFACI Derivation Method Using the Evaluation Indicators and Fuzzy Logic Method

Fuzzy Logic, first proposed by Zadeh [45], is a logical system developed to handle the uncertainty in natural language and human judgment processes. This logic models subjective human judgments mathematically, enabling the derivation of quantitative results. One of the key advantages of Fuzzy Logic is its ability to address uncertainty and ambiguity more precisely than traditional binary logic. This capability has made it applicable in various fields, including artificial intelligence, control systems, optimization problems, and multi-criteria decision-making (MCDM). It is beneficial in quantifying intuitive human judgments in complex systems or drawing reasonable conclusions in situations lacking clear boundaries. Additionally, this technique is widely used in flood-related research [46]. Therefore, this study applied Fuzzy Logic to calculate the Urban Flood Adaptive Capacity Index (UFACI), a quantified urban flood adaptive capacity measure. Evaluating UFACI requires the careful consideration of multiple evaluation indicators across various factors. Such multidimensional characteristics are often challenging to address effectively using traditional deterministic approaches. Therefore, this study utilized the Fuzzy Inference System (FIS) developed by Mamdani [47], which infers outcomes based on the interactions between input variables. This approach ensures a robust and rational evaluation of urban flood adaptive capacity.

The Mamdani Fuzzy Inference System (FIS) is a rule-based system consisting of the following key stages: defining fuzzy sets, fuzzification, fuzzy rules definition, fuzzy inference, and defuzzification. The first stage, defining fuzzy sets, involves representing the input and output values of the system as fuzzy sets. In this stage, appropriate fuzzy sets are defined for each selected evaluation indicator, and the boundaries of the fuzzy sets are determined using membership functions. For example, a triangular membership function can be defined as follows:

$$\mathsf{\mu }\left(\mathrm{x}\right)=\left\{\begin{array}{cc}\hfill 0& \mathrm{i}\mathrm{f} \mathrm{x}<\mathrm{a}\hfill \\ \\ \hfill {\displaystyle \frac{\mathrm{x}-\mathrm{a}}{\begin{array}{c}\mathrm{b}-\mathrm{a}\\ \end{array}}}& \mathrm{i}\mathrm{f} \mathrm{a}<\mathrm{x}<\mathrm{b}\hfill \\ \hfill {\displaystyle \frac{\mathrm{c}-\mathrm{x}}{\begin{array}{c}\mathrm{c}-\mathrm{b}\\ \end{array}}}& \mathrm{i}\mathrm{f} \mathrm{b}<\mathrm{x}<\mathrm{c}\hfill \\ \hfill 0& \mathrm{i}\mathrm{f} \mathrm{x}>\mathrm{c}\hfill \end{array}\right.$$

(1)

where μ(x) represents the membership function, which indicates the degree to which a specific input value x belongs to the corresponding fuzzy set. The parameters a, b, and c define the boundary values of the fuzzy set. Membership functions constitute the fuzzy sets and can be intuitively determined by the system designer or established through data analysis.

In the fuzzification stage, the degree to which an input value belongs to a defined fuzzy set is calculated. The input value is substituted into the membership function, transforming into a membership value between 0 and 1. The third stage, fuzzy rules definition, involves establishing “If-Then” rules that describe the interactions between input variables. These rules use linguistic expressions to define the relationship between conditions and results. The fourth stage, fuzzy inference, involves evaluating the established regulations and generating an output fuzzy set from the input values. In this stage, the activation level of each rule is calculated, and the output fuzzy set is adjusted by clipping it according to the activated value. Subsequently, the results of all rules are combined to produce a single output fuzzy set. This process can be expressed with the following equation:

$${\mathsf{\mu }}_{result}\left(z\right)=\mathrm{m}\mathrm{a}\mathrm{x}({\mu }_{R1}\left(z\right), {\mathsf{\mu }}_{R2}(z),\dots )$$

(2)

where ${\mathsf{\mu }}_{result}\left(z\right)$ represents the membership function of the final output fuzzy set, while ${\mu }_{R1}\left(z\right)$ and ${\mathsf{\mu }}_{R2}(z$) represent the output fuzzy sets generated by individual rules.

Defuzzification’s final stage converts the final output fuzzy set into a crisp value. The Center of Gravity (CoG) method was applied in this study. This method calculates the centroid of the output fuzzy set to determine the resulting value. The following formula expresses the CoG method:

$$z^{*}={\displaystyle \frac{\int z·{\mu }_{result}\left(z\right)dz}{\int {\mu }_{result}\left(z\right)dz}}$$

(3)

where $z^{*}$ is the defuzzified output value.

3. Study Area

Seoul, the capital of South Korea, spans about 605 km² and is home to approximately 10 million people. The city is bisected by the Han River and characterized by a mix of hilly terrain and low-lying floodplain areas. The climate is humid continental with a strong monsoonal pattern—yearly precipitation ranges roughly from 1200 to 1400 mm, which is heavily concentrated during the summer months (June–August). Intense short-duration rainstorms frequently overwhelm local drainage systems during this period, leading to urban flash floods. Seoul is highly urbanized; nearly half of its land area is covered by impervious surfaces, drastically limiting natural infiltration and causing rapid runoff. Notably, impervious cover has increased from approximately 40% in the 1960s to about 48% by 2010, significantly exacerbating runoff challenges and making the city particularly vulnerable to urban flooding [48].

Geologically, Seoul primarily consists of granite and gneiss bedrock, which is part of the central Korean Peninsula’s crystalline massif. The natural soils in undeveloped areas are predominantly loamy to sandy loam, providing reasonable natural drainage. However, urban cores exhibit heavily disturbed soils or have been replaced entirely by artificial fill and pavement, severely restricting water infiltration and resulting in substantial surface runoff during heavy rains. The city’s topography directs runoff from northern and southern uplands toward the Han River basin through numerous small stream valleys, many of which have become heavily urbanized or buried beneath urban infrastructure. This urbanization significantly complicates stormwater management, necessitating artificial pumping and drainage systems rather than relying on natural floodplains.

The city’s drainage infrastructure includes rivers, streams, combined sewers, and strategically located rainwater pumping stations, crucial for mitigating flood impacts and forming a cornerstone of Seoul’s disaster prevention strategy. This study targets 12 drainage areas serviced by these rainwater pumping stations (Figure 2). Each drainage area functions as a micro-watershed within the urban landscape, with stormwater mechanically pumped into the Han River or upstream channels due to an insufficient natural drainage capacity. The selected drainage areas—Bangbae, Seorae, Yangjae, Samgakji, Moonbae, Simwon, Bogwang, Dongbinggo, Geumho, Jegi-1, Gocheok-1, and Godeok—vary considerably in size, land use patterns, and historical flood risks.

Some areas, such as Moonbae, Samgakji, and Dongbinggo, located near the Han River, possess extensive paved surfaces and robust infrastructure investments. Conversely, residential districts such as Bangbae and Godeok include recognized low-lying zones vulnerable to pluvial flooding. Particularly, Bangbae has historically faced recurrent flooding, leading to its official designation as a disaster risk zone in 2011 due to severe flood damage [49]. These drainage areas have been identified as falling short of Seoul’s disaster prevention performance targets, making them critically vulnerable. Therefore, these regions have been prioritized for detailed evaluations and targeted discussions to strengthen their overall disaster prevention and adaptive capacities.

Hydrologic background data for each drainage area, including design flood levels, drainage capacities, pump capacity, and stormwater retention volumes, are explicitly incorporated into our analysis through relevant indicators.

4. Results and Discussion

To derive the UFACI, the previously established definitions of urban flood adaptive capacity, the selection of components and evaluation indicators, and the data collection steps were utilized as the foundation for applying Fuzzy Logic. First, membership functions were designed for each evaluation indicator, and fuzzy rules were constructed based on these functions. Through this process, the UFACI for each drainage area was ultimately derived. The results were analyzed to evaluate each target area’s flood adaptive capacity comprehensively. This analysis enabled the identification of places that are genuinely vulnerable to flooding.

4.1. Design of Membership Functions and Definition of Fuzzy Rules for Indicators

In this study, a Mamdani-type Fuzzy Inference System (FIS) was implemented to calculate the UFACI. Triangular membership functions were adopted for fuzzification due to their simplicity, clarity of interpretation, and prevalence in similar evaluation contexts. These functions are characterized by three parameters, lower bound, midpoint, and upper bound, facilitating the straightforward interpretation of each indicator’s membership category (“Low,” “Medium,” “High”). The parameter values (thresholds) for each indicator’s membership functions were determined primarily through the statistical analysis of data collected from the 12 drainage areas. Specifically, the thresholds were set approximately at the lower 30% and upper 30% quantiles of observed data distributions, ensuring the meaningful representation of the respective categories. Specific indicators incorporated predefined benchmarks based on established policy or technical standards. For instance, the indicator for design frequency (indicator F) utilized thresholds at 25-year and 50-year return periods, consistent with Seoul’s historical (approximately 20–30 years) and contemporary (≥50 years) drainage design standards. Similarly, thresholds for the financial independence indicator (indicator C) were set at 40% and 70%, aligning with municipal fiscal benchmarks; a rate below 40% indicates constrained economic resources, while above 70% signifies substantial budgetary autonomy. The health-related quality-of-life indicator (EQ-5D, indicator I) employed a threshold of 0.960, reflecting that even minor variations significantly influence community resilience.

Table 4. Boundary definition and fuzzy rule design for evaluation indicators.

Adaptive Capacity	Low	Medium	High	Fuzzy Rule
Indicator
A	A < 0.4	0.4 ≤ A < 0.6	0.6 ≤ A	Positively contribute
B	1 ≤ B < 4	4 ≤ B < 7	7 ≤ B	Positively contribute
C	C < 40%	40% ≤ C < 70%	70% ≤ C	Positively contribute
D	D < 4	4 ≤ D < 7	7 ≤ D	Positively contribute
E	E < 40	40 ≤ E < 70	70 ≤ E	Positively contribute
F	F < 25 year	25 year ≤ F < 50 year	50 year ≤ F	Positively contribute
G	$\mathrm{G} \text{<} 5000 {\mathrm{m}}^3$	$5000 {\mathrm{m}}^3$$\text{≤} \mathrm{G} \text{<} 10,000{\mathrm{m}}^3$	$10,000 {\mathrm{m}}^3$ ≤ G	Positively contribute
H	$\mathrm{H} \text{<} 1000 {\mathrm{m}}^3/\mathrm{m}\mathrm{i}\mathrm{n}$	$1000 {\mathrm{m}}^3/\mathrm{m}\mathrm{i}\mathrm{n}$$\text{≤} \mathrm{H} \text{<} 2000 {\mathrm{m}}^3/\mathrm{m}\mathrm{i}\mathrm{n}$	$2000 {\mathrm{m}}^3/\mathrm{m}\mathrm{i}\mathrm{n}$ ≤ H	Positively contribute
I	I < 0.945	0.945 ≤ I < 0.960	0.960 ≤ I	Positively contribute
J	1 ≤ J ≤ 7	8 ≤ J ≤ 14	15 ≤ J	Negatively contribute
K	K < 50	50 ≤ K < 100	K ≤ 100	Positively contribute

and Figure 3 summarize the membership function parameters defined for all indicators.

The fuzzy rule base was constructed using logical conditions reflecting each indicator’s contribution to adaptive capacity. For indicators with positive associations (higher values indicate higher adaptive capacity), the rules were formulated as follows: “If indicator X is Low, then adaptive capacity is Low,” and “If indicator X is High, then adaptive capacity is High.” Conversely, indicators with negative associations, such as flood damage history (indicator J), utilized inverse logic. For example, in this study, one of the fuzzy rules was formulated as follows: “If average household income (B) is High AND design frequency (F) is High, THEN adaptive capacity is High.” Here, the term “High” for average household income refers to values greater than 5.0 million KRW per month, and “High” for design frequency corresponds to return periods exceeding 50 years. This rule reflects the assumption that households with higher financial resources, combined with robust flood protection infrastructure, can better prepare for and respond to extreme rainfall events. In the Mamdani FIS, the membership degrees for each input (e.g., 0.8 for High income, 0.9 for High design frequency) are combined using the fuzzy AND operator (minimum), resulting in a rule strength of 0.8. This strength is then applied to the output membership function for “High adaptive capacity” during the aggregation process.

4.2. Derivation of UFACI

To implement the UFACI methodology, data for the selected evaluation indicators were collected and analyzed (

Table 5. Data collection for evaluation indicators corresponding to each rainwater pumping station drainage area.

Component	Economic Resources			Social Capital		Infrastructure			Risk Perception
Indicator	A	B	C	D	E	F	G	H	I	J	K
Target area
Bangbae	0.56	9	53.2	6	20	10	2600	1000	0.958	20	73
Seorae	0.63	9	53.2	3	30	10	1800	775	0.958	0	110
Yangjae	0.54	8	53.2	2	176	10	3520	702	0.974	0	630
Samgakji	0.23	9	39.4	5	88	10	445	630	0.974	20	317
Moonbae	0.55	8	39.4	3	3	10	1750	350	0.974	0	12
Simwon	1.32	8	39.4	3	21	10	510	160	0.974	0	77
Bogwang	0.88	6	39.4	5	21	10	2520	803	0.974	0	77
Dongbinggo	0.50	8	39.4	2	86	10	147	110	0.974	0	308
Geumho	0.63	8	30.2	11	58	10	1012	1000	0.945	3.12	208
Jegi 1	1.04	6	21.6	4	36	10	250	400	0.960	7.92	129
Gocheok 1-dong	6.28	6	19.9	3	35	10	300	126	0.960	0	127
Godeok	0.34	6	21.9	7	61	20	3112	3971	0.958	0	220

). The economic efficiency of urban flood prevention facilities, design frequency, total water retention capacity, and total drainage capacity indicators were calculated using specifications of the rainwater pumping stations and B/C ratio values derived through the Net Present Value (NPV) method [49]. The average household income indicator was obtained from the Seoul Business District Analysis Service [50], while the financial independence rate of administrative institutions was collected from Seoul’s Open Data Portal [51]. Data for the number of disaster-related institutions were gathered through manual field surveys conducted with the assistance of Naver Map, Kakao Map, and Google Maps. The number of medical institutions and EQ-5D indicators were sourced from the Korean Statistical Information Service (KOSIS) [52], and the number of residents with storm and flood insurance was based on data provided by the Ministry of the Interior and Safety of South Korea [53]. After applying the UFACI methodology with the collected evaluation indicators and Fuzzy Logic, adaptive capacity scores for each of the 12 rainwater pumping station drainage areas in Seoul were derived (

Table 6. UFACI results and rankings for 12 rainwater pumping station areas in Seoul.

Rank	Rainwater Pumping Station	UFACI
1	Moonbae	0.977
2	Gocheok 1-dong	0.970
3	Simwon	0.958
4	Dongbinggo	0.955
5	Samgakji	0.939
6	Seorae	0.937
7	Godeok	0.930
8	Yangjae	0.910
9	Jegi 1	0.879
10	Bogwang	0.867
11	Geumho	0.782
12	Bangbae	0.748

and Figure 4). The UFACI values range from 0 to 1, with values closer to 1 indicating a higher urban flood adaptive capacity for the corresponding area. Overall, UFACI scores ranged from approximately 0.75 to 0.98, revealing notable differences in adaptive capacities among the drainage areas. While most areas showed relatively high scores above 0.85, indicating generally good adaptive capacity city-wide, several areas demonstrated significant vulnerabilities. The Moonbae rainwater pumping station drainage area recorded the highest UFACI value of 0.977, reflecting its robust performance across most evaluation indicators. This implies excellent adaptive capacity, supported by substantial economic resources, recently enhanced infrastructure such as upgraded drainage and storage facilities, and strong community awareness. Following closely were Gocheok-1 (0.970), Simwon (0.958), and Dongbinggo (0.955), which similarly exhibited well developed flood control measures and socio-economic advantages. For instance, Gocheok-1 benefits from a large retention reservoir and a relatively affluent community, positively influencing its resilience. Conversely, the Bangbae rainwater pumping station drainage area had the lowest UFACI score of 0.748, indicating limited adaptive capacity to urban flooding. Bangbae showed a weaker performance, particularly in infrastructure-related indicators, such as lower pumping and retention capacities, as well as moderate-to-low economic and social indicators. Additionally, its history of frequent and significant flood damage significantly reduced its adaptive capacity. Similarly, Geumho exhibited the second lowest UFACI score of 0.782, with Jegi-1 and Bogwang areas also relatively lower (UFACI ~0.87), suggesting clear areas for targeted improvement measures. The observed gap of approximately 0.23 between the highest and lowest UFACI scores highlights substantial variability in urban flood adaptive capacity, even within a single metropolitan area, emphasizing the necessity for tailored flood prevention strategies and interventions.

4.3. Interpretation of Results and Comparative Analysis

These analyses highlight the need for tailored interventions to address the specific vulnerabilities of areas with lower UFACI rankings. Prioritizing such regions can help minimize flood damage and enhance urban communities’ adaptive capacity and resilience. These UFACI results allow for a deeper analysis of why certain areas perform better or worse, and what that implies for flood risk management in Seoul. We combined the quantitative index findings with qualitative insights from media and policy records to interpret each area’s situation.

4.3.1. Media and Public Attention

To investigate the potential relationship between adaptive capacity and public attention, a media frequency analysis was conducted. News and social media data (2022–2024) were collected to quantify the mentions related to flooding or drainage issues for each drainage area (Figure 5). The results indicated that lower-ranked UFACI areas (e.g., Jegi-1, Geumho) had significantly higher media mentions following heavy rainfall events, reflecting higher public concern. Conversely, high UFACI areas (e.g., Moonbae, Gocheok-1) were rarely mentioned. This pattern confirms UFACI’s external validity, suggesting that areas with a lower adaptive capacity indeed attract more public attention due to recurrent flood issues, highlighting the importance of enhancing social capacity and community preparedness.

4.3.2. Historical Flood Experience and Institutional Response

Historical flood data corroborated the UFACI rankings. For instance, Bangbae, which ranked lowest, has been repeatedly affected by floods and was officially designated as a high-risk zone in 2011 [54]. Although infrastructure improvements (e.g., sewer upgrades, new pumping stations) have been implemented, Bangbae’s persistently low UFACI suggests that infrastructure enhancements alone are insufficient. Additional social and institutional measures appear necessary. In contrast, higher-ranking areas (e.g., Seorae, Samgakji) have benefited from sustained infrastructure investments due to their strategic importance, leading to higher UFACI scores.

4.3.3. Socio-Economic and Physical Factors

The analysis of individual indicators revealed that high-performing areas consistently scored well across economic, social, and infrastructural components. In contrast, low-performing areas exhibited pronounced deficiencies in at least one critical area. Geumho, for example, scored low primarily due to insufficient infrastructure capacity, whereas Bogwang’s relatively weak economic indicators reduced its overall adaptive capacity. These observations emphasize that comprehensive strategies addressing both physical and socio-economic factors are critical for effective flood resilience.

4.3.4. Comparison with Traditional Vulnerability Assessments

Traditional urban flood assessments typically focus on hazard exposure and vulnerable elements such as population density in flood-prone areas and property values, resulting in flood risk or vulnerability maps. The UFACI offers a complementary perspective by specifically evaluating adaptive and recovery capacities. For example, conventional flood hazard maps might highlight Seoul’s low-lying areas along rivers as having high flood depth potential; however, the UFACI may rate some of these same areas as relatively resilient due to robust mitigation infrastructure and effective community preparedness. Conversely, areas identified with moderate hazard exposure but exhibiting a very low adaptive capacity stemming from insufficient resources or planning could face higher overall disaster risks. Our findings confirmed this pattern, where areas designated by the city as flood-prone, such as Bangbae and Jegi 1, indeed showed low UFACI scores. Interestingly, the Jegi 1 area exhibited only moderate physical hazards yet had a notably low UFACI, indicating significant vulnerability driven by socio-economic factors such as lower-income communities residing in semi-basement housing, consistent with the findings by Eum and Kim [13]. This observation underscores UFACI’s key advantage in integrating critical factors like social capital and risk perception, which are often overlooked in traditional hazard or vulnerability assessments. Consequently, the UFACI effectively identifies at-risk communities that traditional maps might overlook due to the limited consideration of adaptive capacity.

In summary, the comparative analysis demonstrates that the UFACI differentiates areas in line with real-world experiences. Areas with a low adaptive capacity consistently experience greater flood impacts and higher public concern, thus validating the index’s utility in pinpointing resilience gaps. Conversely, areas with higher UFACI scores reflect the positive outcomes achieved through substantial investments and proactive preparedness measures, offering valuable best-practice insights.

4.4. Policy Implications and Recommendations

The UFACI results provide several critical policy implications and recommendations for urban flood risk management in Seoul and beyond.

4.4.1. Targeted Interventions

The UFACI rankings identify priority areas for intervention, enabling policymakers to direct resources effectively. Specifically, areas with low UFACI scores, such as Bangbae, Geumho, and Jegi 1, require targeted actions to address identified weaknesses. In Bangbae, for instance, structural improvements, including expanded pump capacity and additional stormwater retention facilities, should be coupled with non-structural measures such as community-based flood response training and initiatives promoting flood insurance uptake. The findings indicate that infrastructure enhancement alone is insufficient when socio-economic and community capacities remain low. Thus, comprehensive policy interventions, such as insurance subsidies for low-income residents and the establishment of local flood response volunteer networks, are recommended to enhance overall adaptive capacity.

4.4.2. Enhancing Social Capital and Community Awareness

While areas like Jegi 1 and Geumho possess adequate infrastructure, their lower UFACI scores, coupled with frequent media attention, reflect underlying social vulnerabilities. Strengthening social networks and enhancing risk communication efforts can significantly bolster community resilience. Municipal authorities should collaborate with local NGOs and community leaders to implement community-based disaster management programs. Educational campaigns emphasizing flood risks, personal preparedness, and property protection—including promoting increased enrollment in flood insurance programs (indicator K)—can effectively improve community risk perceptions and preparedness.

4.4.3. Integration with Existing Policies

The UFACI framework offers a complementary quantitative measure to traditional flood risk assessments and can be effectively integrated into existing disaster management frameworks. City planners and emergency management officials can utilize UFACI scores alongside conventional hazard maps to allocate resources more equitably and strategically. For example, areas demonstrating moderate hazard but exceptionally low adaptive capacity should receive increased attention and resources, as their vulnerabilities could exacerbate impacts during flood events. Thus, incorporating the UFACI into the decision-making process ensures a balanced approach, accounting for both physical flood risk and adaptive capacity.

4.4.4. Monitoring and Evaluation

Due to its composite and data-driven nature, the UFACI serves as an effective tool for ongoing monitoring and evaluation. Authorities could periodically update UFACI scores (e.g., annually or biennially) for all drainage areas, facilitating the tracking of changes in adaptive capacities over time. An improvement in UFACI scores would indicate the effectiveness of implemented interventions, whereas declining scores would signal potential concerns requiring corrective measures. Furthermore, the UFACI could be utilized to measure the efficacy of specific policy actions, such as city-wide flood awareness campaigns, by observing changes in indicators related to risk perception and preparedness.

4.4.5. Applicability to Other Cities and Broader Implications

While this study was conducted specifically in Seoul, the UFACI methodology is inherently adaptable to other urban contexts. Cities interested in applying the UFACI should collaborate with local experts to tailor indicator selection and adjust Fuzzy Logic parameters to reflect their unique socio-economic and infrastructural conditions. Despite context-specific adjustments, the core dimensions of economic resources, social capital, infrastructure, and risk perception remain broadly relevant across urban flood scenarios. National-level agencies could consider standardizing a UFACI-based adaptive capacity assessment method, facilitating comparative evaluations across different cities. Such a standardized index would highlight municipalities requiring additional support and encourage the dissemination of best practices between cities exhibiting varying levels of adaptive capacity.

5. Conclusions

This study is an initial exploration proposing the Urban Flood Adaptive Capacity Index (UFACI) to evaluate urban flood adaptive capacity quantitatively. To achieve this, prior studies addressing adaptive capacity within Social–Ecological Systems (SES) were reviewed, and the definition and concept of disaster adaptive capacity were analyzed. Furthermore, the concept of adaptive capacity was redefined in the context of urban flooding. For UFACI assessment, four key components—economic resources, social capital, risk perception, and infrastructure—and 14 detailed indicators were selected. Using these indicators, a methodology to derive the UFACI was established by applying Fuzzy Logic. The approach was implemented across 12 drainage areas managed by rainwater pumping stations in Seoul to assess flood adaptive capacity. The core findings of this study are as follows:

• Urban flood adaptive capacity is defined as “The ability of human systems (economic, social, risk perception, infrastructure) to tolerate and sustain life in given environments during urban flooding events.” It has been conceptualized into four main components: economic resources, social capital, risk perception, and infrastructure. Fourteen detailed indicators were selected to align with these components and reflect the characteristics of urban areas in South Korea.
• Based on the conceptualized evaluation components of urban flood adaptive capacity, 14 detailed indicators were selected to align with each component and reflect the characteristics of urban areas in South Korea. This framework was designed to quantitatively assess the impact of each component on urban flood adaptive capacity.
• A methodology to quantify the UFACI using Fuzzy Logic was proposed, including establishing specific membership functions and fuzzy rules for each indicator. The developed UFACI derivation method was applied to 12 drainage areas managed by rainwater pumping stations in Seoul to validate its reliability and effectiveness.

Despite these contributions, several limitations of the UFACI framework were identified:

• The UFACI accuracy depends on indicator data quality, particularly social capital measures. Some indicators employed proxies that may not capture all dimensions of community resilience. Moreover, indicators currently reflect static conditions and may not account for future climatic or urban developmental changes. Potential data collection errors may arise from subjective judgments or inconsistencies in measurement methods. For example, institutional capacity assessments—such as counting the number of disaster-related organizations—can vary depending on classification criteria and reporting practices. Similarly, reported rates of storm and flood insurance coverage may be based on self-reported data, which can overestimate actual policy enrollment due to misunderstandings or intentional overreporting. These errors can propagate through the UFACI calculation, leading to biased estimates of adaptive capacity. Furthermore, variations in data sources, collection periods, and survey methods may introduce additional uncertainty.
• Indicator selection and membership function thresholds involve expert judgment, introducing potential subjectivity. Although sensitivity analyses and literature-based justifications mitigated this, ongoing refinement is needed, including the possible inclusion of new indicators such as ecological factors (e.g., green space ratio).
• This study assessed only 12 drainage areas, limiting generalizability on a city-wide scale. Applying the UFACI to different urban contexts may require calibration to accommodate varied socio-economic and infrastructural characteristics.
• Adaptive capacity evolves with changing infrastructure conditions and social preparedness. The UFACI presented here offers a snapshot evaluation and thus requires regular updates and integration with scenario analyses to remain policy-relevant.

In conclusion, the UFACI effectively integrates environmental and social data with high levels of uncertainty, enabling the identification of vulnerable areas and the prioritization of targeted actions. This study emphasizes a comprehensive approach to urban flood management, highlighting the importance of improving social capital and risk perception in addition to investments in economic resources and infrastructure. The findings suggest that areas with a relatively low UFACI require additional attention and tailored measures. In particular, the drainage areas managed by the Jegi 1 and Geumho rainwater pumping stations, which have garnered significant public and media attention, should be leveraged as opportunities to implement practical improvements. Furthermore, areas like the Bangbae rainwater pumping station’s drainage zone, where institutional measures have already been enacted, must strengthen additional flood prevention strategies through sustainable disaster management systems. By analyzing UFACI rankings within social and policy contexts, this study provides actionable directions for enhancing urban flood adaptive capacity. It contributes to developing long-term region-specific flood prevention strategies.

Future research aims to enhance the generalizability and reliability of the UFACI by expanding the evaluation scope to include all drainage areas managed by rainwater pumping stations across Seoul. This will enable a more refined analysis that accounts for diverse regional characteristics. In the current study, equal importance was assigned to each evaluation indicator; however, future work will involve a detailed analysis of the impact of each indicator on urban flood adaptive capacity, establishing a UFACI derivation framework with varying weights for different indicators. This approach is expected to produce a more detailed and precise UFACI. Additionally, a sensitivity analysis of the fuzzy rules will be conducted to assess the impact of different rule configurations on the UFACI results. Such analysis will further validate the robustness of the fuzzy inference method and identify which rules significantly influence the adaptive capacity scores, thus enhancing the interpretability and credibility of the index. Moreover, the fuzzy rules employed in the UFACI derivation process will be further refined to ensure they are based on clear and logical grounds, thereby improving the reliability of the results. Ultimately, this study aims to connect UFACI development with other studies on urban flood vulnerability and resilience quantification, thereby contributing to the establishment of a comprehensive urban flood response system. This work offers practical directions for urban flood prevention strategies, management, and adaptive capacity enhancement, aiming to inform long-term disaster prevention policy development and strengthen the overall capacity to manage and adapt to urban flooding.