Systematic Review for Urban Flood Disaster in Managerial Perspective: Forecasting, Assessment and Optimization

Abstract

Urban flood disaster management is an interdisciplinary field that integrates hydrology, geology, engineering, and urban planning, with prediction, assessment, and optimization serving as its core components. However, a comprehensive and systematic synthesis of recent developments in this domain remains limited, constraining both theoretical understanding and practical advancement. To address this gap, this study conducts an in-depth analysis of urban flood management research as a systematic review, with a particular focus on advances in prediction, assessment, and optimization. Utilizing a multistep holistic review, combining bibliometric and scientometric analysis with structured literature categorization, the research critically examines and synthesizes relevant findings. This study analyzed 166 research papers related to urban flood management within the Web of Science database. Through co-citation and keyword co-occurrence analyses, five dominant research dimensions are identified: physics-based simulation methods, data-driven approaches, risk assessment tasks, optimization strategies, and miscellaneous emerging topics. Based on these insights, we propose a task-oriented framework that systematically integrates prediction, assessment and optimization across the four phases of disaster management: mitigation, prevention, emergency response and recovery. This framework aids scholars and practitioners in understanding and implementing effective techniques and strategies. The study’s findings shed light on key trends and potential future directions, providing a roadmap for further exploration of urban flood management and guiding professionals in related fields.

1. Introduction

The inundation of urban areas due to flooding has become a prevalent risk worldwide. In contrast to other natural hazards, floods exhibit a higher frequency of occurrence and inflict more substantial cumulative damage. From 2000 to 2019, a substantial body of global records indicates that 3254 flood events transpired, comprising 44% of the total disaster occurrences during this timeframe. These flood events had a profound impact, affecting approximately 1.65 billion people, resulting in an estimated 100,000 fatalities, and causing staggering economic losses estimated at around USD 6.51 trillion [1]. Urban centers function as dual hubs for population aggregation and infrastructure systems, demonstrating an intricate interconnection between their spatial configurations and socio-economic dynamics. Urban systems, composed of interconnected components such as transportation, water supply, energy, communication, and disaster resilience, are vulnerable to systemic disruptions when any single component fails [2]. Thus, urban systems exhibit heightened vulnerability when confronted with flood disasters.

According to the IPCC Sixth Assessment Report, anthropogenic climate change has already led to increases in the frequency and intensity of heavy precipitation in many regions, exacerbating flood risks in urban settings [3]. Complementarily, the UNDRR Global Assessment Report underscores the growing systemic risks and cascading impacts posed by urban floods, particularly in rapidly urbanizing areas [4]. Under typical circumstances, urban flood disasters are primarily attributed to two interrelated factors: rapid urbanization and the escalating impact of climate change [5]. Rapid urbanization frequently coincides with inadequate drainage systems and improper land use planning, which are key contributors to the high vulnerability of urban areas to flooding [6]. Global climate change increases the likelihood of cities experiencing flood disasters [2], including threats such as sea-level rise induced by the greenhouse effect, directly impacting coastal cities [7,8]. Extreme weather events lead to more frequent occurrences of heavy rainfall, localized precipitation, sudden onset, and unpredictability, ultimately resulting in river flooding [9], flash floods [10], and urban inundation [5,11,12,13]. Combined with high urban vulnerability and increased flood probability due to climate change, the risk of urban flood disasters is on the rise [14]. Consequently, effective urban flood disaster management has garnered widespread attention in academia [15].

Urban flood risk evaluation is a key task in urban flood disaster management [16]. Its purpose is to quantitatively and qualitatively assess the likelihood of floods and waterlogging disasters in cities, revealing potential sources of risk, the severity of hazards, and the possible impact areas [17]. Taking Fuzhou City (China) as an example, X. Wang et al. [18] constructed an urban waterlogging vulnerability evaluation system from three dimensions: exposure, sensitivity, and adaptive capacity. Yuan et al. [19] introduced the use of convolutional neural networks to predict high-resolution urban flood depths, enabling comprehensive evaluation of economic losses based on the attributes of flooded locations. Han et al. [20] presented travel times under ideal evacuation conditions to quantify evacuation vulnerability. Through urban flood risk evaluation, the level of flood risk in different regions and scenarios can be quantified, key prevention targets and defense areas can be identified, and scientific basis can be provided for decision-making in urban flood disaster management.

In order to reduce the risks of urban flood disasters, pre-disaster flood control measures are another significant research focus. Flood control measures in coastal cities typically include constructing seawalls, raising embankments, building breakwaters, and strengthening embankment slopes to withstand the onslaught of marine disasters such as tsunamis, storm surges, and tidal waves [7]. Cities along rivers need to consider building embankments, reinforcing river channels, planning flood process control zones, and installing floodgates [6,21]. Mountainous cities focus on preventive measures against geological disasters such as mountain floods and landslides, including building sediment barriers, setting up warning systems, and reinforcing mountainsides [22]. In flat areas, urban inundation occurs when heavy rainfall exceeds the performance of urban drainage systems. Corresponding flood control measures include building rain gardens, improving drainage systems, and enhancing urban greening [23,24]. However, further validation of the effectiveness of flood control measures involves research on urban flood simulation methods and evaluation index systems.

Urban flood simulation is the foundation of urban flood risk evaluation and flood control measures, as it can simulate future flooding to identify urban flood risk areas and validate the effectiveness of flood control measures such as levee construction, sponge city design, and drainage system design. However, urban flooding is a complex process involving rainfall, runoff, convergence, and watershed dynamics, making urban flood simulation a challenging and complex task [7]. Significant efforts have been made over the past few decades to improve the accuracy and efficiency of urban flood simulation models [25]. Schubert and Sanders [10] compared the accuracy and efficiency of building modeling methods including the building-resistance method, building-block method, building-hole method, and building-porosity method. Chen et al. [26] utilized a coupled rainfall water management model (SWMM) and shallow water model (SWM) to study the response of urban floods to rainfall patterns, aiming to avoid flood risks during urban design and planning stages, and mitigate risks during maintenance phases. Gallien et al. [7,8] used a nonlinear two-dimensional Godunov-type finite volume model of shallow water equations to simulate urban inundation considering wave action, aiming to determine wave protection and urban drainage network schemes.

Significant progress has been made in various aspects of urban flood generation, impact evaluation, and measures optimization. However, existing research mainly focuses on the study of single measures or methods for urban flooding, without fully integrating classical disaster management theories. There is a lack of clear elucidation of the interrelationships among the various elements of simulation, modeling, prediction, evaluation, and flood control scheme formulation in the methodology of urban flood disaster management. A systematic research framework for urban flood disaster management has not yet been established. This represents a significant gap in the current research landscape, necessitating in-depth analysis and exploration of the research framework for urban flood management. On the theoretical level, a systematic review on the interrelationships among various methods and processes form the foundation of establishing a more comprehensive theoretical framework for urban inundation emergency management. At a practical level, the comprehensive synthesis and integration of diverse methodologies and processes contribute significantly to the advancement of systematic, scientifically sound, and operationally efficient urban inundation emergency management. This, in turn, boosts the effectiveness of urban flood disaster prevention and control measures.

Urban flood management spans multiple disciplines including engineering, data science, planning, and environmental studies and encompasses highly heterogeneous types of research. Given the current research gaps, establishing a systematic research framework for urban flood disaster management has become the primary research question. This study systematically reviews existing literature in the field of urban flood disaster management to develop an integrated research framework. By combining scientometric analysis with thematic synthesis, the study identifies five key research dimensions, namely physics-based simulation methods, data-driven approaches, risk assessment tasks, optimization strategies, and miscellaneous emerging topics. This research aims to establish a task-oriented framework that connects prediction, evaluation, and optimization across all phases of the disaster management cycle. This framework systematically integrates forecasting, assessment and optimization across the four phases of disaster management—namely mitigation, prevention, emergency response and post-disaster recovery to provide actionable insights for future academic inquiry and practical applications. It serves as a reference for urban planners, policymakers, and emergency managers in designing more adaptive and resilient urban flood strategies.

2. Background of Urban Flooding Disaster Management

Despite significant improvements in humanity’s capacity to manage floods through economic and technological advancement, flood disasters have become increasingly catastrophic. It is evident that flood risk is not merely a hydrological or engineering issue, but rather a complex, interdisciplinary and cross-system challenge. In managing urban flooding disasters, the perspective must shift from traditional, singular engineering or technical approaches towards a holistic, dynamic flood risk management model involving multiple stakeholders [27].

Research on the management of various types of disasters has become mature and is mainly divided into three related concepts: the disaster management cycle, disaster operations management, and disaster risk management.

The disaster management cycle refers to dividing disasters into different stages [28], which remains the most widely referenced, with varying stage granularity across models—ranging from two-stage [29,30], four-stage [31], to six-stage frameworks [32]. These models generally divide disaster management into phases such as mitigation, preparedness, response, and recovery. While initially conceptualized as a linear or cyclical process, recent critiques argue that these phases often overlap in practice and operate concurrently [33,34].

Disaster operations management is defined by [35] as a series of activities carried out before, during, and after a disaster to prevent loss of life, minimize its impact on the economy, and restore normalcy. Disaster operations management can be seen as further research based on the disaster management cycle. Authors of [36,37] point out that the occurrence and impact of disasters are stochastic and independent, requiring dynamic, real-time, effective, and cost-efficient solutions, which belong to the research field of management and operations research.

Disaster risk management similarly developed based on the disaster management cycle, and it was not until the 1970s that governments institutionalized risk management processes and practices [38]. Risk evaluation is a key part of disaster risk management, including vulnerability, exposure, resilience, etc.

Although disaster management theory has matured, the integration of these concepts into urban flood disaster management remains relatively underdeveloped. Price and Vojinovic [39] were among the first to propose the concept of urban flood disaster management, emphasizing proactive approaches that include historical data analysis, flood modeling, damage assessment, and risk evaluation—implicitly linking simulation, assessment, and optimization tasks. In recent years, especially in flood-prone countries like Malaysia, scholars have explored more structured frameworks. For example, Shariff and Hamidi [40] proposed a Flood Risk Management framework encompassing risk assessment, strategy implementation, and policy feedback loops. Saad et al. [41] aligned urban flood management with the Sendai Framework for Disaster Risk Reduction, which advocates risk understanding, governance, resilience, and preparedness. Muzamil et al. [42] adapted the four-phase model specifically for flood management, identifying tasks such as flood prediction, vulnerability analysis, risk mapping, and planning.

In general, the goal of urban flood disaster management is to systematically coordinate and manage all parties involved in every stage of urban flooding to reduce the impact of disasters, including timely warning, real-time data acquisition, rational evaluation of losses, determination of evacuation plans, and effective management of emergency supplies [43,44]. Considering simplicity, effectiveness, and practicality, the widely accepted and commonly used four-stage theory [31] is chosen to divide the urban flood disaster management cycle. In urban flood disaster management, the mitigation stage aims to prevent disasters and mitigate their impacts; the preparedness stage involves early warning and monitoring of disasters to reduce their impact; the response stage is to take swift actions to address emergencies when disasters occur; and the recovery stage focuses on implementing long-term measures to mitigate the impact of future disasters. The specific management contents of each stage are shown in

Table 1. The Four-Stage Theory of Urban Flood Disaster Management Cycle.

Stages	Objectives	Contents
Mitigation	Preventing the occurrence of disasters and mitigating their impacts.	Prevent
		Low impact development
		Sponge City
Preparedness	Providing early warning and monitoring of disasters to reduce their impact.	Predict
		Monitor
		Warn
Response	Taking swift action to address emergencies when disasters occur.	Emergency supply
		Response
		Control
		Decision
		Coordinate
Recovery	Implementing long-term measures to mitigate the impact of future disasters.	Recovery
		Restoration
		Reconstruction

.

Risk management and operations management are two key tasks in the urban flood disaster management process. These tasks play different roles in mitigation, preparedness, response, and recovery stages. Therefore, different methods should be selected to execute these tasks according to the characteristics of each stage to ensure the optimal effectiveness of urban flood management. To refine the framework mentioned above, it is necessary to review the research progress in urban flood disaster management, which helps understand mainstream academic research directions and future research opportunities.

3. Methodology

3.1. Proposed Methodology

In this study, a multistep holistic review approach is introduced to identify research themes in urban flood disaster management. This method integrates the review steps from disaster management and resilience research by Zeng [44], H. Xu [45], and Tong [46], as depicted in Figure 1. Initially, the background of urban flood disaster management is analyzed to clarify the keywords for retrieval. Subsequently, advanced searches are conducted on the Web of Science platform, followed by the screening of duplicates, invalid entries, irrelevant items, and articles exceeding predefined criteria. Next, VOSviewer (version 1.6.18) software is utilized for bibliometric analysis of the organized data, clustering co-citations and keywords to visually reveal the structural relationships among various components in the field of urban flood disaster management such as the paper by Van Eck & Waltman [47]. Clustering was performed using VOSviewer for both co-citation analysis and keyword co-occurrence analysis, with the resolution parameter set to 1.0. Furthermore, each paper’s content is subjected to categorization analysis, and based on the clustered knowledge map results, the main research dimensions are identified. The study concludes by summarizing the current research themes, methodologies, existing challenges, and future research opportunities in urban flood disaster management. Finally, integrating disaster management theories, a research framework for urban flood disaster management is proposed to elucidate the relationships among tasks such as simulation, prediction, evaluation, and optimization, providing valuable insights for future research in this field. This study follows the PRISMA framework methodologically, integrating scientometrics clustering with disaster management theory. It consciously couples quantitative knowledge mapping with domain-specific theoretical frameworks to identify dominant research dimensions, thereby constructing a framework for urban flood research.

3.2. Literature Search and Selection

To ensure transparency and reproducibility, the literature selection process adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (see Supplementary Materials) [48,49]. The procedure comprised four key stages: identification, screening, eligibility assessment, and inclusion. The initial search yielded 296 articles, which were screened based on relevance to urban flood disaster management and filtered through eligibility criteria. After removing duplicates, non-peer-reviewed studies, ecological-focused research, and regional-scale studies irrelevant to urban pluvial flooding, a final set of 166 articles was retained for synthesis.

In the realm of urban flood disaster management, this study initially employs the disaster management cycle theory, as proposed by McLoughlin [31], to examine its four stages: mitigation, preparedness, response, and recovery. Web of Science is an internationally recognized high-quality literature database, particularly well-suited for systematic review analyses. The period after 2005 marked a paradigm shift in urban flood research, with the emergence of modeling and data-driven tools. Therefore, an advanced search was conducted on the Web of Science database, utilizing the following search criteria for the period 2005–2025: (TI = ((“urban*”) OR (“city”) OR (“cities”))) AND (TI = (flood* OR waterlogging* OR inundation*)) AND (TI = (prevent* OR predict* OR monitor* OR warn* OR response* OR recovery*)), as shown in

Table 2. Keyword search list.

List	Keyword
List1-Geographical Scope	urban, city, cities
List2-Disaster Type	flood, waterlogging, inundation
List3-Disaster Management Methods	prevent, predict, monitor, warn, response, recovery

. This search resulted in a total of 296 articles, including peer-reviewed journal articles, conference papers, technical reports, publications from governmental and international organizations, and foundational theoretical works. The most recent search was executed in August 2025 reflecting the extensive literature on urban flood management.

After deduplication and exclusion of review articles, a total of 260 papers were initially retained. A thorough examination of abstracts and annotation of research subjects and content led to the removal of 6 papers unrelated to urban flooding (n = 6). The excluded papers mainly focused on the ecological effects, changes, and impacts during flood events, such as how tree species respond to floods [50], microbial exposure risks in urban floodwaters [51], the influence of riverbed bacterial communities in urban floods [52], the impact of floods on sandy ecosystems [53], and the interactions and roles of various forces outside disasters [54].

After annotating the remaining papers, it was observed that the research scales varied. They were categorized from large to small as region, urban area, and neighborhood. Regions refer to areas covering over 100 square kilometers and including several administrative districts, such as the Pearl River Delta, Yangtze River Delta, and Xianyang region [55]. Urban areas encompass the entire urban area of a single city, typically ranging from 20 to 100 square kilometers. Neighborhoods, on the other hand, represent relatively independent parts of urban hydrology within urban areas, with an area usually less than 20 square kilometers. This categorization was made because different research scales have distinct disaster triggers, problem characteristics, and research methods. For instance, research focusing on urban flooding disasters at the urban area and neighborhood scales often involves extreme precipitation as the main trigger, with a primary focus on effective research on urban drainage systems, including drainage pipes, urban rainfall, and land-use infiltration. In contrast, for regional urban flooding disasters, the main triggers typically include tsunamis, river floods, and flash floods, where urban drainage systems are no longer the primary influencing factors. Therefore, papers focusing on regional areas were excluded (n = 88), resulting in 166 papers remaining. The exclusion rules are shown in

Table 3. Exclusion rules.

Exclusion Criteria	Number of Exclusions
Review Articles	36
unrelated to urban flooding	6
focusing on regional areas	88

.

Figure 2 presents the annual distribution of research papers, revealing a steady upward trend. Notably, there has been a significant increase since 2021, with a consistent rise in publications each year. This surge suggests a growing recognition among scholars of the relevance of these studies, particularly in light of the rising frequency of extreme weather events, such as temperature extremes and heavy precipitation. It is plausible to expect that this heightened interest will propel the number of papers to new heights by 2025.

3.3. Literature Synthesis Approach

To ensure a rigorous and meaningful synthesis of the selected literature, this study adopted a hybrid approach combining quantitative scientometric techniques with qualitative content analysis. After identifying 166 eligible articles through the PRISMA-guided bibliometric screening, a two-stage synthesis process was conducted:

(1)

Thematic Clustering via Scientometric Mapping: Co-citation analysis and keyword co-occurrence networks were constructed using VOSviewer to reveal latent structures and topic clusters within the field. These clusters represented dominant research dimensions such as physics-based simulation models, data-driven simulation models, risk evaluation tasks, and optimization tasks.

(2)

Qualitative Thematic Analysis: Each cluster was then examined through in-depth reading of the associated core articles. Key themes, methodological paradigms, research objectives, and findings were manually coded and compared. This allowed for identifying methodological patterns, knowledge gaps, and interrelationships across research domains.

By combining algorithmic clustering with domain-specific reading and interpretation, the study ensured both objective mapping and context-sensitive integration of insights, thus achieving a comprehensive synthesis of urban flood disaster management research.

4. Scientometric Analysis

4.1. Co-Citations Analysis

Compared to literature coupling, co-citation [56] is a dynamic relationship that changes with the publication of new papers, reflecting the contributions of articles to different research developments in the field. In this study, the co-citation network was constructed using the following parameters: minimum number of citations = 6, minimum total link strength = 1, and resolution parameter = 1.0, resulting in 20 remaining articles. The co-citation knowledge graph is depicted in Figure 3, while the most influential articles in the field of urban flood management are presented in

Table 4. Most influential papers in urban flood management.

Title	Cluster	Total Link Strength	Citations
An ensemble neural network model for real-time prediction of urban floods (Berkhahn et al., 2019) [57]	1	46	12
Depth prediction of urban flood under different rainfall return periods based on deep learning and data warehouse (Z. Wu et al., 2020) [12]	1	33	12
Cities and Flooding: A guide to integrated urban flood risk management for the 21st Century (Jha et al., 2012) [58]	1	14	9
A deep convolutional neural network model for rapid prediction of fluvial flood inundation (Kabir et al., 2020) [59]	1	28	8
Development and Comparison of Two Fast Surrogate Models for Urban Pluvial Flood Simulations (Bermúdez et al., 2018) [60]	1	27	7
Beyond ‘flood hotspots’: Modelling emergency service accessibility during flooding in York, UK (Coles et al., 2017) [61]	2	12	7
River flow forecasting through conceptual models part I-A discussion of principles (Nash & Sutcliffe, 1970) [62]	1	19	7
Flood inundation modelling: A review of methods, recent advances and uncertainty analysis (Teng et al., 2017) [63]	1	24	7
Evaluating the impact and risk of pluvial flash flood on intra-urban road network: A case study in the city center of Shanghai, China (Yin et al., 2016) [64]	2	15	7

.

This co-citation network reveals the knowledge structure and theoretical foundations within urban flood research by analyzing shared citation relationships among studies. It comprises 20 studies cited at least 6 times in the analyzed dataset and 102 co-citation links. Connections between nodes represent co-citation relationships, where two studies appear simultaneously in each other’s reference lists. Node proximity reflects thematic similarity, with closer-proximity studies exhibiting stronger conceptual connections. Link thickness indicates co-citation strength. VOS clustering identified two distinct research clusters: the red cluster comprises 14 studies primarily focused on urban flood modeling, risk assessment, and management strategies; the green cluster includes 6 studies emphasizing hydrological processes and flood simulation methodologies.

4.2. Co-Occurrence of Keywords Analysis

Keyword analysis is widely used to identify research field hotspots [65]. This study applied knowledge mapping methods to organize and chart data extracted from the included sources of evidence. From each included study, major research hotspots in the field of urban flood management were obtained as key variables for constructing the knowledge graph: cluster, total link strength, occurrences, average publication year, and citations. Since this study covered all articles related to the entire process of urban flood management in urban and suburban areas, the articles covered a wide range of topics and were studied by scholars from various regions and disciplines. This co-occurrence network extracted 649 unique terms from 112 studies, comprising 391 author keywords and 295 index keywords. Link weights were calculated using a fractional counting method, accounting for the number of keywords in each document to prevent high-frequency terms from dominating the network. To ensure semantic consistency, manual normalization was performed. After excluding keywords unrelated to disaster emergency management or with unclear significance, such as “network”, “lid”, “som”, “size”, etc., and standardizing keywords with similar meanings, such as “spatial optimization” and “optimization”, “support vector machine” and “machine learning”. The keyword co-occurrence network was generated with a minimum occurrence threshold of 2, minimum total link strength of 1, and a resolution parameter of 1.0. Synonymous terms were manually merged prior to analysis to enhance semantic consistency, resulting in 38 keywords as shown in Figure 4 and listed in

Table 5. Most Frequent Keywords in urban flood management.

Label	Cluster	Total Link Strength	Occurrences	Avg. Pub. Year	Citations
flood	3	130	46	2019	25.76
model	3	105	30	2020	26.53
risk	1	125	29	2019	27.03
impact	1	92	27	2020	23.33
rainfall	4	81	22	2020	20.59
simulation	3	71	18	2020	27.11
management	1	68	18	2020	20.94
climate change	1	50	15	2019	32.00
neural network	2	39	14	2022	10.07
inundation	2	46	13	2020	37.31
hydraulic model	3	35	11	2018	41.27
vulnerability	1	44	11	2020	20.18
prediction	2	32	11	2021	14.64
assessment	1	28	9	2017	41.67
waterlogging	2	23	9	2022	8.00
drainage system	3	35	8	2019	37.13
resilience	1	32	8	2020	25.88
pluvial flood	1	46	8	2021	18.88
city	1	34	8	2022	14.00
precipitation	4	22	7	2017	26.86
machine learning	2	27	7	2022	14.86

.

According to Figure 4, blue nodes represent physics-based model for urban flood simulation, including modeling and simulation of floods, drainage networks, and runoff. They occupy a central position in the knowledge graph, serving as the foundation and core of other research hotspots. Green nodes represent data-driven simulation methods, including neural networks, machine learning, and deep learning. They are closely linked to blue nodes as they rely on physical urban flood simulation models to obtain data. Red nodes represent risk evaluation tasks in the urban flood management process, including resilience, vulnerability, accessibility, and other indicators. Yellow nodes include prediction, design, and optimization, serving as optimization tasks in the urban flood management process.

This study aims to more effectively determine the current status and direction of urban flood disaster management research. Based on keyword co-occurrence knowledge mapping, existing articles are categorized into five dimensions:

(1)

Physical simulation methods for urban flood simulation, which involve modeling and simulation of floods, drainage networks, and runoff based on physical principles;

(2)

Data-driven methods for urban flood simulation, including machine learning and deep learning methods;

(3)

Risk evaluation tasks in the urban flood disaster management process, focusing on constructing risk indicator systems for different processes of urban flood disasters, including vulnerability, accessibility, resilience, and emergency capacity; and

(4)

Optimization tasks in the urban flood disaster management process, emphasizing the research on perception, design, and optimization strategies and schemes; and

(5)

Other research, which consists of studies from diverse fields using alternative methods, such as flood governance, urban double repair, flood disaster support ontology, system dynamics, and theory of planned behavior. These papers are excluded from Figure 4 for clarity and will be separately examined in subsequent discussions. The distribution of research in these categories, as per statistical analysis, is as follows: 17% for physical simulation, 31% for data-driven methods, 21% for risk evaluation, 16% for optimization, and 16% for other literature.

5. Research Frontiers and Opportunities

Through a comprehensive analysis of the literature, gaps and research opportunities in each aspect were identified. Please see

Table 6. The current research themes, challenges and opportunities in urban flood disaster management.

Research Dimensions	Current Research Themes	Research Challenges and Opportunities
1 Physics-based models for urban flood simulation	Fundamental model development Integration of multiple models to account for diverse flood factors Applications include designing urban flood control schemes and early warning systems	Striking a balance between computational speed and precision Enhancing model versatility and portability Integration with digital twin technology
2 Data-driven models for urban flood simulation	Rapid forecasting Real-time prediction	Striking a balance between computational speed and precision Addressing the ongoing challenges unique to data-driven approaches, including managing large volumes of data, ensuring model interpretability, and mitigating risks of overfitting Innovative integration methods for combining data-driven models
3 Risk assessment tasks for urban flood disaster management	Diverse entities: cities, urban subsystems, communities, families, individuals, and programs Aspects: emergency response capabilities, disaster losses, vulnerability, resilience, and risk.	Establishing a unified evaluation index system Formulating standardized loss quantification methods Accounting for the disaster’s dynamic nature Broadening the scope of evaluation tasks in both temporal and spatial dimensions
4 Optimization tasks of urban flood disaster management	Monitoring: rainfall, water level, water area, network data Optimization: land use, maintenance strategies, real-time control of drainage pipes	Building a disaster early warning system based on multi-source data fusion Optimization framework integrating knowledge from multiple subject areas

for details. These classifications will be introduced in the following sections.

5.1. Physics-Based Models for Urban Flood Simulation

5.1.1. Literature Synthesis

This dimension includes 29 papers, and the primary research objectives along with representative works are summarized in Table 7. Physics-based models for urban flood simulation can be classified into three categories, as shown in Figure 5, according to their application maturity:

The first category is fundamental single hydrodynamic models, such as 1D Saint-Venant equations and 2D nonlinear shallow water equations [66], which serve as the cornerstone for physics-based models for urban flood simulation. Study on such models focuses on examining the movement of water itself from a hydraulic perspective, as well as the interaction between water and buildings [67], and so forth.

The second category consists of laboratory models for urban flooding, such as the storm water management model (SWMM) coupled with various sub-models including rainfall-runoff, surface runoff, and pipe flow. Due to their free availability and ease of extension, this category has the largest number of related studies. These models are primarily used to simulate urban flooding scenarios and analyze the different roles of various elements in the process of urban waterlogging based on their simulation results, such as the importance of temporal rainfall variability relative to spatial rainfall variability [67,68].

The third category comprises commercial models for urban flooding, such as MIKE and InfoWorks, which further incorporate one-dimensional pipe and two-dimensional runoff models, making them more complex and closer to reality. As commercial models, they aim to enhance usability but may sacrifice scalability. These models primarily provide the foundation for future urban flood control planning, sponge city design, and low-impact development. For instance, they support urban flood control and planning beyond standards [69], evaluate different levels of urban development and low-impact development schemes [70], wave protection schemes [7,71], watershed protection schemes [72], flash flood warnings [73], and integration and warning of rainfall, flood, and disaster information [2].

Table 7. Representative Papers on Physics-Based Models for Urban Flood Simulation.

Research Objectives	Representative Papers
Enhancing data quality	Improving the accuracy of rainfall forecasts for hydrological applications (Yoon, 2019) [74] Investigate the potential of radar rainfall nowcasting in predicting flood events (Thorndahl et al., 2016) [75] Using a uniform grid of 624 × 550 units with a high resolution of 1 m (Bai et al., 2021) [76]
Model optimization	Applying the SWMM-LISFLOOD coupled model (Z. Zhao et al., 2024) [77] Determining hydrological model parameters using intelligent algorithms (Liao et al., 2019) [78]
Improve model computational efficiency	Developing a relatively coarse grid in the 2D ground surface flow model (L. Wu et al., 2022) [66] Developing the GPU parallel computing technology to improve computing efficiency (X. Li et al., 2022) [79] Impact of different building modeling approaches on model efficiency (Schubert & Sanders, 2012) [10]

Table 7. Representative Papers on Physics-Based Models for Urban Flood Simulation.

Research Objectives	Representative Papers
Enhancing data quality	Improving the accuracy of rainfall forecasts for hydrological applications (Yoon, 2019) [74] Investigate the potential of radar rainfall nowcasting in predicting flood events (Thorndahl et al., 2016) [75] Using a uniform grid of 624 × 550 units with a high resolution of 1 m (Bai et al., 2021) [76]
Model optimization	Applying the SWMM-LISFLOOD coupled model (Z. Zhao et al., 2024) [77] Determining hydrological model parameters using intelligent algorithms (Liao et al., 2019) [78]
Improve model computational efficiency	Developing a relatively coarse grid in the 2D ground surface flow model (L. Wu et al., 2022) [66] Developing the GPU parallel computing technology to improve computing efficiency (X. Li et al., 2022) [79] Impact of different building modeling approaches on model efficiency (Schubert & Sanders, 2012) [10]

5.1.2. Challenges and Opportunities

The primary challenge faced by Physics-based models for urban flood simulation is the delicate balance between computational efficiency and accuracy. Urban flood simulation typically involves fundamental elements such as rainfall, land, and drainage networks, as well as additional factors like waves, rivers, and flash floods. The physical simulation of urban flooding is characterized by its high complexity and computational demands. Thus, achieving precise predictions of urban inundation situations, including inundation points, depths, areas, and temporal variations, is crucial at a technical level.

To enhance the accuracy of models, future endeavors can focus on improving data quality and optimizing models. Firstly, enhancing data quality entails various approaches, including the utilization of authentic rainfall data, prediction future rainfall [73,75,80], simulating rainfall data [81,82], integrating multiple rainfall simulation models [74,83], predicting urban land use, employing high-precision digital elevation models [76], and incorporating real-time monitoring data. Under these strategies, the significance of historical data for model optimization becomes particularly pronounced [39]. Secondly, model optimization encompasses methodologies such as refining urban building modeling, integrating multiple models [77,79] enhancing models for various scenarios such as river and urban model integration [84], selecting resolutions for multiscale models [73], and optimizing model parameters [78].

To improve model computational efficiency, research can explore avenues such as constructing rapid models, grid simplification [66], utilizing different numerical computation methods, multiscale modeling [85], model fusion [26], and leveraging physical means such as GPUs for acceleration [79].

In addition, future research topics of interest include ensuring the generality and transferability of models, applying them to watersheds that have not been measured or suffer from poor measurements. Expanding the simulation scope and selecting appropriate levels of generalized models according to the scale covered by the model can strike a balance between accuracy and computational efficiency. Combining digital twin technology with physical models can achieve more comprehensive and accurate simulation and prediction of urban flood disasters, thus enhancing the efficiency and reliability of urban flood disaster management.

5.2. Data-Driven Models for Urban Flood Simulation

5.2.1. Literature Synthesis

This dimension comprises 51 articles. The primary research objectives and corresponding representative works are presented in

Table 8. Representative Papers on Data-driven models for urban flood simulation.

Research Objectives	Representative Papers
Predicting urban flooding using single-value output	Presenting an Artificial Neural Network (ANN) based model for the prediction of maximum water levels during a flash flood event (Berkhahn et al., 2019) [57] Developing an ANN model to predict cumulative overflow volumes, based on simulation results generated by SWMM (H. I. Kim & Han, 2020) [86] Presenting a CNN model for the prediction of maximum water levels (Guo et al., 2021) [87] Presenting a Gradient Boosted Decision Tree for predicting flood depth (Z. Wu et al., 2020) [12] Developing a Light Gradient Boosting Machine model to predict maximum depth, based on simulation results generated by PCSWMM (K. Xu et al., 2023) [88]
Predicting time series of urban flooding	Presenting a LSTM model for generating three-hour urban flooding predictions (Nguyen & Bae, 2020) [89] Using CNN and LSTM to predict urban flood depth (J. Chen, Li et al., 2023) [90] Employing a GRU model optimized via simulated annealing for hourly urban rainfall-inundation depth prediction (Yan et al., 2023) [91]

.

The papers in this field are relatively new, with publication dates concentrated after 2019, and more than two-thirds of them were published after 2023. This indicates that urban flood simulation methods based on machine learning and deep learning are currently a focal point in the research on urban flood disaster management. One possible reason is that machine learning and deep learning methods have already achieved significant success in other fields.

Data-driven models exhibit a strong connection with physics-based models, as their data sources frequently originate from the latter, as depicted in Figure 6. Owing to the scarcity of real-world data, the majority of data-driven simulation techniques function as secondary replicas of physical models [92], reflecting their reliance on physical principles. The pioneering work by [93] employed a Bayesian model to address the challenge of prediction and modeling unmeasured or inadequately measured watersheds. Since then, conventional machine learning techniques have been increasingly employed in urban flood simulation, such as multiple regression [94], random forest [92], gradient boosting decision trees [12], logistic regression [95], XGBoost [96] and K-means clustering [97], among others. However, these methods blur the spatial relationships between the predicted grids. Therefore, deep learning methods, represented by convolutional neural networks (CNNs) [87,98,99], have been used for urban flood simulation.

Compared to physics-based models, which simulate the dynamic states of multiple elements involved in the urban flood process at each time step, the aforementioned data-driven studies exhibit a significant limitation: most focus solely on predicting the peak inundation depth within a region, thereby lacking the capacity to support real-time decision-making and control during flood events. To address the need for temporal modeling, recurrent neural networks (RNNs) have been widely adopted. In particular, Long Short-Term Memory (LSTM) networks [100] and Gated Recurrent Units (GRUs) [91,101] have shown promising performance in capturing the sequential evolution of urban inundation levels.

5.2.2. Challenges and Opportunities

Data-driven models also focus on enhancing accuracy and efficiency. Therefore, extracting effective features [102] and predicting features such as rainfall [103] play a crucial role in effectively predicting urban flooding. Particularly, data-driven models for urban flood simulation exhibit significant advantages in computational efficiency, enabling predictions of urban flooding over larger areas in less time and with higher efficiency. Consequently, overcoming the low computational efficiency of physical models stands as a prominent research focus in this domain.

Data-driven models not only strive for enhanced accuracy and efficiency but also focus on model expansion, interpretability, transferability, and credibility. As a large number of data-driven approaches have been applied to urban flood simulation, the inherent limitations of data-driven approaches have become apparent. Data-driven approaches, such as deep learning, necessitate substantial data availability [104], often exhibit model interpretability challenges [95,96], and can be sensitive to data size and structure [104], with a tendency to yield larger errors in time series predictions [86]. Overfitting [57] is a concern that requires further investigation, and ensemble models [105] are employed to address instability. Moreover, integrating physically based and data-driven simulation methods [85,92,106] presents a promising area for future research.

5.3. Risk Evaluation Tasks for Urban Flood Disaster Management

5.3.1. Literature Synthesis

This dimension comprises 35 papers. Figure 7 provides a comprehensive overview of the research landscape by summarizing the evaluation methods, target objects, and indicator systems employed in these studies. Representative works, categorized by evaluation method, are listed in

Table 9. Representative Papers on risk evaluation tasks for urban flood disaster management.

Evaluation Methods	Research Topic and Representative Papers
Entropy weight method	Evaluating urban public service emergency response capabilities (Y. Zhang, Li et al., 2022) [107] Predicting regional water accumulation risks under different urban heavy rain scenarios (J. Zhang, Li et al., 2023) [13] Assessing Fuzhou’s vulnerability and predict its future development (X. Wang et al., 2023) [18]
Depth-destruction function	Estimating direct and indirect losses during flood events in urban areas (Albano et al., 2014) [108] Comprehensive assessment of economic losses (H. Yuan et al., 2024) [19] Analysis of successive flood events for recoverability (Guimarães & Miguez, 2020) [14] Examining the various damage states (Gallegos et al., 2012) [109]
Dijkstra shortest path	Quantifying evacuation risk in terms of evacuation time (Z. Han et al., 2023) [20] Assessing the spatial accessibility of emergency response to key public services in cities (Y. Zhang, Li et al., 2022) [107] Optimizing the distribution of emergency stations and developing strategic emergency plans for vulnerable populations and facilities (Yin et al., 2021) [110]
Graph theory	Reveal accessibility disparities and identify vulnerable communities (Gangwal & Dong, 2022) [111]
Expert scoring method	Predicting social, physical and economic resilience before floods occur (Cui et al., 2022) [23]
Analytic hierarchy process	Flood disaster mitigation and emergency response in urban watersheds (Levy et al., 2007) [112] Evaluating the performance of LID practices in urban flood control and emission reduction (Hua et al., 2020) [5]

.

A limited number of studies, such as [112], who developed a hierarchical analysis index system for urban flood risk evaluation, considering safety, economic, infrastructure, and environmental aspects, and [23], who employed expert scoring and BP neural network techniques to evaluate urban resilience from social, natural, and economic viewpoints, predominantly focus on qualitative evaluations at a macro level. Reference [113] developed an index system encompassing five critical aspects: driving indicators (precipitation), pressure indicators (water level at river channels), status indicators (depth of inundation), impact indicators (damage caused by urban flood disasters), and response indicators (urban flood control measures). In contrast, the majority of research centers on micro-level quantitative evaluations. These studies utilize urban flood prediction results as the basis, using indicators such as maximum water depth, inundation area, flow velocity, etc., as foundational data. They employ tools like depth-damage functions [19] and flood velocity-damage functions [109] to assess the risks and impacts of urban flooding on cities. This cluster’s contribution to urban flood prediction research differs by emphasizing the development of evaluation index systems and the determination of appropriate evaluation methodologies, rather than merely replicating existing studies.

The evaluation scope in this field encompasses a diverse array of targets, including cities [112], urban subsystems [114], communities [115], households [67], individuals [116], low-impact development schemes [5], as well as public attention and perception of risk [117].

Regarding evaluation indicators, the research is rich and varied. Studies primarily evaluate emergency response capabilities [118], disaster damage [19,108,113], vulnerability [6,18,119], resilience [14,114,120], and risk [13,115,116,119]. Emergency response capabilities mainly include the accessibility of public services, emergency response capabilities, evacuation time, etc. Disaster damage is evaluated from an economic perspective and is generally divided into direct and indirect losses, also referred to as tangible and intangible losses. Direct losses include losses of life, property, and assets, while indirect losses refer to economic losses caused by city road closures, etc. Vulnerability refers to the degree of susceptibility to damage when facing disaster risks, indicating the ability of urban systems or socio-economic systems to resist disasters. Resilience is typically defined as the adaptability, recovery, and rebuilding capabilities of systems when faced with external pressures or impacts. Risk is a comprehensive evaluation of the probability of disaster occurrence and potential losses. In terms of the disaster management cycle, vulnerability and risk evaluations occur during the mitigation and preparedness phases, emergency response capability evaluations occur during the response phase, and disaster losses occur during the recovery phase. Resilience is relevant throughout the entire disaster management process [6]. However, the current trend in related research is to use scenario analysis and urban flood simulation predictions as tools to evaluate the vulnerability, losses, and emergency response capabilities of various urban objects before disasters occur, which is more in line with practical societal needs.

5.3.2. Challenges and Opportunities

Quantifying disaster losses, particularly in urban flood events, remains challenging due to the lack of standardized methods. Current research employs depth-damage [108] and flow velocity-damage functions [109], but their limited applicability across diverse geographical, climatic, and urban contexts restricts their universal use. Measuring indirect losses, which involve complex economic and social aspects, poses an even greater challenge. To evaluate urban emergency response capacity, it is vital to anticipate future urban changes, continually refine evaluation models, and adapt emergency plans promptly. Disaster dynamics [110,121], such as transportation networks and human traffic, should be incorporated into evaluation factors to ensure efficient urban resilience. The abstract concepts of vulnerability and resilience [114] lack unified definitions and evaluation methodologies, necessitating future research on standardizing indicator selection criteria.

Urban flood disaster evaluation primarily relies on scenario analysis [13], which is grounded in flood simulation methods. Enhancing simulation accuracy is crucial for accurate risk evaluation and response planning, necessitating continuous model refinement and data improvement. To broaden the scope, urban flood risk evaluation should extend beyond a single event to encompass the entire disaster process and multiple consecutive events, considering frequency, intensity, and spatial extent. The evaluation targets should expand from isolated facilities to the comprehensive urban system, encompassing infrastructure, buildings, communities, economic activities, and more. This expanded approach better reflects the city’s risk profile, supporting sustainable development.

5.4. Optimization Tasks of Urban Flood Disaster Management

5.4.1. Literature Synthesis

With representative works listed in Table 10, this dimension encompasses 25 scholarly contributions, which focus on aspects such as monitoring, optimization, and control. The field of optimization in urban flood management is relatively nascent, with a significant concentration occurring in recent years, signifying its emerging status as a prominent research focus. The current research on optimization tasks aims to mitigate and prevent urban flooding: Before disasters occur, optimization efforts include optimizing impermeable surface areas to reduce runoff in urban renewal schemes [24,122,123] and finding optimal and adaptive maintenance, repair, and overhaul (MRO) strategies for stormwater retention basins [124]. During disaster events, the focus of current research is on real-time control of drainage pipes and reservoirs [125,126].

To achieve real-time control during disaster events, monitoring of urban flooding is crucial. Pressure sensors [127] and radar water level sensors [128,129] are commonly used for monitoring water depth and flow velocity, while acoustic sensors [130] and tipping bucket rain gauges [128] are used for rainfall monitoring. Furthermore, cloud platforms are utilized as data storage tools [131]. In addition to research on traditional sensors, current studies focus on using computer vision to research water depth [132], inundation area [133], and flood height [134]. Furthermore, data fusion from multiple sources is also a current research focus. Ref. [135] integrated heterogeneous sensor systems to provide disaster information to stakeholders. In addition to this, crowdsourced data [136], social media data [137,138], and road traffic data [139] are widely used in urban flood monitoring. The selection of monitoring points is also a topic worthy of research [140].

Table 10. Representative Papers on Optimization tasks of urban flood disaster management.

Representative Papers	Research Topics	Research Methods
(Sun et al., 2023) [125]	Reduce peak tank outflow	linear programming
(Z. Zhang, Tian et al., 2023) [126]	Leveraging infrastructure to mitigate sewer overflows (CSOs) and urban flooding	Decentralized control strategy for multi-agent reinforcement learning
(Chang et al., 2018) [127]	Development of wireless water level monitoring system for urban drainage floods	Pressure Sensor
(Peleg et al., 2023) [130]	Low-cost acoustic sensor detects rainfall	Low-cost acoustic sensor, short-term early warning
(Hong & Shi, 2023) [135]	Integrating heterogeneous sensor systems to provide disaster information to stakeholders	Multiple data fusion
(R.-Q. Wang et al., 2018) [137]	High-resolution monitoring of urban flooding using social media and crowdsourced data	Natural language processing; computer vision;
(H. Han et al., 2021) [133]	Automatic monitoring method for urban road flooding	YOLOv2
(J. Zhao et al., 2024) [123]	Optimizing the spatial layout of impervious surfaces	Nondominated Sorting Genetic algorithm 2 (NSGA2), and Multiple Linear Programming (MLP) algorithm

Table 10. Representative Papers on Optimization tasks of urban flood disaster management.

Representative Papers	Research Topics	Research Methods
(Sun et al., 2023) [125]	Reduce peak tank outflow	linear programming
(Z. Zhang, Tian et al., 2023) [126]	Leveraging infrastructure to mitigate sewer overflows (CSOs) and urban flooding	Decentralized control strategy for multi-agent reinforcement learning
(Chang et al., 2018) [127]	Development of wireless water level monitoring system for urban drainage floods	Pressure Sensor
(Peleg et al., 2023) [130]	Low-cost acoustic sensor detects rainfall	Low-cost acoustic sensor, short-term early warning
(Hong & Shi, 2023) [135]	Integrating heterogeneous sensor systems to provide disaster information to stakeholders	Multiple data fusion
(R.-Q. Wang et al., 2018) [137]	High-resolution monitoring of urban flooding using social media and crowdsourced data	Natural language processing; computer vision;
(H. Han et al., 2021) [133]	Automatic monitoring method for urban road flooding	YOLOv2
(J. Zhao et al., 2024) [123]	Optimizing the spatial layout of impervious surfaces	Nondominated Sorting Genetic algorithm 2 (NSGA2), and Multiple Linear Programming (MLP) algorithm

5.4.2. Challenges and Opportunities

Currently, urban flood monitoring datasets predominantly rely on water depth measurements from isolated stations, which fail to provide a comprehensive representation of flood conditions. Consequently, the strategic selection of monitoring stations to effectively integrate data into a comprehensive urban flood prediction framework represents a substantial challenge [141]. Social, crowdsourced, and traffic data, characterized by lag, are difficult to directly use for future urban flood prediction. Although integrating these data as supplements to other monitoring data can provide a more comprehensive understanding of urban flooding, offering a more accurate basis for formulating optimization plans, the heterogeneity, complexity, and vastness of data still present challenges in the research field of multi-source data fusion [137]. With the deepening research into urban flood disaster simulation, prediction, and evaluation, optimization tasks have become a focal point in urban flood disaster management research, requiring the integration of knowledge from multiple disciplines such as hydrology, meteorology, urban planning, and civil engineering to address issues such as low-impact development schemes, flood control plans, selection of public infrastructure, as well as optimization of drainage strategies, real-time control of drainage pipelines, and evacuation process optimization during disaster occurrence.

5.5. Other Research on Urban Flood Disaster Management

In addition to the four prominent research clusters, there are 26 papers falling outside the conventional categorization. Some of these papers use their unique approaches to study the entire process of urban flood disaster management, employing methods like empirical research [142], system dynamics [143], and economics [144], offering distinct perspectives and insights. Other papers focus on more specialized aspects of urban flood disaster management, addressing specific subfields. These include studies on the relationships between disaster management entities using ontologies [145] and holistic management theory [146], spatial-temporal visualization of road inundation [147], human-centered flood risk strategies [148], understanding and influencing driver behavior [149], Identifying different postures of humans submerged in floodwaters [150], and evacuation strategies [151].

6. Research Framework for Urban Flood Disaster

In Section 5, a thorough examination of urban flood simulation models, risk evaluation tasks, and management optimization tasks was conducted, with a particular focus on the analysis of methodologies within each research cluster. This examination delved into the current status, challenges, and potential opportunities. Moving forward, the paper will explore the interconnectedness of three key subtasks of urban flood disaster management: simulation, evaluation, and optimization, thereby contributing to the development of the research framework initially outlined in Section 2.

6.1. Three Subtasks in Urban Flood Disaster Management

Drawing on disaster management theory and a review of existing research, the urban flood disaster management cycle is intrinsically composed of three interconnected subtasks: prediction, evaluation, and optimization. The prediction task, in practical application, employs urban flood simulation techniques, whether they are physics-based or data-driven models, to predict key parameters such as flood extent, duration, water depth, and flow velocity. The evaluation task centers on constructing a multi-dimensional indicator system to provide an in-depth understanding of the current situation. Optimization, on the other hand, revolves around the development and refinement of strategies related to urban planning, water resource management, real-time response, and control. By examining the disaster management cycle, one discerns distinct objectives at each stage, which in turn dictate the nature of the prediction, evaluation, and optimization tasks.

These tasks necessitate tailored methods that align with their respective stage-specific objectives, as depicted in Figure 8. Specifically, Figure 8a illustrates the distinct management objectives associated with each phase of the disaster cycle. Figure 8b further outlines the corresponding objectives of the three core tasks—prediction, evaluation, and optimization—within each phase. Finally, Figure 8c presents the methodological approaches used to fulfill these task-specific objectives across different stages.

6.2. The Relationship Between Prediction, Evaluation, and Optimization

Having established the framework for urban flood disaster management, the subsequent discussion will focus on the relationship among prediction, evaluation, and optimization tasks. The prediction task serves as the cornerstone of urban flood disaster management, providing data support for evaluation and optimization tasks. The evaluation task relies on the output of the prediction task to identify and quantify the potential impacts of flood disasters on various aspects of the city, including its residents, environment, and economy. The evaluation results furnish feedback and guidance for the optimization task. By evaluating the actual impacts of flood disasters on various aspects of the city, existing issues and areas for improvement can be identified. This process provides a basis and reference for the formulation of optimization schemes, aiming to minimize the occurrence and impact of disasters to the greatest extent possible. In summary, the relationship among prediction, evaluation, and optimization tasks in the four stages of urban flood disaster management is illustrated in Figure 9.

In urban flood disaster management, the tasks during the mitigation and recovery phases involve the long-term management of potential flood disasters that have not yet occurred. Therefore, actions such as urban planning, land use changes, and the implementation of sponge flood control projects can be undertaken. At these stages, the objective of optimization tasks is to balance flood control costs with potential disaster losses, determining the optimal solution with the minimum overall cost. The evaluation task determines the potential losses that may arise from disasters. During these phases, there is a low requirement for timeliness but a high requirement for accuracy in prediction tasks. Therefore, it is necessary to select urban flood simulation methods based on physical modeling, as illustrated in Figure 9 Cycle 1.

In urban flood disaster management, the tasks during the preparedness and response phases involve the short-term management of imminent flood disasters. Therefore, when significant changes in urban structure are not feasible, optimization tasks primarily focus on how to implement mitigation measures to ensure the safety of affected populations and property to the greatest extent possible. This includes actions such as real-time control of drainage pipelines, response to sudden rainstorms, and formulation of evacuation plans. During these phases, evaluation tasks need to be based on predicted flood data to evaluate the emergency capacity of urban public service facilities. At these stages, the timeliness of prediction tasks becomes more crucial. Hence, real-time monitoring data such as rainfall, water depth, flow velocity, and network status are inputted into data-driven approaches to predict data such as urban flood depth, as illustrated in Figure 9 Cycle 2.

It is important to note that Figure 8 and Figure 9 present a conceptual framework based on the four-stage disaster management cycle. In practical urban flood management, however, core tasks—particularly simulation and optimization—often transcend individual phases. These tasks are frequently deployed in multiple stages simultaneously or cyclically, depending on evolving flood dynamics and system requirements. This reflects a well-known critique of the traditional disaster management cycle: that disaster phases are not strictly sequential, but rather dynamic and overlapping in real-world scenarios. Therefore, the framework should be understood as an abstract representation of dominant task-stage relationships, rather than a rigid or linear progression.

7. Conclusions

This study demonstrates that despite methodological diversity in urban flood disaster management research, a systematic review can be organized around three core tasks: prediction, assessment, and optimization. These tasks correspond to distinct phases within the disaster management cycle. By integrating bibliometric mapping with disaster management theory, we reveal dominant research dimensions: physics-based simulation models, data-driven simulation models, risk assessment tasks, optimization tasks, and various emerging themes. Building upon this foundation, we propose a task-oriented systematic framework that provides a comprehensive guiding perspective for both academic research and practical applications.

Based on the systematic framework, we offer the following actionable recommendations to policymakers and urban stakeholders to support evidence-based urban flood resilience planning:

(1)

Prediction

Use physics-based models for long-term mitigation and recovery planning—such as sponge city design and land-use regulation. Deploy data-driven models in preparedness and response phases for real-time forecasting and early warning under extreme rainfall.

(2)

Evaluation

Establish unified metrics for vulnerability, resilience, and economic loss quantification across departments (urban planning, emergency management, infrastructure). Integrate scenario-based risk assessments into development approvals and disaster drills.

(3)

Optimization

Prioritize optimization of drainage networks and low-impact development schemes using multi-objective algorithms. Develop dynamic control systems that use real-time monitoring data (rainfall, water level, traffic) to guide evacuation routes and reservoir operations during emergencies.

Despite its comprehensive scope, this review has several limitations. First, the proposed framework is constructed based on the disaster management cycle, which has itself been critiqued for oversimplifying the dynamic and overlapping nature of disaster phases. Second, the scientometric analysis relies on clustering performed by VOSviewer, where parameter settings and normalization choices are sensitive. Alternative clustering methods may yield different results. Dependence on the Web of Science database introduces potential biases: inadequate coverage of non-English literature and regional technical reports may weaken geographical representativeness. Additionally, the systematic review excluded regional-scale flood studies, narrowing the scope to urban storm flooding. While this enhances focus on urban resilience, it overlooks insights from larger hydrological systems affecting urban areas during extreme events. Third, as artificial intelligence tools continue to evolve, the landscape of scientific literature reviews is likely to shift, with automated knowledge discovery and real-time synthesis becoming increasingly feasible. Future reviews should embrace these developments while maintaining critical interpretation and domain-specific insight.