A Comprehensive Systematic Review of Contemporary Geospatial Approaches to Flood Hazard and Risk Assessment

Abstract

Due to climate change and its increased variability, as well as the extreme weather events, flooding is becoming a major natural threat causing profound economic, social, and ecological impact. This paper systematically reviews 89 peer-reviewed articles published between 2010 and 2024 on flood risk assessment approaches, including geospatial techniques and methods for flooding, using the PRISMA framework and the ScienceDirect and Web of Science databases. GIS and remote sensing are the most popular tools for flood hazard mapping, and hydrodynamic models such as HEC-RAS and MIKE FLOOD dominate flood simulation. Machine learning algorithms, multi-criteria decision analysis (MCDA), and climate scenario analysis have also emerged as increasingly prominent methodological contributions to flood risk frameworks. This review makes a novel contribution by providing the first systematic synthesis of geospatial flood risk assessment methods, explicitly quantifying both the urban–rural research imbalance and the degree of hazard, vulnerability, and exposure integration across the literature. Specifically, only 13 (2.7%) of all eligible articles addressed rural flooding, despite the profound socioeconomic impacts that disproportionately affect these communities, and only 16% of included studies integrated any combination of hazard, vulnerability, and exposure components within current assessment approaches. This review highlights the importance of interdisciplinary collaboration and sensitivity to rural contexts in cultivating resilience and fostering equitable flood risk management.

1. Introduction

Floods are among the most devastating water-related natural hazards, occurring when water inundates land that is normally dry due to a variety of hydrological, meteorological, and anthropogenic drivers. Understanding the distinction between flood hazard, the physical probability and intensity of a flood event, flood vulnerability, the susceptibility of communities and infrastructure to flood damage, flood exposure, the presence of people, assets, and systems within flood-prone areas, and flood resilience, the capacity of a system to absorb, recover from, and adapt to flood impacts, is fundamental to developing effective forecasting, prevention, and adaptation strategies.

Floods can be characterized into multiple types, such as riverine, flash, and urban floods, each with distinct causes and impact [1]. Hundecha et al. [2] categorized floods according to meteorological drivers, catchment state or hydrograph shape. These categories include three groups of rainfall driven floods and two groups of snowmelt-induced floods. Floods can also be categorized based on their primary causes, including natural drivers such as rainfall patterns, snowmelts and storm precipitation [3]; anthropogenic factors such as urbanization, land use changes [4] and agricultural practices [5]; and climate change, which is increasingly recognized as a key driver of more severe and frequent flood events worldwide [6,7].

The consequences of flooding are well documented globally. In Asia, recurrent floods across India, China, and Southeast Asia have caused widespread loss of life and billions in economic damages, exposing critical limitations in early warning systems and spatial risk mapping [8,9,10]. In Europe and North America, major flood events have similarly revealed persistent gaps in floodplain delineation, vulnerability assessment, and the integration of socioeconomic data into risk frameworks [11,12,13]. Crucially, these events share a common challenge: existing assessment approaches have struggled to fully capture the spatial complexity of flood risk, partly due to limited data availability, inadequate integration of socioeconomic factors, and the underutilization of geospatial tools, particularly in rural and data-scarce environments.

Recent advancements in geospatial techniques have significantly enhanced flood monitoring, hazard assessment and vulnerability analysis. These techniques integrated geospatial data and analytical models to identify flood-prone areas, assessing risk levels and developing mitigation strategies [14,15,16].

Techniques for flood evaluation have advanced greatly with remote sensing (RS) and geographic information systems (GIS) becoming tools because they can collect and interpret extensive spatial data instantly [17,18,19]. These techniques facilitate flood mapping, hazard zone identification, and damage assessment by integrating diverse data such as rainfall, elevation, and population density [20,21]. Similarly, hydrological, and hydraulic models, like HEC-RAS and HEC-HMS, are essential for simulating flood dynamics, particularly in river basins, providing critical insights for floodplain management and ecological assessments [22,23].

Progress in machine learning (ML) and artificial intelligence (AI) is revolutionizing flood risk evaluations by allowing real-time forecasts and continuous learning from updated information [24,25]. These approaches excel in flood forecasting, vulnerability mapping, and damage estimation, supporting data-driven decision-making [26]. Criteria decision analysis (MCDA) methods have improved flood assessments by combining various elements like socioeconomic and environmental metrics to accurately identify and prioritize areas most at risk [27,28]. While these methods have demonstrated significant utility, challenges remain in addressing data limitations, model uncertainties, and integrating socio-environmental factors.

The policy imperative for more comprehensive flood risk assessment is firmly established by the Sendai Framework for Disaster Risk Reduction (2015–2030), which explicitly calls for multi-dimensional risk assessments that integrate hazard, exposure, and vulnerability analyses [29]. The Framework’s emphasis on reducing underlying risk factors and strengthening resilience across all community types, urban and rural alike, directly motivates the need for systematic evaluation of how well current geospatial methodologies meet these objectives. This study is positioned as a direct response to that call, examining whether existing flood risk assessment approaches adequately operationalize the Sendai Framework’s integrated risk perspective.

While several reviews have examined flood risk assessment methodologies, none has systematically focused on the landscape of geospatial methods as a distinct analytical category, nor explicitly compared the urban–rural divide in research coverage. Existing reviews tend to either address broad disaster risk management or focus narrowly on specific tools such as hydrodynamic modeling, leaving a gap in understanding how the full spectrum of geospatial approaches, from RS and GIS to ML and MCDA, are being applied, and where critical blind spots remain. This review addresses that gap directly.

This study aims to review these advancements, their applications, and the remaining gaps, aiding in the creation of thorough flood risk evaluation frameworks. It integrates ideas and methods of flood hazard analysis into a whole and offers a summary of recent studies concerning flood hazard assessment, exposure, vulnerability, and risk assessment. It investigates the time and location emphasis of research and analyzes the ways flood risk is understood. It also aims to analyze the evolution of flood risk by examining the main outcomes of flood risk analysis, the methodologies employed, and the key factors influencing flood risk over time. This review seeks to enhance understanding of current flood risk assessment approaches and to identify future directions for research and policy development in comprehensive flood management.

2. Methodology

A detailed and structured approach was applied to identify, select, and analyze the literature related to flood hazard, vulnerability and risk assessments. The review followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to ensure transparent, reproducible, and comprehensive reporting, thereby enabling a systematic capture of methodological patterns and key advancements in flood assessment, with a strong emphasis on spatial technologies and contemporary analytical frameworks.

2.1. Search Strategy

The literature search was conducted across two major databases as shown in

Table 1. Search criteria used to find relevant articles from the selected databases.

Search Criteria	Database	Number
(ALL = (“Flood risk assessment” OR “Flood hazard assessment” OR “flood vulnerability assessment”)) AND ALL = (“Geospatial methods” OR “spatial methods” OR “Spatial techniques” OR “GIS” OR “Remote sensing” OR “Machine learning” OR “ML” OR “Multi-criteria” OR “Multicriteria” OR “MCDA” OR “Hydrolog” OR “Hydraulics”) AND PY = (2010–2024), filtered by language: English or French, document type: Article	Web Of Science	4381
ALL(“Flood risk assessment” OR “Flood hazard assessment” OR “Flood vulnerability assessment”) AND ALL(“Geospatial methods” OR “GIS” OR “Remote sensing”) AND NOT ALL(“hurricane” OR “prediction”), filtered by language: English or French	Science Direct	538

: Web of Science and ScienceDirect. These sources were selected due to their rigorous academic indexing, multidisciplinary scope, and access to high-quality peer-reviewed content [30,31,32]. All searches were restricted to peer-reviewed journal articles published in English or French between 2010 and 2024. This period was specifically selected to reflect advancements in flood assessment techniques, including the advent of machine learning methods for flood prediction and vulnerability analysis, as well as the introduction of high-resolution satellite imagery such as Sentinel-1 and Landsat-8 [25,26] and the adoption of open-access geospatial platforms [20]. The Sendai Framework (2015) further reinforced the relevance of this period by promoting multi-dimensional disaster risk assessments integrating hazard, vulnerability, and exposure [29].

This search yielded to a total of 4919 records. Additional exclusion terms, covering unrelated hazard domains such as earthquakes, landslides, glaciers, drought, erosion, and cyclones, were applied during the screening phase rather than at the search stage to preserve the breadth of initial retrieval while maintaining thematic relevance.

2.2. Screening Criteria and Exclusion Justification

The screening process adhered to a rigorous framework to minimize selection bias, maintain the standards and meet our research objectives. The following exclusion criteria were made deliberately: gray literature, including reports, theses, and institutional documents, was excluded due to variability in methodological rigor and the challenges it poses to reproducibility and quality assessment. Studies published in languages other than English or French were excluded, as these represent the only languages in which the authors have sufficient proficiency to ensure accurate interpretation of technical methodological content.

Studies were shortlisted when their title and abstract included one or more relevant keywords such as “Flood Hazard,” “Exposure,” “Vulnerability,” “Flood Risk,” “Susceptibility,” “Flood Mapping,” “Spatial Techniques,” “Geospatial Methods,” “GIS,” “Remote Sensing,” “MCDA,” “Modeling,” and “ML.” This ensured thematic alignment with this study’s objectives and guaranteed methodological rigor. Articles were then subjected to manual and automated screening to eliminate duplicates and records falling outside the defined scope, with further exclusions based on the relevance to flood hazard and risk assessment and the integration of geospatial and remote sensing techniques.

As illustrated in the PRISMA flow diagram (Figure 1), a total of 4919 records were initially identified from Web of Science (n = 4381) and ScienceDirect (n = 538). After removing 25 duplicates, 4894 records were screened based on title and abstract, of which 2996 were excluded as irrelevant. Of the 1898 records sought for retrieval, 1413 could not be accessed due to institutional restrictions or database unavailability, leaving 485 records assessed for full-text eligibility. Following full-text review, 396 studies were excluded for not employing geospatial methods, focusing on flood management rather than assessment or falling outside the defined objectives. A total of 89 articles were thus selected for the final synthesis.

2.3. Research Landsapce for Eligible Literature

To characterize the broader research landscape identified through the search process, the following figures present the distribution of the 485 articles assessed for eligibility by publication year, geographic location, research field, and flood type. This contextual overview situates the final 89 included studies within the wider body of flood risk literature and highlights overarching trends in the field prior to methodological synthesis.

As shown in Figure 2, the chronological spread of flood hazard, exposure, vulnerability, and risk evaluation studies shows a continuous growth in research efforts from 2010 to 2024. Particularly, a sharp upturn is seen starting in 2016, reaching a peak of 55 publications in 2023, highlighting an increased academic focus and acknowledgment of the significance of flood risk research in tackling the escalating issues brought by climate change and swift urban growth. This upward trend over the last decade highlights the accelerating evolution of methodologies and the emergent need to address the impacts of increasingly frequent and severe flood events.

Figure 3 displays the distribution of research publications centered on flood hazard and flood risk evaluation worldwide. The findings show a cluster of studies coming from China and the United States, with more than 70 and about 45 papers, respectively, highlighting these countries’ strong involvement in flood-related research and risk control. Additionally, nations like Italy, Germany, and England each provide input with roughly 25 publications each, demonstrating their important contributions to the development of flood assessment methodologies. Additional notable contributions are evident from India, France, and the Netherlands, each registering between 15 and 20 studies, further demonstrating an active global dialogue on flood vulnerability and preparedness.

Figure 4 displays the breakdown of publications concerning flood hazard and vulnerability analyses, showing a notable focus on specific research areas. Environmental Sciences and Water Resources together represent more than 200 publications each, highlighting the key influence of ecological and hydrological viewpoints in directing modern flood studies. A significant share of research also originates from Geosciences Multidisciplinary, reflecting involvement from disciplines and their expertise in understanding flood processes and related hazards. The cross-disciplinary nature of flood risk evaluation is underscored by the input from areas like Meteorology and Remote Sensing which collectively enhance the analytical resources and offer essential context regarding the complex causes and effects of flood occurrences. Additional involvement from areas like Imaging Science, Green Sustainable Science, and Civil Engineering further supports the integration of technological innovation and engineering approaches into flood risk management, promoting adaptive solutions and resilience planning.

Although various research domains are actively involved in flood hazard and vulnerability assessments, a noticeable unevenness in representation persists, echoing the previously observed geographic and thematic gaps. The prevailing emphasis on water and geoscience fields highlights their fundamental importance in flood research, while the comparatively lower representation of civil engineering and related disciplines reflects the fact that structural flood management approaches, including dikes, dams, and diversion systems, are already well established in practice. The subsequent development of green infrastructure and nature-based solutions has further demonstrated the recognized limitations of purely structural approaches. Greater integration of civil engineering expertise with emerging geospatial and data-driven methods therefore represents an opportunity to enhance the robustness, applicability, and real-world transferability of flood risk models.

Figure 5 illustrates the distribution of flood types across the eligibility pool, showing a clear dominance of riverine/pluvial floods (n = 153) and urban floods (n = 121). Rural floods remain notably underrepresented (n = 13), alongside groundwater floods (n = 2) and multi-hazard floods (n = 20), reinforcing the thematic gaps that motivate this review.

Together, Figure 2, Figure 3, Figure 4 and Figure 5 characterize the broader research landscape of flood risk assessment literature, revealing persistent geographic and thematic concentrations that contextualize the methodological synthesis presented in the following sections.

2.4. Data Extraction

A structured data extraction framework was applied consistently across all 89 included studies. Bibliographic information, including author(s), publication year, and geographic location, was recorded to map temporal trends and regional concentrations in flood research. Each study was classified by flood type (riverine, urban, rural, flash, pluvial, groundwater, or multi-hazard) and assessment category (hazard, vulnerability, exposure, risk, or integrated), enabling systematic comparison across the literature.

Methodological dimensions were captured in detail, including the geospatial tools and analytical methods employed (GIS, remote sensing, machine learning, MCDA, and hydrodynamic modeling), the specific software and models used (such as HEC-RAS, MIKE FLOOD, ArcGIS Pro 3.4, QGIS, and Google Earth Engine), and the key hazard and vulnerability indices applied (including FHI, SoVI, FVI, and LVI). Data sources and inputs, such as digital elevation models, satellite imagery, rainfall records, and socioeconomic datasets, were also documented to assess data availability and transferability across geographic contexts. Finally, each study was evaluated for the degree to which it integrated hazard, vulnerability, and exposure components into a unified risk framework alongside its main findings and acknowledged limitations.

The extracted data was categorized thematically to highlight understudied methodological areas, document current research trends, and identify directions for future development. Geographic patterns within the selected studies were also examined, revealing areas of concentrated research activity and regions where flood risk assessment remains underexplored, offering important insight into the generalizability of existing methodologies and persistent geographic gaps in research coverage.

Following the full screening and eligibility assessment process described above, 89 peer-reviewed articles met all inclusion criteria and were selected for final synthesis. These studies form the analytical foundation of the results presented in the following section, which examine flood assessment types, spatial techniques, methodological trends, and the degree of hazard, vulnerability, and exposure integration across the included literature.

2.5. Quality Assessment

A custom five-criterion quality checklist was developed and applied consistently across all 89 included studies, tailored to the specific demands of geospatial flood risk research. Each study was assessed on the following criteria: clarity of research objectives (C1); appropriateness of the geospatial method to the stated aim (C2); transparency of data sources and inputs (C3); validation of results through quantitative metrics or field verification (C4); and explicit acknowledgment of limitations (C5). Each criterion was scored 0 or 1, yielding a maximum possible score of 5. Studies scoring 5 were classified as high quality, scores of 3–4 as moderate, and scores below 3 as low.

Of the 89 included studies, 40 (44.9%) were rated high quality and 49 (55.1%) moderate quality, with no studies rated low quality, yielding an average quality score of 4.2 out of 5. The most common gap identified was result validation (C4), where studies described methodological frameworks without reporting explicit accuracy metrics such as ROC curves, AUC values, or field-based verification—a recognized challenge in geospatial systematic reviews. Critically, findings from high-quality studies were consistent with the overall synthesis, supporting the robustness of the conclusions drawn in this review regarding geospatial method distribution, the urban–rural research imbalance, and the limited integration of hazard, vulnerability, and exposure components. Full quality scores for all 89 included studies are provided in the Supplementary Materials.

Following the screening, eligibility assessment, and quality appraisal described above, 89 peer-reviewed articles met all inclusion criteria and were selected for final synthesis. These studies form the analytical foundation of the results presented in the following section, which examine flood assessment types, spatial techniques, methodological trends, and the degree of hazard, vulnerability, and exposure integration across the included literature.

3. Results

3.1. Statistical Analysis

Hazard assessment dominates flood assessment research, as shown in Figure 6, making up 37% of studies, followed by risk assessment at 20%. Vulnerability assessment accounts for 18%, while exposure analysis is less common at 9%, indicating a gap in understanding the spatial distribution of assets at risk. Integrated assessments, such as hazard and vulnerability (8%), hazard and exposure (5%), and vulnerability and exposure (3%), are less frequently covered; therefore, this distribution suggests a need for more holistic approaches that integrate hazard, vulnerability, and exposure to provide a comprehensive flood risk perspective.

Remote sensing and GIS-based methods are the most frequently used, accounting for 28% of the studies, as shown in Figure 7, underscoring their importance in spatial data acquisition and flood mapping. Both geospatial and multi-criteria decision analysis (MCDA) and hydrological and hydraulic modeling each account for 19%, indicating a balanced reliance on decision-support frameworks and water-flow simulations for assessing flood risk. A total of 15% of the methods are statistical and probability-based, indicating their role in probabilistic assessments. Although they are being used more frequently, machine learning techniques (13%) still make up a smaller portion. When considered collectively, this distribution shows a growing support for data-driven techniques like machine learning and a strong emphasis on hydrological and spatial approaches.

Figure 8 displays the distribution of spatial techniques used across various flood assessment types, including flood hazard assessment, exposure, vulnerability and risk assessment, flood hazard and vulnerability, and flood hazard and exposure. Each chart illustrates the frequency of specific methods such as geospatial and MCDA techniques, hydrological and hydraulic modeling, remote sensing and GIS-based methods, statistical and probability-based methods, and machine learning approaches within each assessment category, showing which techniques are most applied in different types of flood studies.

Flood hazard assessment depends significantly on GIS and remote sensing (RS) methods to identify flood-prone regions by examining spatial and environmental elements, like elevation, slope, and precipitation trends. Moreover, it typically uses modeling to replicate flood behavior and water movement. Flood risk assessment merges GIS and RS techniques to conduct analysis and mapping of flood risk by merging hazard and vulnerability information. This process often incorporates machine learning techniques, such as Random Forest and Support Vector Machine, to classify, predict, and rank areas at risk [33,34].

In the flood vulnerability assessment graph, multi-criteria decision analysis (MCDA) techniques play a primary role by prioritizing and combining various vulnerability factors, including social, economic, and environmental data, while statistical and probability-based methods quantify and assess the likelihood of these factors contributing to flood impacts. For the flood exposure analysis graph, we can say that this analysis primarily uses statistical and probability-based methods to evaluate the likelihood and extent of exposure for populations, infrastructure, and assets, with GIS and RS techniques also being essential for mapping the spatial patterns of exposed areas.

Three-Dimensional Cross-Comparison

Table 2. Three-dimensional cross-comparison: Flood type × assessment type × spatial technique (n = 89).

Flood Type	Assessment	RS & GIS	Hydrological & Hydraulic Modeling	Geospatial & MCDA	ML & AI	Statistical Methods	Climate & Scenario Analysis	Total
Riverine/Pluvial (n = 29)	Hazard	5	5	2	1	1	1	15
	Risk	1	1	3	3	—	1	9
	Vulnerability	3	—	1	—	—	—	4
	Exposure	1	—	—	—	—	—	1
Urban (n = 24)	Hazard	—	2	1	1	—	—	4
	Risk	1	—	2	—	—	—	3
	Vulnerability	3	—	5	1	2	1	12
	Exposure	—	—	—	1	—	—	1
	Integrated	—	1	2	—	1	—	4
Coastal (n = 15)	Hazard	3	—	—	1	—	1	5
	Vulnerability	4	—	1	—	—	1	6
	Exposure	3	—	—	—	—	—	3
	Integrated	1	—	—	—	—	—	1
Flashflood (n = 6)	Hazard	2	1	2	1	—	—	6
Multi Haz (n = 3)	Hazard	1	—	—	—	—	—	1
	Risk	1	—	—	—	—	1	2
Rural (n = 1)	Vulnerability	—	1	—	—	—	—	1
General/Other (n = 11)	Hazard.	1	1	—	1	—	—	3
	Risk	—	—	3	—	—	—	3
	Vulnerability	2	—	1	—	—	—	3
	Integrated	1	—	1	—	—	—	2
Total		33	12	24	10	4	6	89

The following section provides a descriptive analysis of the spatial methods and assessment indices identified across the 89 included studies, elaborating on their technical characteristics, applications, and comparative strengths.

presents a three-dimensional cross-comparison of flood type, assessment type, and spatial technique across the 89 included studies, revealing distinct methodological patterns for each flood context. Riverine and pluvial floods dominate the literature (n = 29, 32.6%), with hazard assessment as the primary focus (n = 15), relying heavily on hydrological and hydraulic modeling and RS and GIS approaches. Urban flooding (n = 24, 27.0%) shows a contrasting pattern, where vulnerability assessment predominates (n = 12), largely addressed through geospatial and MCDA and statistical methods, reflecting the socioeconomic complexity of urban flood risk. Coastal flooding (n = 15, 16.9%) is almost exclusively addressed through RS and GIS techniques (n = 11), with vulnerability and exposure analyses prioritized over hazard modeling, consistent with the spatial nature of sea-level rise and storm surge assessments. Flash floods (n = 6, 6.7%) are addressed exclusively through hazard assessment, drawing equally on hydrological modeling, MCDA, and RS and GIS, reflecting their rapid-onset nature. Integrated assessments combining two or more components appear most frequently in urban flood studies (n = 4), suggesting that urban contexts are more likely to adopt holistic risk frameworks. Most strikingly, rural flooding is represented by only a single study (n = 1, 1.1%), which focused on vulnerability using hydrological modeling, underscoring a profound and persistent gap in the literature with direct implications for equitable flood risk management.

3.2. Descriptive Analysis

3.2.1. Common Contemporary Spatial Methods for Flood Hazard Assessment and Their Applications

Table 3. Common contemporary spatial methods for flood hazard assessment and their applications.

Method Category	Description	Key Techniques and Tools	Applications	References
Remote Sensing and GIS-Based	Integrate satellite imagery and geospatial data (e.g., elevation, vegetation, drainage) to assess and map environmental and anthropogenic processes like flood risk. Remote sensing captures surface characteristics, while GIS analyzes data for decision-making, enabling detailed flood maps, land-use planning, and resource management.	ArcGIS, QGIS, ERDAS IMAGINE, ENVI 5, Google Earth Engine, Sentinel-1 SAR, Landsat, MODIS	Useful in emergency response planning, identifying exposure and vulnerable zones. Assessing the impacts of land-use changes on flood risk.	[36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68]
Hydrological and Hydraulic Modeling	Simulates water flow within watersheds and flood propagation over land and water bodies. Hydrological models focus on rainfall-runoff and watershed processes, while hydraulic models simulate the movement of water in rivers and floodplains using flow equations.	HEC-HMS, SWAT, WEAP (Water Evaluation and Planning), LISFLOOD, HYDRUS, MODFLOW, HEC-RAS, MIKE FLOOD, TUFLOW, Delft3D, FLO-2D, RiverFlow2D, Arc SWAT	Used for planning flood mitigation structures (e.g., dams, levees) and assessing impacts on urban areas.	[69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86]
Machine Learning and Artificial Intelligence (AI)	Employs algorithms for high-accuracy flood predictions based on spatial and environmental data, identifying critical factors contributing to flood risk through feature selection techniques.	Random Forest, XGBoost, SVM, Deep Learning (CNN, LSTM), TensorFlow, Scikit-Learn	Enhances predictive capabilities in flood-prone areas, supporting proactive measures in urban planning and infrastructure protection.	[87,88,89,90,91,92,93,94,95,96,97,98,99]
Statistical and Probability-Based Methods	Applies statistical distributions and probabilistic models to estimate flood probabilities and simulate possible flood scenarios, useful for long-term risk assessments.	Gumbel, Log-Pearson, Copula models, Monte Carlo simulations, R Python 4.5	Supports flood insurance calculations, long-term floodplain management, and climate adaptation planning by deriving exceedance probabilities for registered flood characteristics such as peak discharge, volume, duration, and hydrograph shape.	[100,101,102,103,104,105,106,107,108,109]
Geospatial and Multi-Criteria Decision Analysis (MCDA)	Integrates geospatial data with decision-making frameworks to prioritize flood-prone areas by weighing multiple criteria (e.g., rainfall, land use, elevation, population density) for systematic flood risk assessment.	Analytic Hierarchy Process (AHP), Fuzzy AHP, Weighted Sum Model (WSM), TOPSIS, GIS platforms (ArcGIS, QGIS), Python/R, Google Earth Engine	Applied in hazard zoning, exposure, and vulnerability mapping. Resource allocation to high-risk areas for flood preparedness and mitigation.	[110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126]
Climate and Scenario Analysis	Evaluates future flood risks based on climate change projections, incorporating global and regional climate models to assess impacts of changing precipitation patterns, sea-level rise, and extreme weather events on flood risks.	CMIP6 climate models, PRECIS (Providing Regional Climates for Impacts Studies), GCMs (Global Climate Models), RCP (Representative Concentration Pathways), GIS, Python (climate libraries)	Supports long-term planning for climate-resilient infrastructure, adaptation strategies, and scenario-based flood risk assessments in dynamic environments.	[127,128,129,130,131,132,133,134,135]

presents modern spatial techniques employed for flood hazard evaluation along with their uses. These approaches, such as sensing, GIS, hydrological simulation, and multi-criteria decision analysis (MCDA), are essential for converting hazard and vulnerability metrics into practical evaluations. Every method targets components of flood hazard analysis ranging from identifying flood-susceptible zones to assessing exposure and risk, offering researchers and decision-makers a range of tools to tackle different flood conditions and issues. Descriptive analysis plays a crucial role in systematic reviews by providing a comprehensive understanding of the data and context to interpret the application of the findings. It also helps summarize the characteristics of the data to set the stage for building meaningful conclusions [35]. This analysis is essential for demonstrating how contemporary spatial methods provide the foundational tools needed to translate complex hazard and vulnerability data into practical strategies for mitigating flood risks and addressing diverse challenges effectively. Additional details about these methods are provided in the Supplementary Materials.

Among the spatial techniques identified across the 89 included studies, machine learning and climate scenario analysis represent the most rapidly evolving methodological contributions, though their application remains comparatively limited. Machine learning approaches (n = 10, 11.2%) are most frequently applied in hazard and risk assessments for riverine and urban flooding, where algorithms such as Random Forest, XGBoost, Support Vector Machines, and deep learning models, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, demonstrate superior predictive accuracy compared to traditional statistical methods, particularly in non-linear and data-rich environments. Unlike conventional hydrological and hydraulic models, which rely on physically based equations and are computationally intensive, ML models derive patterns directly from observational data, producing faster predictions with comparable or higher accuracy, particularly in ungauged or data-sparse basins where calibrated physical models struggle. Their primary advantage lies in their ability to process large volumes of heterogeneous spatial and temporal data simultaneously, enabling real-time flood forecasting and continuous model improvement as new data becomes available. However, their application is largely concentrated in data-rich urban and riverine contexts, with virtually no presence in rural, coastal, or multi-hazard flood assessments, reflecting the data availability constraints that limit their transferability to underrepresented regions. Furthermore, ML models are often criticized for their limited interpretability, which can hinder their adoption in policy and emergency planning contexts where transparent decision-making is essential.

Climate and scenario analysis methods (n = 8, 9.0%) draw on global and regional climate models, including CMIP6, RCP pathways, and GCMs, to project future flood risks under different emissions scenarios. Their effect differs fundamentally from both ML and traditional GIS-based approaches: while ML optimizes predictions based on historical patterns and GIS methods map current spatial conditions, climate scenario analysis explicitly projects forward in time, quantifying how flood frequency, intensity, and spatial extent may shift under future climate trajectories. This forward-looking dimension makes it uniquely valuable for long-term infrastructure planning and climate adaptation policy, particularly in riverine and multi-hazard contexts where planning horizons extend decades ahead. However, climate scenario analysis introduces greater uncertainty than present-focused approaches, as projections are sensitive to emission pathway assumptions and model resolution, limitations that become especially pronounced at local scales where flood risk decisions are ultimately made. It also remains underutilized in vulnerability and exposure assessments, where its integration could substantially strengthen the long-term resilience planning dimension of flood risk frameworks. While both methods show clear methodological promise, their combined representation of only 20% of included studies suggests that the field has not yet fully transitioned toward data-driven and climate-informed approaches. The choice between them is fundamentally context-dependent: ML is best suited for near-term operational forecasting in data-rich environments, while climate scenario analysis is indispensable for strategic long-term planning under uncertainty, and their integration represents a promising but largely unexplored frontier in comprehensive flood risk assessment.

3.2.2. Key Hazard and Vulnerability Indices for Flood Assessments

Table 4. Key hazard and vulnerability indices for flood assessment.

Index	Purpose	Indicators	Applications	References
Flood Hazard Index (FHI)	To measure the potential hazard level in flood-prone areas	Flood depth, duration, frequency	Used to identify high-risk flood zones and prioritize hazard mitigation strategies	[113,136,137]
Social Vulnerability Index (SoVI)	Measures social vulnerability to environmental hazards, including floods, by combining socioeconomic factors	Age, income level, disability, race/ethnicity, education, housing quality	Identifies at-risk populations within flood-prone areas; applied in urban and rural contexts for flood resilience planning and resource allocation	[121,138,139]
Flood Vulnerability Index (FVI)	Designed for flood events; assesses vulnerability by combining social, economic, environmental, and physical indicators	Population density, economic dependency on agriculture, proximity to water bodies, infrastructure resilience	Adaptable to local conditions for diverse environments; used in planning and prioritizing areas for flood mitigation and disaster response	[59]
Livelihood Vulnerability Index (LVI)	Evaluates flood vulnerability by assessing livelihood factors, focusing on flood-sensitive economic sectors	Employment diversity, access to credit, social support networks, food and water security, housing durability	Particularly useful in rural or agrarian communities; helps prioritize interventions where livelihoods are closely tied to flood-sensitive resources	[70,72,73,126,140,141,142,143,144,145]
Composite Vulnerability Index (CVI)	Combines various indicators based on regional needs, offering a holistic view of vulnerability	Housing quality, public health infrastructure, road density, literacy rate, poverty level	Adaptable for diverse flood-prone areas; allows cross-regional comparison and local customization in vulnerability assessment	[59]
Resilience and Adaptive Capacity Indices	Assesses community resilience and adaptive capacity to flooding, considering recovery and adaptation potential	Access to emergency funds, insurance coverage, education, healthcare services, social cohesion, self-organization	Useful for assessing long-term community resilience to recurrent flooding; guides investments in adaptive strategies and support systems	[116,132,146,147,148,149]
Flood Risk Index (FRI) & Urban Flood Risk Index (UFRI)	To provide a composite score combining hazard, exposure, and vulnerability for comprehensive risk. To evaluate flood risk in urban areas with unique infrastructure and population densities.	Hazard, exposure, and vulnerability indices. Impervious surfaces, drainage systems, urban density, building types.	Broad application in flood risk assessments for policymaking and risk management. Used by urban planners for flood mitigation in cities with high population density and infrastructure.	[117,150,151,152,153,154]

displays hazard and vulnerability metrics frequently utilized in flood risk evaluation. This table includes indicators designed to evaluate flood hazards and examine vulnerability components offering metrics that support an evaluation of flood risk. These indicators generally combine environmental and socioeconomic aspects, enabling researchers and policymakers to pinpoint high-risk zones and distribute resources effectively for flood mitigation. Indices such as the Flood Vulnerability Index (FVI), which integrates environmental and physical risk factors unique to flood-prone areas, and the Social Vulnerability Index (SoVI), which concentrates on sociodemographic factors, are examples in the table. Strong frameworks for assessing flood risk and creating flexible, evidence-based strategies are made possible by the integration of various data sources through the collection of flood assessment indices from reviewed papers.

While the indices presented in Table 4 share the overarching goal of quantifying flood risk components, they differ substantially in their conceptual focus, data requirements, and contextual applicability, and their selection should be guided by the specific assessment objectives and available data. The Flood Hazard Index (FHI) is the most physically oriented of the indices, focusing exclusively on flood depth, duration, and frequency. Its primary advantage lies in its simplicity and direct linkage to observable flood parameters, making it particularly suitable for rapid hazard zoning in data-scarce environments. However, its narrow physical focus means it captures neither the social nor the economic dimensions of risk, limiting its utility as a standalone tool for comprehensive flood risk assessment.

The Social Vulnerability Index (SoVI) addresses this gap by incorporating sociodemographic factors such as age, income, disability, and housing quality, making it especially valuable for identifying at-risk populations in urban and peri-urban contexts. Its main limitation is its reliance on census-based data, which may be outdated, unavailable, or inconsistent in developing regions, constraining its transferability across geographic contexts. The Flood Vulnerability Index (FVI) offers a broader integrative framework by combining social, economic, environmental, and physical indicators, making it more adaptable to diverse local conditions than either the FHI or SoVI alone. However, its multi-dimensional nature introduces challenges in indicator weighting and data availability, particularly in rural and data-deficient settings where socioeconomic datasets are often incomplete.

The Livelihood Vulnerability Index (LVI) is specifically designed for rural and agrarian communities, capturing flood-sensitive economic dimensions such as employment diversity, food security, and access to credit—factors that are systematically overlooked by urban-focused indices. This makes the LVI uniquely valuable for addressing the rural flood research gap identified in this review, though its application remains rare, reflecting the broader underrepresentation of rural contexts in the literature. The Composite Vulnerability Index (CVI) and the Flood Risk Index (FRI) represent the most integrated approaches, combining multiple hazard, exposure, and vulnerability dimensions into a single composite score. Their key advantage is their capacity for cross-regional comparison and policy prioritization, but this comes at the cost of methodological complexity, subjectivity in indicator selection and weighting, and sensitivity to data quality. The Urban Flood Risk Index (UFRI) extends this framework specifically to urban environments, incorporating infrastructure-specific variables such as impervious surfaces, drainage systems, and building types, making it the most contextually tailored tool for urban flood risk management.

All together, these findings suggest that no single index is universally applicable—the selection of appropriate indices should be driven by the flood type, geographic context, available data, and the specific hazard, vulnerability, or exposure dimension being assessed. The limited application of integrated indices such as the FRI and UFRI across the 89 included studies further reinforces the finding that holistic multi-component risk assessment frameworks remain underutilized in current flood risk research.

3.3. Regional Technology Adaptability Analysis

Table 5. Regional distribution of spatial techniques: Developed vs. developing regions (n = 89 included studies).

Spatial Technique	Developed Regions (n = 27)	Developing Regions (n = 55)	Global/Multi- Country (n = 7)	Total (n)	% of Total
RS & GIS	11 (41%)	21 (38%)	1 (14%)	33	37.1%
Hydrological & Hydraulic	4 (15%)	4 (7%)	4 (57%)	12	13.5%
Geospatial & MCDA	6 (22%)	17 (31%)	2 (29%)	25	28.1%
ML & AI	2 (7%)	7 (13%)	—	9	10.1%
Statistical	1 (4%)	3 (5%)	—	4	4.5%
Climate & Scenario	3 (11%)	3 (5%)	—	6	6.7%
Column Total	27	55	7	89	100%

presents the distribution of spatial techniques across developed and developing regions among the 89 included studies, revealing notable differences in methodological preferences that reflect underlying disparities in data availability, computational infrastructure, and institutional capacity. Developing regions account for most included studies (n = 55, 61.8%), with China (n = 11) and India (n = 7) as the most prolific contributors, followed by Egypt, Iran, Romania, and Bangladesh.

RS and GIS methods dominate in both developed (n = 11, 41%) and developing (n = 21, 38%) regions, reflecting their widespread accessibility and applicability across diverse contexts. However, important differences emerge for other techniques. Geospatial and MCDA approaches are disproportionately concentrated in developing regions (n = 17, 31%) compared to developed regions (n = 6, 22%), suggesting that MCDA frameworks—which require less specialized infrastructure and can integrate readily available socioeconomic data—are particularly well suited to data-constrained environments. Machine learning methods, while still limited overall, are more prevalent in developing regions (n = 7, 13%) than developed (n = 2, 7%), driven largely by studies from China and India where large datasets and computational resources are increasingly available.

Conversely, hydrological and hydraulic modeling shows an equal distribution between developed and developing regions (n = 4 each), reflecting the universal importance of physically based models for flood simulation regardless of regional context. Climate and scenario analysis is similarly balanced (n = 3 each), though its overall low representation across both groups indicates that long-term climate-informed approaches remain underutilized globally. These findings suggest that while developing regions are actively adopting accessible geospatial tools, significant gaps remain in the application of computationally intensive approaches, a disparity that has direct implications for the equity and comprehensiveness of flood risk assessment worldwide.

4. Discussion

4.1. Reliability and Limitations of Geospatial and Hydrological Models in Flood Hazard Assessment

The review of flood hazard assessment shows a trend in integrating diverse spatial methodologies, including GIS, remote sensing, machine learning, and hydrodynamic modeling. As illustrated by [155], spatial dependency frameworks such as exceedance and copula-based methods enhance flood forecasting by incorporating the natural variability of flood events across different watersheds. This spatial awareness is essential for flood risk evaluation, especially in areas with diverse topography and hydrological characteristics. Likewise, GIS and remote sensing technologies have improved flood mapping through the integration of factors, like slope, drainage, and land use empirically confirmed by [45], whose GIS-RS-MCDA integration produced a high-accuracy flood susceptibility map in Ethiopia, and [156], whose AHP-GIS framework effectively identified high flood risk zones in the Zambezi region. This integration, illustrated by [157] in their study on the King Talal Dam region, employs the Analytic Hierarchy Process (AHP) to prioritize these factors, ensuring that geospatial data remains responsive to ongoing environmental changes and, thus, keeps assessments current.

The review further reveals progression from traditional hydrological methods to advanced geospatial and machine learning techniques, each tailored to specific assessment needs. GIS-based flood mapping remains foundational due to its accessible visualization and its effectiveness in regions with robust topographical data, though its accuracy is often limited by the resolution of Digital Elevation Models (DEMs), [158]; this limitation is documented across multiple studies, with [61,159,160] each reporting that low-resolution DEMs introduced significant errors in flood zone delineation and depth estimation. As noted in prior studies, GIS mapping alone may not be sufficient as a predictive tool, particularly in complex terrains where low-resolution DEMs introduce errors. Meanwhile, remote sensing, particularly Synthetic Aperture Radar (SAR), offers invaluable capabilities in continuous flood monitoring due to its ability to collect data regardless of weather conditions. SAR’s utility in real-time monitoring is underscored by its application in inaccessible regions demonstrated empirically by [161], whose RAPID SAR system achieved high-accuracy flood inundation mapping for emergency response, and [162], whose Hydro SAR Sentinel-1 monitoring provided year-round flood data in flood-prone agricultural areas. Yet, its limitation lies in its surface-level assessment, which lacks the hydrological depth provided by traditional models [163].

Hydrodynamic modeling tools such as HEC-RAS and MIKE FLOOD are highly effective for simulating flood behavior where ample data is available [151,164], making them well suited for accurate applications like regulatory floodplain delineation. Nonetheless, their dependence on datasets and historical records restricts their use in areas lacking data and under shifting climatic scenarios [165,166]. Conversely, machine learning (ML) approaches provide adaptability by detecting patterns in changing settings and frequently deliver superior results with less reliance on physical input data [167]. However, ML models encounter difficulties including data requirements for training and poor interpretability, hindering their adoption in policy environments [168], as illustrated by [90], who found that purely data-driven ML models risk losing physical process understanding, proposing a Physics-Informed Machine Learning hybrid to balance predictive power with hydrological interpretability. Combining these methods might improve flood risk management across contexts.

The selection of an appropriate flood assessment method is inherently context-dependent, varying based on data availability, geographic characteristics, and computational resources. While GIS and remote sensing are ideal for initial, large-scale monitoring, hydrodynamic models are superior for detailed local predictions with ample data. Machine learning models, particularly when combined with multi-sourced geospatial and socioeconomic data, offer high-resolution flood hazard mapping, effectively addressing gaps in conventional approaches [169]. However, to fully harness ML’s potential, limitations in interpretability must be addressed to foster broader regulatory acceptance. This evolving field underscores the importance of an integrative, interdisciplinary approach, where combining the strengths of each technique leads to more comprehensive and responsive flood management strategies.

4.2. Tools and Methods in Flood Hazard Assessment

Reviewing the tools applied in flood hazard evaluation reveals a distinct pattern showcasing the integration of hydrological, GIS, remote sensing, machine learning and stochastic methods. These instruments tackle aspects of flood risk, each offering advantages that align well with flood-vulnerable settings and data scenarios. Hydrological and hydraulic models are prominent as the foundation of flood forecasting, modeling water flow and runoff behaviors essential for infrastructure design in regions such as urban floodplains, and river valleys. Flood hazard evaluations achieve the accuracy required for planning decisions like enhancing levee and flood channel locations through models like HEC-RAS and MIKE FLOOD [164]. This is supported empirically across the reviewed literature, as ref. [69] demonstrated that a porosity-based flood inundation model achieved efficient large-scale flood simulations while preserving spatial detail and reducing computation time, and [170] confirmed that an integrated hydrological–hydraulic model enhanced flood prediction accuracy in complex lake-influenced floodplains. When analyzing instances in U.S. flood areas, these models have shown to be essential for replicating flood dynamics under scenarios, especially where heavy precipitation and swift runoff meet, in heavily inhabited regions [171]. Ref. [85] further demonstrated that hydrodynamic forecasts substantially improved flood mapping accuracy for hurricane-prone areas, confirming the value of physically based models in extreme event scenarios.

Conversely, when examining the changing patterns of land use and hydrological reactions resulting from growth, remote sensing and GIS prove to be essential. The extensive data provided by Sentinel-1 SAR and MODIS imagery enable real-time flood surveillance and hazard mapping, detecting changes that could impact flood dynamics over time. This is empirically confirmed by [172], whose ensemble classifier combining Sentinel-1 and Sentinel-2 imagery improved land use mapping in the Sahel, directly aiding flood risk mitigation, and by [173], whose spatiotemporal analysis of urban rainstorm waterlogging demonstrated how urbanization patterns can be tracked through spatial analysis to improve flood management in rapidly growing cities. This review clearly demonstrated that these technologies are crucial for developing areas such as the Yangtze River Basin, where swift urban development increases susceptibility to flash floods.

As [174] notes, “GIS-based assessments enable planners to dynamically account for evolving land-use patterns, providing the responsive mapping required in these rapidly changing environments.” By layering this data within GIS frameworks, flood hazard maps are updated to reflect new infrastructure, impervious surfaces, and changing natural features, making this approach indispensable for proactive flood risk. Statistical approaches such as Monte Carlo simulations tackle uncertainties in flood forecasting by generating scenarios facilitating extended risk management in areas lacking sufficient data. Ref. [130] demonstrated this in practice, showing that high-resolution downscaled climate models revealed critical flood peak increases in Vietnam’s Upper Thu Bon catchment, necessitating adaptive flood risk management, though the study also acknowledged limitations in model uncertainties and spatial resolutions that constrain local-scale applicability. Nevertheless, their accuracy relies on data and premises emphasizing the necessity for enhanced inputs and revised climate forecasts [175].

Ultimately, a compelling aspect of our findings was the value of combining machine learning models such as Random Forest (RF) with multi-sourced geospatial and socioeconomic data to create high-resolution, granular flood hazard maps. Ref. [176] illustrated this effectively, demonstrating that combining SWMM with Random Forest analysis optimized flood resilience planning for high-density urban zones, directly supporting policy decisions. Similarly, ref. [95] showed that integrating Random Forest with TOPSIS decision-making identified flood-prone zones with improved hazard prioritization, combining the predictive power of ML with the structured multi-criteria reasoning of MCDA. These advanced, data-integrative models allow for an unprecedented level of detail, identifying not only areas of physical vulnerability but also those where socioeconomic factors heighten risk. This layered approach captures the intricacies of flood hazards, providing a comprehensive understanding that guides more effective, localized flood management strategies. The diversity of tools underscores the evolution of flood hazard assessment, where integrative, high-resolution mapping has become essential to addressing the intersecting drivers of flood risk in today’s complex environmental landscape.

4.3. Indices-Based Approaches and the Effectiveness of MCDA in Flood Vulnerability Assessment

The evaluation of flood vulnerability assessment techniques underscores the significance of indicator-based methods employing vulnerability indices. As demonstrated in studies, indices such as the Social Vulnerability Index (SoVI) and the Flood Vulnerability Index (FVI) continue to play a key role in integrating various socioeconomic, environmental, and physical information to generate standardized measures that highlight essential elements of flood risk. This is confirmed empirically across the reviewed literature; ref. [177] demonstrated that SoVI-based analysis revealed income-based spatial injustices in flood vulnerability, directly supporting equitable flood risk reduction policy design at the municipal level, while [121] applied social vulnerability indices to highlight the financial needs of vulnerable Romanian communities, enabling targeted flood resource allocation. For example, as highlighted in the research of [167], SoVI is commonly utilized in flood risk analysis to measure elements like age, income and housing security that affect a community’s ability to withstand floods. The organized framework of this index helps emergency planners and decision-makers pinpoint populations by facilitating a more targeted distribution of resources to safeguard at-risk groups. In the same vein, FVI, as pointed out by [178], combines social, environmental and physical metrics designed specifically for flood-vulnerable regions, rendering it a flexible instrument suitable for different regional settings, with distinctive environmental or socioeconomic characteristics. Ref. [59] empirically validated both CVI and FVI for coastal flood vulnerability assessment, confirming their effectiveness in highlighting critical areas for targeted resilience planning in response to sea-level rise and storm surges, particularly in data-scarce regions.

A major benefit of these indices is their multi-faceted approach, allowing flood evaluations to extend past one-dimensional analysis and incorporate various social and physical vulnerabilities. Ref. [143] demonstrated this empirically, showing that integrating social indicators into flood damage models produced more comprehensive vulnerability mapping by merging socioeconomic and physical data—a finding that directly supports the argument for multi-dimensional index-based assessments over single-component approaches. Providing an encompassing perspective on elements leading to flood risk indices like SoVI and FVI supply decision-makers with valuable information to develop more precise context-aware flood management plans. The study by [179] demonstrates the application of these indices in facilitating flood resilience planning tailored to specific regions, guaranteeing that evaluations are both precise and pertinent to the distinct socioeconomic and environmental issues faced by various locations. Furthermore, ref. [140] confirmed the value of the Livelihood Vulnerability Index (LVI) for mapping flood-exposed communities in rural contexts, demonstrating its utility for food security and emergency planning in flood-prone areas—a finding particularly significant given the underrepresentation of rural flood research identified in this review.

Multi-criteria decision analysis (MCDA) methods hold a position in assessing flood vulnerability by offering a systematic approach to analyze and rank multiple flood risk factors [180]. Techniques such as the Analytic Hierarchy Process (AHP) Fuzzy AHP, the Weighted Sum Model (WSM) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) are frequently employed. These approaches enable decision-makers to allocate weights to socioeconomic and environmental criteria specific to various areas [181]. Ref. [111] empirically validated Fuzzy AHP for urban flood risk assessment in arid regions, confirming its effectiveness in supporting proactive planning in Qatar, while [182] demonstrated that a Hierarchical Fuzzy Inference System reduced decision-making bias in flood-prone urban areas, enhancing flood resilience planning. For instance, Fuzzy AHP introduces a level of adaptability by incorporating “fuzziness” in indicator weighting, addressing uncertainties from incomplete data or different expert opinions. This flexibility is essential in complex flood-prone environments where local knowledge enhances the reliability of vulnerability assessments.

Every MCDA approach offers advantages improving the flexibility of flood vulnerability evaluations across various geographical and socioeconomic settings. Ref. [183] demonstrated this adaptability, showing that a GIS-based multi-criteria framework generated a flexible flood susceptibility assessment applicable even in ungauged catchments, a critical advantage in data-deficient regions. For instance, WSM’s simple combination of indicators into one vulnerability value is helpful for producing clear and understandable outcomes, whereas TOPSIS orders locations according to their closeness to an “ideal” vulnerability model, making it especially effective, for pinpointing areas that match particular vulnerability traits [184]. Ref. [95] empirically illustrated this by combining Random Forest with TOPSIS to identify flood-prone zones in Jiroft, demonstrating that integrating ML’s predictive power with MCDA’s structured reasoning improved hazard prioritization beyond what either method could achieve alone. However, these methods also have limitations, particularly regarding subjectivity in weighing indicators, which can introduce biases and inconsistencies [167]. This limitation is consistently documented across the reviewed studies; [156] noted that AHP criteria selection remained dependent on subjective judgment, ref. [113] reported limited scope for dynamic flood data integration based on his study area in Greece, and [185] found that weight assumptions in MCDA affected hazard map precision in complex terrains. While Fuzzy AHP reduces some uncertainty by employing a spectrum of values and then fixed weights, it does not fully remove subjectivity. Furthermore, the premise in MCDA that each criterion independently affects vulnerability might neglect actual interactions between socioeconomic indicators and infrastructure elements [186]. Overcoming these constraints demands choice of indicators, accurate adjustment of weights, and incorporation of objective information to reduce bias and improve the trustworthiness of MCDA-based evaluations [184].

In sum, the findings suggest that while indicator-based and MCDA methods provide powerful tools for flood vulnerability assessment, effective implementation requires careful attention to context-specific indicators and the potential biases in weighting. By acknowledging and adjusting for these limitations, MCDA and index-based approaches can produce robust, actionable insights, supporting comprehensive flood management and resilience planning.

4.4. Integrating Hazard, Exposure, and Vulnerability for a Comprehensive Flood Risk Assessment Framework

After examining various methodologies in flood hazard and vulnerability assessments, the necessity of integrating hazard, exposure, and vulnerability into a unified flood risk framework has become apparent. This integration approach, as illustrated in recent studies, allows for a more comprehensive and actionable assessment of flood impacts than independent analyses of each component. While hazard analysis effectively determines flood probability and intensity, it lacks the socioeconomic and infrastructural context that defines its true impact on communities [187]. Similarly, while vulnerability assessments offer valuable insights into factors such as socioeconomic status and housing stability that may increase susceptibility to flood damage, their practical application in mitigation planning is limited if hazard probabilities are not considered [188]. By bringing these dimensions together, a unified framework captures not only where and when floods are likely to occur but also identifies the specific people, assets, and infrastructure at risk, thus offering a multi-dimensional view of flood impacts.

Geospatial and statistical tools are essential for operationalizing this unified approach. As demonstrated by [157], geographic information systems (GIS) and remote sensing provide spatial precision in mapping hazard zones, exposed assets, and vulnerable populations. Integrating these spatial data layers with statistical methods, such as Principal Component Analysis (PCA) or machine learning algorithms, allows for the efficient processing of large datasets to isolate the primary drivers of flood risk, unveiling patterns and relationships often concealed in standalone analyses. This synergy not only deepens the understanding of flood risks but also enhances predictive accuracy, surpassing the capacity of traditional single-component evaluations.

A particularly critical insight from the reviewed papers is the often-overlooked role of exposure in flood risk assessments. While hazard and vulnerability analyses reveal the likelihood of flood events and the factors that increase susceptibility, exposure determines the actual extent to which people, infrastructure, and assets are at risk [178]. Overlooking exposure in flood assessments can lead to incomplete and sometimes misleading conclusions. For example, a high-hazard area with low exposure due to minimal infrastructure or population may present less risk than a densely populated urban zone with moderate hazard levels. Exposure acts as a bridge between hazard and vulnerability, translating potential flood events into tangible risks by accounting for the physical presence and density of at-risk elements [186].

Incorporating exposure analysis enables a more practical understanding of risk by directly identifying specific people, infrastructure, and assets within flood-prone areas. While hazard assessments estimate the likelihood and intensity of flooding, they often fail to reveal who and what will be affected. Exposure analysis addresses this gap by detailing the population, critical infrastructure, and assets situated within hazard zones. This adaptability is critical, as exposure data can be continuously updated with new demographic or infrastructural information, ensuring that flood risk assessments remain relevant as conditions evolve. Thus, integrating exposure with hazard data can significantly transform theoretical flood risk models into actionable frameworks for disaster preparedness and response. Combining hazard, exposure, and vulnerability not only identifies where floods are likely to occur but also quantifies their potential impact on local communities and economies [179]. This integrated approach empowers decision-makers to implement proactive strategies that mitigate flood damage, safeguard lives, and minimize economic disruption. Drawing on the evidence from the reviewed literature, a practical and operable three-stage integration pathway can be proposed for flood risk assessment frameworks seeking to incorporate hazard, vulnerability, and exposure in a unified approach. In the first stage—hazard delineation—RS and GIS tools and hydrodynamic models such as HEC-RAS are used to map flood extent, depth, and frequency, establishing the physical baseline of risk. Ref. [45] demonstrated this effectively, combining GIS, remote sensing, and MCDA to produce a high-accuracy hazard susceptibility map in Ethiopia that directly fed into risk management planning. In the second stage—vulnerability and exposure layering—socioeconomic and demographic data are integrated with the hazard baseline using MCDA frameworks such as AHP or Fuzzy AHP, weighted against locally relevant indicators of social vulnerability, infrastructure exposure, and adaptive capacity. Ref. [132] illustrated this stage in the context of Ghana’s agricultural basin, linking climate-driven hazard projections with vulnerability and exposure data through an impact chain methodology to produce an integrated risk assessment. In the third stage—synthesis and decision support—the combined hazard–vulnerability–exposure layers are translated into actionable risk scores using composite indices such as the FRI or UFRI, enabling spatial prioritization for flood mitigation investment and policy targeting. Ref. [176] demonstrated this operationally, combining SWMM hydrological modeling with Random Forest and TOPSIS decision-making to match flood risk demand with resource supply in high-density urban zones. This three-stage pathway—delineate, layer, synthesize—is deliberately method-agnostic, allowing practitioners to select the most appropriate tools at each stage based on data availability and local context, making it applicable across both data-rich urban environments and data-scarce rural settings where current integrated frameworks remain absent. By moving from isolated assessments to a comprehensive framework, flood management becomes more precise, adaptive, and resilient, supporting sustainable, long-term flood resilience in vulnerable areas.

4.5. Rural Flooding Compared to Urban, Riverine, and Pluvial Floods

A review of contemporary flood risk research reveals a pronounced emphasis on riverine, pluvial, and urban flooding due to their immediate impact and growing frequency. Pluvial flooding, where intense rainfall exceeds the drainage capacity of both urban infrastructure and natural land surfaces, has garnered significant attention as climate change projections forecast more extreme rainfall events, particularly in densely populated areas. This urban-centric focus highlights the critical need for resilient urban planning and infrastructure capable of managing flood risks in densely populated areas. However, developing robust forecasting models remains challenging due to the complex processes involved in urban flooding, limited long-term observations, and data variability [167,179,189]. Innovative approaches integrating satellite imagery, gauged measurements, and citizen observations are advancing urban flood forecasting, using key inputs like digital elevation models and rainfall data to improve modeling accuracy. Ref. [173] empirically demonstrated this, showing that spatiotemporal analysis of urban rainstorm waterlogging effectively tracked urbanization impacts on flood dynamics, directly supporting sustainable urban flood management planning. Many countries are complementing these advancements with both structural and non-structural measures, including spatial planning and green infrastructure, which aim to curb the frequency and impact of urban floods.

Although progress has been made, flood risk in rural areas is still considerably understudied despite their special vulnerabilities and considerable effects. Recent research, including the work by [186], indicates that rural flood hazards account for 22% of predicted flood damages in Ethiopia, highlighting the substantial consequences these floods may cause. Flood response in rural regions encounters unique difficulties frequently worsened by scarce resources, inadequate infrastructure and limited emergency service availability. Moreover, uncalibrated flood models for rural areas tend to perform poorly, pointing to the need for improved calibration and validation methods tailored to rural hydrological dynamics [190]. This underrepresentation is not incidental but reflects three interrelated structural barriers. First, data scarcity remains the most fundamental constraint, as rural areas typically lack the dense networks of rain gauges, streamflow sensors, and high-resolution DEMs that urban flood modeling depends upon, as confirmed by [183], who noted that their GIS-MCDA framework was specifically designed to function in ungauged catchments precisely because standard data infrastructure is absent in rural contexts. Second, model inadaptation compounds this problem. The dominant tools identified in this review, including HEC-RAS, machine learning algorithms, and MCDA frameworks, were largely developed and calibrated for urban and riverine environments with structured infrastructure and well-defined drainage systems, making their direct application to dispersed rural settlements and agricultural landscapes unreliable without substantial recalibration. Third, rural flooding receives disproportionately low policy attention, as disaster risk reduction frameworks have historically prioritized urban centers due to their higher population density and economic concentration, leaving rural communities with fewer early warning systems, lower infrastructure investment, and limited representation in flood risk governance, as reflected in the finding that only 1 of the 89 included studies and even only 13 out of all eligible papers focused exclusively on rural flood contexts. As [178,186] suggest, frameworks incorporating rural flood risks alongside urban concerns are essential for capturing a complete picture of regional flood vulnerabilities.

The need to tackle flood risks in rural areas grows more apparent when reflecting on the extensive consequences of rural flooding. Floods often cause economic and farming damage in rural communities, jeopardizing food stability and unsettling local economies that depend on agriculture. Ref. [132] empirically confirmed this, demonstrating that flood risk in Ghana’s White Volta Basin—an agricultural region—is projected to increase by 19.3% by 2100 under climate change scenarios, with adaptive capacity severely constrained by socioeconomic vulnerabilities. Ref. [140] similarly showed that the Livelihood Vulnerability Index effectively mapped flood-exposed rural communities, highlighting the direct threat to food security and the urgent need for emergency planning tailored to agrarian contexts. Interruptions to transportation, supply networks and service accessibility due to flooding further trigger ripple effects for urban centers reliant on rural supplies. However, the practical management consequences of the urban–rural divide extend beyond resource disruption. The absence of rural-specific flood control decision-making frameworks means that rural communities are typically reactive rather than proactive in flood response—lacking the early warning infrastructure, spatial planning tools, and institutional capacity that urban centers increasingly benefit from. Furthermore, the failure of urban–rural coordination in flood management creates systemic vulnerabilities—when rural flooding disrupts agricultural supply chains, transportation corridors, and water systems, the cascading effects on urban food security and economic stability are rarely accounted for in urban flood risk frameworks, creating blind spots in regional resilience planning [168]. For example, disruptions in rural–urban linkages can interrupt the flow of essential goods and services, which are critical to the resilience and economic stability of nearby cities [178].

Incorporating flood risk into flood management is thus crucial for developing a thorough and fair approach to flood resilience. Effective flood evaluation and planning need to expand their focus to cover rural regions, guaranteeing that flood resilience efforts safeguard every community. Ref. [191] illustrated the scale of this need, finding that approximately 24,837 hectares of crop area and 95 villages fell within high flood risk zones in their study region, a finding that underscores the urgency of developing rural-specific risk assessment frameworks that go beyond the urban-dominated methodologies currently prevalent in the literature. Improved flood modeling designed for environment infrastructure improvements capable of enduring rural floods and flexible risk management techniques considering local requirements will be vital for reducing rural flood hazards. By developing resilience in both rural and urban areas, regions can strengthen their overall capacity to manage the increasing frequency of flood events, creating a more robust defense against future climate-driven flood challenges.

4.6. Climate Change Adaptability of Geospatial Methods

Climate change is fundamentally altering the frequency, intensity, and spatial distribution of flood events, presenting new and growing challenges to the geospatial methods reviewed in this study. While the tools identified across the 89 included studies have demonstrated considerable effectiveness under historical flood conditions, their adaptability to the emerging realities of climate-driven flooding warrants critical examination across three dimensions: extreme flood events, data timeliness, and model transferability under shifting climatic baselines.

The increasing frequency of extreme flood events poses perhaps the most immediate challenge to current geospatial methods. Static hazard mapping approaches—which represent the dominant paradigm across the reviewed literature—are calibrated against historical flood records and return period statistics that are rapidly becoming outdated under non-stationary climate conditions. Ref. [130] demonstrated this directly, showing that high-resolution downscaled CMIP5 climate models revealed critical flood peak increases in Vietnam’s Upper Thu Bon catchment that would not have been captured by historically calibrated models, while [131] confirmed that probabilistic flood risk assessments under RCP climate scenarios showed significant increases in flood hazard in South Korea—findings that underscore the inadequacy of static approaches in capturing future extremes. Furthermore, ref. [192] highlighted that multiscale flood risk assessments under climate change scenarios revealed future flood hazard trends in riverine urban centers that existing infrastructure was not designed to accommodate, pointing to a fundamental mismatch between current assessment frameworks and future flood realities.

Data timeliness represents a second critical challenge. Geospatial flood assessments depend heavily on input data, DEMs, land use maps, precipitation records, and socioeconomic datasets that are often static and periodically updated at best. As climate change accelerates land use transformation, sea-level rise, and hydrological regime shifts, the temporal gap between data collection and assessment application introduces growing uncertainty into flood risk outputs. Ref. [193] illustrated this limitation, noting that their GIS-based screening tool for climate change-related flood risks was constrained by the lack of historical flood data for validation, a challenge that will intensify as climate change drives flood events outside historical ranges. Ref. [118] similarly acknowledged that DEM resolution and sea-level rise scenario variability limited the reliability of coastal vulnerability assessments, reflecting the broader challenge of keeping geospatial inputs current in rapidly changing coastal environments.

The transferability of models calibrated under historical conditions to future climate scenarios constitutes a third challenge, one that is particularly acute for hydrodynamic and machine learning models. Ref. [84] demonstrated that fast flood simulation tools using steady-state models, while effective for early warning under known conditions, were limited by simplified dynamics that may affect precision in complex extreme scenarios—precisely the scenarios that climate change is making more frequent. Machine learning models face a related challenge: trained on historical flood data, they may underperform when flood patterns shift beyond the range of their training datasets, as confirmed by the Physics-Informed Machine Learning approach proposed by [90], which explicitly addressed the risk of purely data-driven models losing physical process understanding under novel climate conditions.

Collectively, these findings suggest that the geospatial flood assessment field must urgently transition from historically calibrated static frameworks toward dynamic, climate-adaptive approaches that explicitly incorporate future scenario projections, near-real-time data updating, and uncertainty quantification. The integration of climate scenario analysis tools, currently represented by only 6% (n = 5) of the 89 included studies, with established GIS, remote sensing, and machine learning methods represents the most promising pathway toward climate-resilient flood risk assessment frameworks capable of meeting the challenges of an increasingly flood-prone world.

5. Conclusions

This systematic review addressed four objectives: mapping the geospatial flood risk assessment methodological landscape (2010–2024); examining its evolution; quantifying the urban–rural research imbalance; and assessing the degree of hazard, vulnerability, and exposure integration across the literature. The analysis of 89 peer-reviewed studies yields four core findings that together reveal both the progress and the persistent blind spots of the field.

First, the methodological landscape remains anchored in established spatial tools—RS and GIS and hydrological modeling collectively account for nearly half of all technique applications—while machine learning and climate scenario analysis, despite their growing promise, remain underutilized at the margins of current practice. This distribution reflects not just methodological preference but structural constraints of data availability, computational capacity, and institutional familiarity that will need to be actively addressed to enable methodological transition.

Second, the urban–rural imbalance in flood research is stark and quantifiable—with rural flooding representing only a single included study—which fundamentally limits the field’s capacity to inform equitable flood risk management across all community types. This gap is driven by interrelated structural barriers: data scarcity, models ill-suited to rural contexts, and the historically low policy priority of rural flood governance.

Third, the fragmentation of hazard, vulnerability, and exposure assessments, with only 16% of studies combining any two components, represents a critical methodological shortcoming with direct policy consequences. Fragmented assessments systematically underestimate flood impacts and limit the actionability of risk frameworks. To address this, this review proposes a three-stage integration pathway—delineate, layer, synthesize—as a practical and method-agnostic approach applicable across diverse data environments.

Future research should prioritize three specific and actionable directions: the development of lightweight, low-data geospatial models specifically calibrated for rural hydrological conditions—moving beyond urban-optimized tools toward approaches that function effectively with sparse sensor networks and limited ground-truth data; the establishment of quantitative exposure assessment systems that dynamically track population distribution, critical infrastructure, and asset concentration within flood-prone areas, enabling exposure to be treated as a living dataset rather than a static input; and the systematic integration of climate scenario analysis into standard flood risk workflows—shifting the field from historically calibrated static assessments toward dynamic, forward-looking frameworks capable of capturing flood events beyond historical ranges under accelerating climate change.

This review acknowledges some key limitations. The search was restricted to Web of Science and ScienceDirect, which may not capture the full breadth of relevant literature. The language restriction to English and French excludes contributions from non-English-speaking research communities in flood-prone regions of Asia, Latin America, and Africa. The geographic distribution of included studies is skewed toward China and the United States, potentially limiting the generalizability of findings to other regional contexts.

By quantifying the methodological landscape, diagnosing the structural causes of persistent research gaps, and proposing a concrete integration pathway, this review offers both a rigorous assessment of the current state of geospatial flood risk science and a directional roadmap toward a more equitable, integrated, and climate-adaptive future for the field. For policymakers, the findings underscore the urgent need to expand flood risk governance frameworks beyond urban centers, invest in rural data infrastructure, and mandate integrated hazard, vulnerability, and exposure assessments as a standard requirement for national flood risk planning.