Search Results (76)

Understanding the relationship between the processes involved in hydrology and changesin land use becomes more urgent amid the accelerated development of urban areas. In this regard, this paper proposes the application of a spatio-temporal analysis of flood vulnerability through multi-criteria analysis (Analytical Hierarchy Process), integrated with GIS and modeling of multidimensional urban development processes within Cizre, Turkey. Important hydrological factors for the formation of flood risks, such as elevation, slope, land use/cover, rainfall, drainage density, and proximity to the river, were considered when preparing the flood susceptibility map. It was revealed that high- and very-high-risk zones are mainly located near the Tigris River and in urbanized areas, which occupy more than half of the territory under consideration. Multidimensional analysis showed that unplanned development increases flood risks in the area because of the increased area of impervious surfaces and the violation of natural water flows. As a way to overcome the limitations of traditional methods of static analysis of flood risks, the Flood Risk Transfer Index (FRTI) has been developed to describe the process of spatial redistribution of risks resulting from the impact of the increase in urbanization rates. The indicator of spatial redistribution of flood risk reached a value of 0.72, showing that flood pressures increased in existing cities instead of reducing them. Thus, this study provides a breakthrough in understanding flood risks through the introduction of a new methodology. Full article

Rapid peri-urban expansion has intensified flood risk in Southeast Asian cities through wetland loss, rural–urban migration, and delayed infrastructure development. This study examines the spatial and temporal dimensions of flood risk in Phnom Penh, Cambodia using a multi-factor framework based on hazard, exposure, vulnerability, and coping capacity. Vulnerability and coping capacity are analysed at both community and household levels. Migrant settlement duration captures differences in exposure and adaptive capacity over time. A composite flood risk index is constructed from survey data using the Rank Order Centroid weighting method. Results show that exposure is the dominant driver of flood risk, exceeding the influence of hazard intensity and largely shaping spatial patterns. Community-level vulnerability and coping capacity exert stronger effects than household-level characteristics, highlighting the importance of infrastructure and local settings. Flood risk varies across migrant groups: new migrants face the highest risk due to elevated exposure and vulnerability, while long-term migrants experience lower risk as adaptive capacity improves over time. However, risk reduction varies across groups, with persistent challenges linked to infrastructure and disaster preparedness systems. These findings highlight the importance of community-scale resilience strategies and targeted infrastructure investment to reduce flood risk in rapidly urbanising cities. Full article

With the intensification of global climate change and rapid urbanization, urban flooding poses an increasing threat to urban safety and sustainable development. Flood susceptibility mapping (FSM) serves as a practical approach for recognizing areas that may be vulnerable to flooding and is therefore essential for flood mitigation and urban planning. In this study, an interpretable ensemble machine-learning framework for urban FSM was developed using social media data. First, the spatial locations of flood events were extracted from social media posts and news reports to construct a flood inventory. Subsequently, a non-flood sample selection strategy, termed Similarity- and Diversity-Based Representative Sampling (SDRS), was proposed to ensure both sample similarity and diversity. Based on these samples, a heterogeneous bagging-based ensemble machine learning model was established for flood susceptibility assessment. To enhance model interpretability, the GeoShapley method was introduced to quantify the contributions of key conditioning factors and reveal their directional effects. The findings indicated that the proposed SDRS strategy delivered the best performance, yielding an AUC of 0.893 and a test-set precision of 0.859. The resulting susceptibility map exhibited a clear south-to-north decreasing gradient, with High- and Very-high-susceptibility zones accounting for approximately 26% of the study area (1897.23 km²). The interpretability analysis further indicated that the Nighttime Light Index (NLI), Impervious Surface Percentage (ISP), and population density were among the most strongly associated positive factors in the model, with a Global Spatial Share of 7.18%. These findings demonstrate that the proposed framework can reliably recognize areas vulnerable to flooding and offer a scientific basis for urban flood management in Guangzhou. Full article

Urban hydrological risks are endangering vulnerable populations, particularly in densely populated metropolitan areas undergoing rapid land use transformation. This study uses geospatial analysis to identify zones in the Athens metropolitan area that are prone to surface water accumulation and stream flow development during extreme rainfall events. Two spatial indices were developed by integrating digital elevation models, flow accumulation, slope, aspect, the topographic wetness index, and classified road network data: a Surface Water Accumulation Index and a Stream flow Pathway Index. Roads were categorized based on their orientation relative to the direction of the slope, which allowed for an assessment of their influence on hydrological flow. Both indices were classified into five risk levels representing gradients of hydrological vulnerability. The spatial patterns revealed by this analysis show strong correlations with flood-prone areas and natural drainage systems. These insights are essential for guiding urban planning efforts aimed at reducing hydrological hazards, particularly for at-risk groups such as the homeless. This approach offers a valuable tool for promoting sustainable, socially inclusive landscape management. Full article

The Upper Mono Basin Valley (UMBV) in Togo faces recurrent flooding hazards. This study assesses spatial flood susceptibility using an integrated approach combining Geographic Information Systems (GISs), Multi-Criteria Decision Making (MCDM), and the Analytic Hierarchy Process (AHP). Eight factors were weighted according to their influence: accumulation flow, annual precipitation, soil permeability, land use/land cover, slope, elevation, distance from drainage networks, and drainage network density. With a consistency ratio of 0.052, the AHP method proved coherent and enabled the development of a normalized Flood Hazard Index (FHI). Results revealed accumulation flow (weight = 0.33), distance to drainage networks (0.18), and network density (0.16) as the most critical drivers, while precipitation and soil permeability are secondary. Spatial classification revealed heterogeneity: 55% (871,046 ha) of the UMBV has very low susceptibility, while 1% (10,034 ha) is highly vulnerable, mainly in Est-Mono, Ogou, Anié, Tchamba, and Tchaoudjo. In contrast, Blitta and Sotouboua show lower vulnerability due to higher altitudes. This reveals that the UMBV is relatively less prone to flooding. The comparison of data from 28 focus groups in 14 municipalities with the flood susceptibility map shows a strong concordance between local perceptions and the mapping (r = 0.805, p < 0.001). These findings highlight the need for differentiated territorial strategies integrating physical parameters, land use dynamics, and community risk perceptions to strengthen flood risk management in the UMBV. Full article

This study presents a comprehensive geospatial framework for assessing coastal vulnerability and ecosystem service distribution along the Greek coastline, one of the longest and most diverse in Europe. The framework integrates two complementary components: a Coastal Erosion Vulnerability Index applied to all identified beach units, and Coastal Flood Risk Indexes focused on low-lying and urbanized coastal segments. Both indices draw on harmonized, open-access European datasets to represent environmental, geomorphological, and socio-economic dimensions of risk. The Coastal Erosion Vulnerability Index is developed through a multi-criteria approach that combines indicators of physical erodibility, such as historical shoreline retreat, projected erosion under climate change, offshore wave power, and the cover of seagrass meadows, with socio-economic exposure metrics, including land use composition, population density, and beach-based recreational values. Inclusive accessibility for wheelchair users is also integrated to highlight equity-relevant aspects of coastal services. The Coastal Flood Risk Indexes identify flood-prone areas by simulating inundation through a novel point-based, computationally efficient geospatial method, which propagates water inland from coastal entry points using Extreme Sea Level (ESL) projections for future scenarios, overcoming the limitations of static ‘bathtub’ approaches. Together, the indices offer a spatially explicit, scalable framework to inform coastal zone management, climate adaptation planning, and the prioritization of nature-based solutions. By integrating vulnerability mapping with ecosystem service valuation, the framework supports evidence-based decision-making while aligning with key European policy goals for resilience and sustainable coastal development. Full article

(1) Background: In the context of rapid urbanization and climate change, Chinese traditional villages are facing severe challenges such as deterioration of hydrological environment, weakened social resilience, and degradation of cultural heritage. (2) Methods: This paper took Baoyan Village in Zhenjiang City, Jiangsu Province as the research object and constructs a research framework of “assessment of historical hydrological resilience–diagnosis of current problems–construction of enhancement strategies”, aiming to explore the paths and driving mechanisms for enhancing the resilience of traditional villages. The spatio-temporal evolution of historical hydrological resilience in Baoyan Village was quantitatively evaluated by establishing a three-dimensional resilience index system of “ecological governance–social adaptation–cultural continuity”, combined with the Analytic Hierarchy Process (AHP) and GIS spatial overlay technology. (3) Results: The study found that ① The hydrological resilience zoning of Baoyan Village presented spatial differentiation characteristics of “core vulnerability-marginal resilience”, and the high-risk area was concentrated in the cultural building density area along the old Tongji River in the historical town area, indicating that this area requires key flood protection and resilience construction; ② this paper constructed a composite evaluation system of “Ecological Governance–cultural inheritance–social adaptation”, and the total score after evaluation was 0.67, indicating that the overall HHRI of Baoyan Village has declined. Specifically, the scores for Ecological Governance Resilience and Cultural Heritage Resilience were 0.48 and 0.46, respectively, reflecting a significant decrease compared to historical scenarios. Conversely, the score for Social Adaptation Resilience was recorded at 1.05, suggesting an improvement in this dimension. This enhancement can be attributed to advancements in water infrastructure and increased levels of community organizational support, which have bolstered the village’s capacity to withstand flooding events. ③ The integrity of weir fields, the transmission of traditional disaster prevention knowledge, and the stability of natural river channels are the main factors hindering the improvement of resilience systems. (4) Conclusions: Based on the assessment results, this study proposed the resilience enhancement path of “ecological space reconstruction-traditional water management wisdom activation–cultural resilience empowerment” for this case, and constructed a four-pronged driving mechanism consisting of government guidance, community participation, technology empowerment, and industrial synergy for implementation. Practice has shown that through specific strategies such as restoring the weir and field system, constructing sponge village units, and developing the rain and flood cultural experience industry, the key obstacle factors of the village can be effectively addressed, and the goals of flood safety and cultural inheritance can be achieved in a coordinated manner. This case provides an empirical reference that combines historical wisdom with modern technology for understanding the evolution of human–water relationships and the enhancement of resilience in traditional villages, and its research framework and methods are also of reference value for similar villages. Full article

Enhancing climate resilience is imperative for cities to mitigate the effects of global warming and the rising frequency of extreme weather events. This paper develops an evaluation index system for urban climate resilience in Hebei Province, based on data from 11 cities within the province. It evaluates the levels of climate resilience and identifies their limiting factors using the entropy weight method, an urban climate resilience assessment model, and an obstacle degree model, with a focus on four dimensions: ecological resilience, economic resilience, social resilience, and infrastructure resilience. The results indicate that (1) spatial variations in climate resilience across cities in Hebei Province are minimal, with the majority of cities exhibiting climate resilience levels within the moderate resilience category. (2) The majority of regions display low ecological and infrastructure resilience (0.1–0.3), while economic resilience is distributed across three tiers, with regional variations; social resilience remains moderately resilient (above 0.3). (3) Among the social resilience factors, C₃ and C₈ exhibit the highest obstruction levels, emerging as key barriers. (4) In order to effectively respond to climate change risks and challenges in a scientific manner, differentiated implementation of climate response strategies, the core of which lies in identifying the dominant vulnerability dimensions of different cities and accurately applying policies, such as Shijiazhuang, Baoding, Xingtai, Handan, and other cities with fragile ecological resilience, should comprehensively deepen the construction of sponge cities to alleviate urban flooding and the heat island effect. Full article

Urban coastal areas are increasingly vulnerable to compound flooding due to the convergence of extreme rainfall, storm surges, and infrastructure aging, especially in high-density settings. This study proposes and empirically validates a multi-scale strategy for enhancing urban flood resilience in the Macau Peninsula, a densely built coastal city with complex flood exposure patterns. Building on a previously developed network-based resilience assessment framework, the study integrates hydrodynamic simulation and complex network analysis to evaluate the effectiveness of targeted interventions, including segmented storm surge defense barriers, drainage infrastructure upgrades, and spatially optimized low-impact development (LID) measures. The Macau Peninsula was partitioned into multiple shoreline defense zones, each guided by context-specific design principles and functional zoning. Based on our previously developed flood simulation framework covering extreme rainfall, storm surge, and compound events in high-density coastal zones, this study validates resilience strategies that achieve significant reductions in inundation extent, water depth, and recession time. Additionally, the network-based resilience index showed marked improvement in system connectivity and recovery efficiency, particularly under compound hazard conditions. The findings highlight the value of integrating spatial planning, ecological infrastructure, and systemic modeling to inform adaptive flood resilience strategies in compact coastal cities. The framework developed offers transferable insights for other urban regions confronting escalating hydrometeorological risks under climate change. Full article

The intensification of extreme hydrologic events, such as flash floods and landslides, has amplified the challenges of ensuring timely and effective emergency response. A key factor in the efficiency of such interventions is the accessibility of affected areas, which often becomes compromised during hazard events. In this context, the present study focuses on the Vărbilău River catchment in Romania, a region highly exposed to frequent flash floods and terrain instability. The research evaluates the spatial accessibility of emergency intervention units. Four major intervention centers were assessed under both normal and constrained scenarios. Accessibility was quantified through travel-time thresholds, incorporating variables such as road quality, network density, topography, and hazard-induced disruptions. Findings indicate that southern localities enjoy relatively short intervention times (less than 10 or between 10 and 20 min) due to favorable terrain and proximity to well-equipped centers. In such cases, the speed on main roads is 50–60 km/h, while the accessibility index is 5. Conversely, northern areas and villages like Lutu Roşu face elevated isolation risks, as single-road access and weak connectivity heighten their vulnerability during floods or landslides. In such cases, speeds reduce to 10 km/h and accessibility is very low, with the accessibility index of 1. Scenario modeling further demonstrated that the loss of key hubs (e.g., Ploieşti or Văleni) severely undermines coverage efficiency, particularly in high-risk zones, where the access times increases over 40 min. These results emphasize the need for dynamic intervention planning, infrastructure reinforcement, and the systematic integration of hazard-prone areas into emergency response strategies. Moreover, the methodological framework developed here can be adapted to other regions exposed to hydrologic hazards. Full article

Sewer systems are essential for sustainable infrastructure management, influencing environmental, social, and economic aspects. However, sewer network capacity is under significant pressure, with many systems overwhelmed by challenges such as climate change, ageing infrastructure, and increasing inflow and infiltration, particularly through groundwater infiltration (GWI). Current research in this area has primarily focused on general sewer performance, with limited attention to high-resolution, spatially explicit assessments of sewer exposure to GWI, highlighting a critical knowledge gap. This study responds to this gap by developing a high-resolution GWI assessment. This is achieved by integrating fuzzy-analytical hierarchy process (AHP) with geographic information systems (GISs) and machine learning (ML) to generate GWI probability maps across the Dawlish region, southwest United Kingdom, complemented by sensitivity analysis to identify the key drivers of sewer network vulnerability. To this end, 16 hydrological–hydrogeological thematic layers were incorporated: elevation, slope, topographic wetness index, rock, alluvium, soil, land cover, made ground, fault proximity, fault length, mass movement, river proximity, flood potential, drainage order, groundwater depth (GWD), and precipitation. A GWI probability index, ranging from 0 to 1, was developed for each 1 m × 1 m area per season. The model domain was then classified into high-, intermediate-, and low-GWI-risk zones using K-means clustering. A consistency ratio of 0.02 validated the AHP approach for pairwise comparisons, while locations of storm overflow (SO) discharges and model comparisons verified the final outputs. SOs predominantly coincided with areas of high GWI probability and high-risk zones. Comparison of AHP-weighted GIS output clustered via K-means with direct K-means clustering of AHP-weighted layers yielded a Kappa value of 0.70, with an 81.44% classification match. Sensitivity analysis identified five key factors influencing GWI scores: GWD, river proximity, flood potential, rock, and alluvium. The findings underscore that proxy-based geospatial and machine learning approaches offer an effective and scalable method for mapping sewer network exposure to GWI. By enabling high-resolution risk assessment, the proposed framework contributes a novel proxy and machine-learning-based screening tool for the management of smart cities. This supports predictive maintenance, optimised infrastructure investment, and proactive management of GWI in sewer networks, thereby reducing costs, mitigating environmental impacts, and protecting public health. In this way, the method contributes not only to improved sewer system performance but also to advancing the sustainability and resilience goals of smart cities. Full article

Wetlands are natural ecosystems of great ecological and economic value. They provide undeniable ecosystem services that contribute to promoting sustainable development. Exposed to different pressures, these limnic ecosystems are particularly vulnerable to climate change. Thus, assessing wetland vulnerability is of utmost importance. Based on a systematic selection of relevant peer-reviewed studies, this paper helps to develop a general vision of the methods used to assess wetland vulnerability in different contexts, emphasizing the use of advanced computational approaches. Hence, an overview of different cases of wetlands all across the five continents and of different types of habitats is presented. Whether the wetland is permanently or seasonally flooded, coastal, or tropical, this study enables the analysis of diverse, already established vulnerability evaluation index systems. Some of these indices were computed using geographic information systems (GISs), artificial intelligence (AI), machine learning (ML), spatial principal component analysis (SPCA) and driver–pressure–state–impact–response (DPSIR) as evaluation models. Indeed, given the adoption of different methods, diverse models, and analytical approaches under different scenarios, the vulnerability assessment process should be seen as an iterative rather than a definitive process. An accurate wetland vulnerability assessment is essential for ensuring the sustainability of wetland ecosystems and for informing effective conservation and management strategies. Full article

The changing global climate and rapid urbanization make extreme rainstorm events frequent, and the flood disaster caused by rainstorm has become a prominent problem of urban public safety in China, which severely restricts the healthy and sustainable development of social economy. The weight calculation method of traditional risk assessment model is single and ignores the difference of multi-dimensional information space involved in risk analysis. This study constructs a flood risk assessment model by incorporating natural, social, and economic factors into an indicator system structured around four dimensions: hazard, exposure, vulnerability, and disaster prevention and mitigation capacity. A combination of the Analytic Hierarchy Process (AHP) and the entropy weight method is employed to optimize both subjective and objective weights. Taking the central urban area of Wuhan with a high flood risk as an example, based on the risk assessment values, spatial autocorrelation analysis, cluster analysis, outlier analysis, and hotspot analysis are applied to explore the spatial clustering characteristics of risks. The results show that the overall assessment level of flood hazard in central urban area of Wuhan is medium, the overall assessment level of exposure and vulnerability is low, and the overall disaster prevention and mitigation capability is medium. The overall flood risk levels in Wuchang and Jianghan are the highest, while some areas in Qingshan and Hanyang have the lowest levels. The spatial characteristics of each dimension evaluation index show obvious autocorrelation and spatial differentiation. These findings aim to provide valuable suggestions and references for reducing urban disaster risks and achieving sustainable urban development. Full article

Losses from floods and the wide range of impacts have been at the forefront of hazard-triggered disasters in China. Affected by large-scale human activities and the environmental evolution, China’s defense flood situation is undergoing significant changes. This paper constructs a comprehensive flood disaster risk assessment model through systematic analysis of four key factors—hazard (H), exposure (E), susceptibility/sensitivity (S), and disaster prevention capabilities (C)—and establishes an evaluation index system. Using the Analytic Hierarchy Process (AHP), we determined indicator weights and quantified flood risk via the following formula R = H × E × V × C. After we applied this model to 16 towns in coastal Zhejiang Province, the results reveal three distinct risk tiers: low (R < 0.04), medium (0.04 ≤ R ≤ 0.1), and high (R > 0.1). High-risk areas (e.g., Longxi and Shitang towns) are primarily constrained by natural hazards and socioeconomic vulnerability, while low-risk towns benefit from a robust disaster mitigation capacity. Risk typology analysis further classifies towns into natural, social–structural, capacity-driven, or mixed profiles, providing granular insights for targeted flood management. The spatial risk distribution offers a scientific basis for optimizing flood control planning and resource allocation in the district. Full article

Flood susceptibility modeling (FSM) plays a key role in advancing proactive disaster risk reduction and spatial planning. This research developed FSM for the Buzău River catchment in Romania—a region historically vulnerable to recurrent flood events—using four state-of-the-art ensemble boosting algorithms: AdaBoost, CatBoost, LightGBM, and XGBoost. Initially, a comprehensive set of 13 flood conditioning factors was assessed, which was subsequently narrowed down to 9 essential factors through multi-tier feature selection strategies. Analysis of performance via receiver operating characteristic (ROC) andprecision–recall curves showed only marginal differences between the models; however, CatBoost excelled with an area under the ROC curve (AUC) of 0.972 and an average precision (AP) of 0.971, with XGBoost following closely behind. The SHAP (SHapley Additive exPlanations) analysis of the CatBoost model indicated that the Slope, Distance from Rivers, Topographic Wetness Index (TWI), and Land Use/Land Cover (LULC) are the key contributing factors. The novelty of this research is found in its comparative analysis of AdaBoost alongside three gradient boosting algorithms—CatBoost, LightGBM, and XGBoost—while utilizing explainable artificial intelligence (XAI) and a multi-tier feature selection strategy to create FSM that are precise and comprehensible. These strategies deliver robust tools for managing flood risks and reinforce the viability of data-driven modeling in the various catchments of Europe. Full article