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ABSTRACT The increasing frequency of natural disasters, such as floods, droughts, and tsunamis, has made vulnerable communities less resilient, pushing them toward long‐term poverty and food insecurity. Effective post‐disaster rehabilitation is critical to restoring livelihoods, infrastructure, and food security. However, challenges such as corruption, misallocation, and mistargeting undermine post‐disaster aid programs. This study systematically reviews 86 peer‐reviewed articles (1990–2023) using the preferred reporting items for systematic reviews and meta‐analyses (PRISMA) protocol to investigate aid inefficiencies in disaster recovery. The findings reveal that aid often fails to reach the most affected communities, being diverted to unaffected areas due to political influence and local elites, exacerbating inequalities. Corruption further hampers institutional performance and long‐term disaster resilience efforts. The study calls for transparent, accountable, and inclusive strategies for aid distribution, aligning with SDG 10 (reduced inequalities) and SDG 11 (sustainable cities and communities). Future research should focus on gender‐sensitive strategies, local governance, and technological innovations to enhance aid transparency and effectiveness.

Abstract Market-based instruments, including competitive tenders, are central to funding global environmental restoration and management projects. Recently, tenders have been utilised to fund Nature-based Solutions schemes for Natural Flood Management, with the explicit purpose of achieving co-benefits; flood management and reducing inequities. While multiple studies consider the efficacy of Nature-based Solutions for tackling inequities, no prior research has quantified whether the resource allocation for these projects has been conducted equitably. We analyse two national natural flood management programmes funded through competitive tenders in England to explore who benefits by considering the characteristics of projects, including socio-economic, geographical (e.g. rurality) and flood risk dynamics. Our results suggest that inequity occurs at both the application and funding stages of Nature-based Solutions projects for flood risk management. This reflects wider international challenges of using market-based instruments for environmental resource allocation. Competitive tenders have the potential to undermine the equitable benefits of Nature-based Solutions.

Rapid urban expansion has significantly altered land use patterns, resulting in a decrease in pervious surface areas and a disruption of hydrologic connectivity between surface water and groundwater systems. Combined with inadequate drainage systems and poorly managed runoff, these changes have intensified urban flooding, leading to fatalities and significant infrastructure damage in many rapidly growing and climate-vulnerable urban areas around the world. This study presents an integrated economic-hydrologic model to assess the effectiveness of Low Impact Development (LID) measures—specifically permeable pavement, infiltration trenches, bio-retention cells, and rain barrels—in mitigating flood damage in the Bronx river watershed, NYC. The Storm Water Management Model (SWMM) was employed to simulate flood events and assess the effectiveness of various LIDs, applied individually and in combination, in reducing peak discharge. Flood inundation maps generated using HEC-GeoRAS were integrated with the HAZUS damage estimation model to quantify potential flood damages. A benefit-to-cost (BC) ratio was then calculated by comparing the monetary savings from reduced flood damage against the implementation costs of LID measures. Results indicate that the combined LID scenario offers the highest peak flow reduction, with permeable pavement alone reducing flow by 57%, outperforming other techniques under equal area coverage. Among all individual options, permeable pavement yields the highest cumulative BC ratio under all scenarios (4.6), whereas rain barrels are the least effective (2.6). The proposed evaluation framework highlights the importance of economic efficiency in flood mitigation planning and provides a structured foundation for informed decision-making to enhance urban resilience through LID implementation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

Study region: This study aims at the Kunhar River Basin, Pakistan, that has been facing repeated flood occurrences on a recurring basis. As the flood susceptibility of this area is high, its topographic complexity demands correct predictive modeling for strategic flood planning. Study focus: We developed a system of flood susceptibility mapping based on Geographic Information Systems (GIS), Principal Component Analysis (PCA), and Support Vector Machine (SVM) classification. Four kernel functions were applied, and the highest-performing was the Radial Basis Function (SVM-RBF). The model was validated and trained using historical flood inventories, morphometric parameters, and hydrologic variables, and feature dimensionality was reduced via PCA for increased efficiency. New hydrological insights: The SVM-RBF model recorded an AUC of 0.8341, 88.02% success, 84.97% predictability, 0.89 Kappa value, and F1-score of 0.86, all of which indicated high predictability. Error analysis yielded a PBIAS of +2.14%, indicating negligible overestimation bias but within limits acceptable in hydrological modeling. The results support the superiority of the SVM-RBF approach compared to conventional bivariate methods in modeling flood susceptibility over the complex terrain of mountains. The results can be applied in guiding evidence-based flood mitigation, land-use planning, and adaptive management in the Kunhar River Basin. © 2025 The Author(s)

Urban flooding, intensified by climate change and rapid urbanization, demands robust and operationally effective resilience strategies. However, empirical evidence on the comparative effectiveness of such strategies remains limited. This study presents the first meta-analytic synthesis evaluating urban flood resilience interventions across institutional, infrastructural, and socio-ecological domains. By synthesizing data from 29 peer-reviewed studies (2000–2024), this study applies standardized effect sizes (Cohen's d) and meta-regression models to assess the effectiveness of different strategies. Results reveal a substantial overall effect (pooled d = 2.96, 95 % CI: [1.92, 3.99]) with high heterogeneity (I2 = 93.8 %). Institutional mechanisms, such as policy coordination, regulatory frameworks, and risk governance, consistently show the strongest and most statistically significant impacts (d ≈ 2.96). Low Impact Development (LID) demonstrates limited, non-significant effects (d ≈ 0.08). The study introduces a novel hierarchical resilience framework spanning different dimensions and establishes an evidence-based typology of urban flood resilience strategies. These findings highlight the importance of integrated, multi-level governance and context-specific planning in enhancing urban flood resilience. The study findings provides critical insights for implementing resilience strategies in flood-prone urban areas, and support the formulation of adaptive and sustainable urban policies. © 2025

Urban soil sealing and anthropogenic activities, combined with the increasing intensity of rainfall due to climate change, is a threat to urban environments, exacerbating flood risks. To assess these challenges, Low Impact Development strategies, based on Nature-based solutions, are a key solution to mitigate urban flooding. To enhance the hydrological performance of LID infrastructure, and to meet the guideline requirements related to emptying time, specifically in low hydraulic conductivity soils, earthworm activity and vegetation dynamics can play a major role. The ETAGEP experimental site was built to study to address those challenges. 12 swales (10 m2 infiltration area for each swale) were monitored to evaluate the impact of earthworm activity (A. caliginosa and L. terrestris) and vegetation dynamics (Rye Grass, Petasites hybridus and Salix alba) to enhance the hydrological performance. The infiltration rate of the swales evolved in a differentiated manner, with an increase of 16.1 % to 310.8 % and draining times decrease of 13.9 % to 75.7, depending on initial soil hydro-physical properties and the impervious areas of the catchment which influence runoff volumes. The simulations on SWMM software showed similar results, with an enhancement of the hydraulic conductivity of N6 swales (60 m2 total catchment area) increasing from 18 mm h−1 to 25 mm h−1, and a reduction of drawdown time by 24.4 % (N6) and 20.8 % (N11–110 m2 active surface). A simulated storm event of 44.8 mm resulted in an overflow of 2.12 m3 for the N11 swale configuration, while no overflow was observed for N6. These results highlight the ecosystem services of earthworms for a sustainable stormwater management in urban environments, enhancing the hydrological performance of LID infrastructures and reducing therefore flood risks and limiting pressure on drainage network. © 2025 The Author(s)

Urban flooding threatens Indian cities and is made worse by rapid urbanization, climate change and poor infrastructure. Severe flooding occurred in cities such as Mumbai, Chennai and Ahmedabad. This has caused huge economic losses and displacement. This study addresses the limitations of traditional flood forecasting methods. It has to contend with the complex dynamics of urban flooding. We offer a deep learning approach which uses the network Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to improve flood risk prediction. Our CNN-LSTM model combines spatial data (water table, topography) and temporal data (historical model) to classify flood risk as low or high. This method includes collecting data pre-processing (MinMaxScaler, LabelEncoder) Modeling, Training and Evaluation. The results demonstrate the accuracy of flood risk predictions and provide insights into flexible strategies for urban flood management. This research highlights the role of data-driven approaches in improving urban planning to reduce flood risk in high-risk areas. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

This study presents a novel bivariate-output deep learning framework based on LeNet-5 for the simultaneous prediction of urban flood susceptibility and inundation depth in Seoul, South Korea. Unlike previous studies that relied on single-output models, the proposed approach jointly learns classification and regression targets through a shared feature extraction structure, enhancing consistency and generalization. Among six tested architectures, the Le5SD_CBAM model—integrating a Convolutional Block Attention Module (CBAM)—achieved the best performance, with 83% accuracy, an Area Under the ROC Curve (AUC) of 0.91 for flood susceptibility classification, and a mean absolute error (MAE) of 0.12 m and root mean squared error (RMSE) of 0.18 m for depth estimation. The model’s spatial predictions aligned well with hydrological principles and past flood records, accurately identifying low-lying flood-prone zones and capturing localized inundation patterns influenced by infrastructure and micro-topography. Importantly, it detected spatial mismatches between susceptibility and depth, demonstrating the benefit of joint modeling. Variable importance analysis highlighted elevation as the dominant predictor, while distances to roads, rivers, and drainage systems were also key contributors. In contrast, secondary terrain attributes had limited influence, indicating that urban infrastructure has significantly altered natural flood flow dynamics. Although the model lacks dynamic forcings such as rainfall and upstream inflows, it remains a valuable tool for flood risk mapping in data-scarce settings. The bivariate-output framework improves computational efficiency and internal coherence compared to separate single-task models, supporting its integration into urban flood management and planning systems. © 2025 by the authors.

This study uses remote sensing data to assess susceptibility to hazards, which are then validated to model impact scenarios for land subsidence and coastal flooding in the Integrated Coastal Zone Management (ICZM) of Selangor, Malaysia, to support decision-making in urban planning and land management. Land subsidence and coastal floods affect a major proportion of the population in the ICZM, with subsidence being significant contributing factors, but information on the extent of susceptible areas, monitoring, and wide-area coverage is limited. Land subsidence distribution is demarcated using Interferometric Synthetic Aperture Radar (InSAR) time-series data (2015–2022), and integrated with coastal flood susceptibility derived from Analytic Hierarchy Process (AHP)-based weights to model impacts on land cover. Results indicate maximum subsidence rates of 46 mm/year (descending orbit) and 61 mm/year (ascending orbit); reflecting a gradual increase in subsidence trends with an average rate of 13 mm/year. In the worst-case scenario, within the ICZM area of 2262 km2, nearly 12% of the total built-up land cover with the highest population density is exposed to land subsidence, while exposure to coastal floods is relatively larger, covering nearly 34% of the built-up area. Almost 27% of the built-up area is exposed to the combined effects of both land subsidence and coastal floods, under present sea level conditions, with increasing risks of coastal floods over 2040, 2050 and 2100, due to both combinations. This research prioritizes areas for further study and provides a scientific foundation for resilience strategies aimed at ensuring sustainable coastal development within the ICZM. © 2025 by the authors.

Floods pose a substantial risk to human well-being. These risks encompass economic losses, infrastructural damage, disruption of daily life, and potential loss of life. This study presents a state-wide and county-level spatial exposure assessment of the Iowa railway network, emphasizing the resilience and reliability of essential services during such disasters. In the United States, the railway network is vital for the distribution of goods and services. This research specifically targets the railway network in Iowa, a state where the impact of flooding on railways has not been extensively studied. We employ comprehensive GIS analysis to assess the vulnerability of the railway network, bridges, rail crossings, and facilities under 100- and 500-year flood scenarios at the state level. Additionally, we conducted a detailed investigation into the most flood-affected counties, focusing on the susceptibility of railway bridges. Our state-wide analysis reveals that, in a 100-year flood scenario, up to 9% of railroads, 8% of rail crossings, 58% of bridges, and 6% of facilities are impacted. In a 500-year flood scenario, these figures increase to 16%, 14%, 61%, and 13%, respectively. Furthermore, our secondary analysis using flood depth maps indicates that approximately half of the railway bridges in the flood zones of the studied counties could become non-functional in both flood scenarios. These findings are crucial for developing effective disaster risk management plans and strategies, ensuring adequate preparedness for the impacts of flooding on railway infrastructure. © 2025 by the authors.

Rapid urbanization and climate change have intensified urban flood risks, necessitating resilient upstream infrastructure to ensure metropolitan water security and effective flood mitigation. Gravity dams, as critical components of urban flood protection systems, regulate discharge to downstream urban areas. Gravity dams are critical for regulating flood discharge, yet their seismic vulnerability poses significant challenges, particularly under compound effects involving concurrent seismic loading and climate-induced elevated reservoir levels. This study introduces a novel seismic analysis framework for gravity dams using the scaled boundary finite element method (SBFEM), which efficiently models dam–water and dam–foundation interactions in infinite domains. A two-dimensional numerical model of a concrete gravity dam, subjected to realistic seismic loading, was developed and validated against analytical solutions and conventional finite element method (FEM) results, achieving discrepancies as low as 0.95% for static displacements and 0.21% for natural frequencies. The SBFEM approach accurately captures hydrodynamic pressures and radiation damping, revealing peak pressures at the dam heel during resonance and demonstrating computational efficiency with significantly reduced nodal requirements compared to FEM. These findings enhance understanding of dam behavior under extreme loading. The proposed framework supports climate-adaptive design standards and integrated hydrological–structural modeling. By addressing the seismic safety of flood-control dams, this research contributes to the development of resilient urban water management systems capable of protecting metropolitan areas from compound climatic and seismic extremes. © 2025 by the authors.

Flooding is an escalating hazard in arid and rapidly urbanizing environments such as Jeddah, Saudi Arabia, where the lack of historical flood records and sparse monitoring systems challenge effective risk prediction. To address this gap, this study aims to develop an accurate and interpretable flood susceptibility-mapping framework tailored to data-scarce urban settings. The research integrates a stacked ensemble model—comprising machine learning: XGBoost, CatBoost, and Histogram-based Gradient Boosting (HGB)—with SHapley Additive exPlanations (SHAP) to enhance prediction accuracy and model transparency. Random Forest was excluded from the final model stack due to inferior classification performance. A diverse set of geospatial inputs, including digital elevation model, slope, flow direction, Curve Number, topographic indices, and LULC (from ESRI Sentinel-2) were used as predictors. Furthermore, 92 and 198 flooded and non-flooded points were used for model validation. The model achieved strong predictive performance (AUC = 0.92, Accuracy = 0.82) on the validation set. In the absence of official flood records, model outputs were intersected with road network data to identify 395 road points in highly susceptible zones. Although these points do not represent a formal validation dataset—due to the general lack of detailed flood event records in the region, particularly in relation to infrastructure—they provide a valuable proxy for identifying flood-prone road segments. SHAP explainability analysis revealed that TRI, TPI, and distance to rivers were the most globally influential features, while Curve Number and LULC were key drivers of high-risk predictions. The model mapped 139 km2 (8.7 %) of the area as very high flood susceptibility and 325 km2 (20.3 %) as high susceptibility, outperforming individual learners. These results confirm that stacked ensemble learning, paired with explainable AI and creative validation strategies, can produce reliable flood susceptibility maps even in data-constrained contexts. This framework offers a transferable and scalable solution for flood risk assessment in similar arid and urbanizing environments. © 2025 Elsevier Ltd

Climate change and rapid urbanisation are straining urban stormwater management further, with floods and water pollution becoming more intense. SUDS is a nature-based alternative that solves these issues because it replicates natural hydrologic processes to create urban resilience. This systematic review summarises recent trends in SUDS technologies, performance, and policy frameworks, and their potential to mitigate flood risks, improve water quality, and enhance climate resilience. By the PRISMA methodology, 90 peer-reviewed studies published between 2014 and 2025 were considered, dealing with SUDS performance, cost-effectiveness, and overall difficulties with large-scale implementation of these systems. Main results are that bio-retention systems, permeable pavements, and green roofs are effective in controlling surface runoff and enhancing water quality. Moreover, the development of IoT-based monitoring and smart technologies has also considerably increased the scalability and efficiency of a SUDS. The review recommends the standardisation of SUDS performance, the incorporation of smart technologies, and more attractive policy incentives to speed up the uptake of SUDS in urban planning. One of the main contributions that this research is likely to make to the discourse concerning urban water resilience is that it offers evidence-based suggestions to policymakers and urban developers, and these suggestions argue in favour of taking urgent action in the area of climate adaptation by using SUDS extensively. © 2025 The Authors

Named Data Networking (NDN) represents a promising Information-Centric Networking architecture that addresses limitations of traditional host-centric Internet protocols by emphasizing content names rather than host addresses for communication. While NDN offers advantages in content distribution, mobility support, and built-in security features, its stateful forwarding plane introduces significant vulnerabilities, particularly Interest Flooding Attacks (IFAs). These IFA attacks exploit the Pending Interest Table (PIT) by injecting malicious interest packets for non-existent or unsatisfiable content, leading to resource exhaustion and denial-of-service attacks against legitimate users. This survey examines research advances in IFA detection and mitigation from 2013 to 2024, analyzing seven relevant published detection and mitigation strategies to provide current insights into this evolving security challenge. We establish a taxonomy of attack variants, including Fake Interest, Unsatisfiable Interest, Interest Loop, and Collusive models, while examining their operational characteristics and network performance impacts. Our analysis categorizes defense mechanisms into five primary approaches: rate-limiting strategies, PIT management techniques, machine learning and artificial intelligence methods, reputation-based systems, and blockchain-enabled solutions. These approaches are evaluated for their effectiveness, computational requirements, and deployment feasibility. The survey extends to domain-specific implementations in resource-constrained environments, examining adaptations for Internet of Things deployments, wireless sensor networks, and high-mobility vehicular scenarios. Five critical research directions are proposed: adaptive defense mechanisms against sophisticated attackers, privacy-preserving detection techniques, real-time optimization for edge computing environments, standardized evaluation frameworks, and hybrid approaches combining multiple mitigation strategies. © 2025 by the authors.

Waterflooding, a key method for secondary hydrocarbon recovery, has been employed since the early 20th century. Over time, the role of water chemistry and ions in recovery has been studied extensively. Low-salinity water (LSW) injection, a common technique since the 1930s, improves oil recovery by altering the wettability of reservoir rocks and reducing residual oil saturation. Recent developments emphasize the integration of LSW with various recovery methods such as CO2 injections, surfactants, alkali, polymers, and nanoparticles (NPs). This article offers a comprehensive perspective on how LSW injection is combined with these enhanced oil recovery (EOR) techniques, with a focus on improving oil displacement and recovery efficiency. Surfactants enhance the effectiveness of LSW by lowering interfacial tension (IFT) and improving wettability, while ASP flooding helps reduce surfactant loss and promotes in situ soap formation. Polymer injections boost oil recovery by increasing fluid viscosity and improving sweep efficiency. Nevertheless, challenges such as fine migration and unstable flow persist, requiring additional optimization. The combination of LSW with nanoparticles has shown potential in modifying wettability, adjusting viscosity, and stabilizing emulsions through careful concentration management to prevent or reduce formation damage. Finally, building on discussions around the underlying mechanisms involved in improved oil recovery and the challenges associated with each approach, this article highlights their prospects for future research and field implementation. By combining LSW with advanced EOR techniques, the oil industry can improve recovery efficiency while addressing both environmental and operational challenges. © 2025 by the authors.

Coastal high tide flooding doubled in the U.S. between 2000 and 2022 and sea level rise (SLR) due to climate change will dramatically increase exposure and vulnerability to flooding in the future. However, standards for elevating buildings in flood hazard areas, such as base flood elevations set by the Federal Emergency Management Agency, are based on historical flood data and do not account for future SLR. To increase flood resilience in flood hazard areas, federal, state, regional, and municipal planning initiatives are developing guidance to increase elevation requirements for occupied spaces in buildings. However, methods to establish a flood elevation that specifically accounts for rising sea levels (or sea level rise-adjusted design flood elevation (SLR-DFE)) are not standardized. Many municipalities or designers lack clear guidance on developing or incorporating SLR-DFEs. This study compares guidance documents, policies, and methods for establishing an SLR-DFE. The authors found that the initiatives vary in author, water level measurement starting point, SLR scenario and timeframe, SLR adjustment, freeboard, design flood elevation, application (geography and building type), and whether it is required or recommended. The tables and graph compare the different initiatives, providing a useful summary for policymakers and practitioners to develop SLR-DFE standards. © 2025 by the authors.