Search Results (2,094)

Accurate short-term flood forecasting is vital for effective risk management and early warning systems. However, many data-driven models struggle to generalise with limited historical data and fail to consistently capture complex temporal dependencies across varying forecasting horizons. To address these challenges, this study proposes GHDFloodNet (Generalised Hybrid Data-limited Flood Prediction Network), a hybrid deep learning framework designed for robust multi-step-ahead forecasting. GHDFloodNet integrates First-Order Model-Agnostic Meta-Learning (FOMAML) with a Temporal Fusion Transformer (TFT) to enable rapid task adaptation and effectively capture long-range temporal dependencies and variable interactions. To further enhance predictive consistency, the framework incorporates a bidirectional Long Short-Term Memory (BiLSTM) network augmented with an additive attention mechanism and static feature fusion as a core learner within a meta-ensemble architecture. Bayesian hyperparameter optimisation within an AutoML framework identifies optimal model configurations, while a dedicated data handling layer with real-time augmentation improves stability under non-stationary conditions. The framework was evaluated for multi-horizon water level forecasting across four lead time ranges (1–6 h, 6–12 h, 12–24 h, and 24–48 h) using rainfall and lagged water level observations as primary inputs. Experimental results demonstrate that GHDFloodNet achieves robust, nearly invariant error distributions across the full 1–48 h forecast window, reporting an MSE of 0.53–0.55, RMSE of 0.72–0.74, and MAE of 0.35–0.36. Furthermore, the model exhibits stable goodness-of-fit, with R² and NSE values consistently ranging from 0.44 to 0.47 across all lead times, significantly outperforming conventional baselines, which typically exhibit pronounced error escalation at longer horizons. Overall, GHDFloodNet demonstrates that horizon-independent forecast reliability can be architecturally engineered, offering critical value for operational flood forecasting where consistent performance across all lead times outweighs peak short-range precision. Full article

Tropical mid-size cities on alluvial floodplains face compounded flood challenges combining pluvial accumulation from intense convective storms, regulated river overflow, and aging drainage networks. This study presents an integrated framework for Monteria, Colombia (~450,000 inhabitants; Sinu River, Caribbean lowlands), within Colombian Decree 1807/2014 and structured in four phases. (1) Hazard: A Rain-on-Grid 2D HEC-RAS 6.6 model covering 4090 ha, calibrated against four gauged events, identifies three dominant pluvial mechanisms (poor hydraulic connectivity, limited evacuation capacity, downstream channel overflow), plus 17 critical fluvial erosion points affecting ~289 properties at 100-year return period. (2) Vulnerability: Depth-damage functions from 1465 household surveys yield 36.36% of 3015 assets in high risk and 57.77% in medium risk. (3) Measures: Scenario M2 (channel widening plus dikes, land-raising, retention lagoons) removes 80 ha of flooding while displacing 28 ha at COP 845 million pre-design cost. Non-structural measures include a Sustainable Urban Drainage Master Plan, IoT-based Early Warning System, minimum construction-elevation map, and land-management instruments. A Monte Carlo residual-risk model reduces baseline risk to 19.9% under full implementation. (4) Emergency: A February 2026 cold-front event was addressed with a 4300 m perimeter dike and six pump stations deployed jointly by the Regional Environmental Authority (CVS) and Municipal Administration. Full article

Flooding in the Kathmandu Valley has intensified in recent years due to rapid urbanization, unregulated land-use change, and insufficient drainage infrastructure. Existing flood hazard assessments are often based on low-resolution datasets and lack proper field validation. This study presents an integrated flood modeling framework that combines Unmanned Aerial Vehicle (UAV)-derived Digital Elevation Models (DEMs), field-based flood measurements, and hydrological simulations to assess urban flood hazards in the Bagmati-Nakkhu confluence, Nepal. High-resolution UAV-derived DEM and field survey data, including flood marks and high-water levels, were used as the foundation for the analysis. Hydrological modeling was conducted using the Hydrologic Engineering Center—Hydrologic Modeling System (HEC-HMS) to estimate the peak discharges of the Nakkhu River (2000–2024), which were then used to derive design flows for return periods of 5 to 150 years using the Gumbel distribution. These flows were used as boundary condition inputs for the Hydrologic Engineering Center—River Analysis System (HEC-RAS) to simulate flood depth and inundation extent under different scenarios. Flood extents for the 27 September 2024 event were derived from Sentinel-2 imagery and validated against surveyed flood marks. Additionally, land use/land cover (LULC) mapping based on UAV data was used to support flood impact analysis. The results show that flood depths ranged from approximately 0.5 m to 2.8 m, with inundation areas increasing by 35–50% under extreme rainfall. Model validation demonstrated strong agreement with simulated results, with deviations generally within ±0.3–0.5 m. Scenario analysis further indicates that urban expansion significantly increases runoff and flood extent, particularly in low-lying areas near the river confluence. Socio-economic exposure analysis for the 27 September 2024 event indicates that approximately 2569 residents (56.4% of the study zone population) and 4.011 km (77.42%) of the local road network were exposed to inundation. Overall, the results demonstrate that integrating high-resolution UAV data, field observations, and hydrological modeling greatly improves the accuracy and reliability of flood hazard assessments in data-scarce urban environments. Full article

Rapid increase in mining activities, outdated management approaches, and climate change pose risks to the safe operation of mines. We explore public databases on mine tailings storage facilities (TSF) in Kazakhstan, a major mineral producer. We proceed to an in-depth analysis of a representative TSF, located in an area that has been affected by spring flooding. Our geospatial analysis and review of company reports reveal serious challenges related to the TSF design, tailings deposition patterns, and changing weather conditions. Despite modifying the TSF design in response to its failure, the company has struggled with persistent TSF overtopping and seepage in the subsequent years. Our findings from both the country-level review of TSF and the case study highlight the urgency of adopting best practices of TSF management. Specifically, our study demonstrates that risks stemming from spring flooding in Kazakhstan call for proactive TSF management, transparency, and stakeholder engagement. Such changes in TSF governance are essential for achieving a number of Sustainable Development Goals, in particular, SDG 12 Responsible Consumption and Production and SDG6 Clean Water and Sanitation. Full article

Cape Coast is a prominent tourism destination in Ghana, distinguished by its historical landmarks, coastal ecosystems, and cultural heritage. Yet the city faces mounting threats from environmental hazards such as coastal erosion, flooding, extreme heat, and lagoon degradation, which directly compromise the sustainability of its tourism sector. Guided by the Sustainable Tourism Development Theory (STDT) and the Tourism Resilience and Adaptation Theory (TRAT), this study investigates the impacts of these hazards on tourism development, the effectiveness of current disaster risk reduction (DRR) strategies, and the roles of key stakeholders in building sectoral resilience. Using a qualitative research design, data were collected through in-depth interviews with eighteen stakeholders comprising four policymakers, six community leaders, five tourism business operators, and three representatives from non-governmental organisations, alongside documentary analysis of four institutional reports. The study contributes to the literature by demonstrating that fragmented, reactive DRR strategies and weak stakeholder coordination undermine Cape Coast’s tourism resilience, and by showing how urban natural assets, a dimension largely neglected in existing tourism–DRR scholarship, are central to both hazard exposure and adaptive capacity. The findings call for integrated, ecosystem-based DRR frameworks that align governance mechanisms with sustainable tourism imperatives. Full article

Indian metropolitan cities such as Mumbai grapple with rapid urbanisation, extreme urban density, high built-up areas, loss of green cover, and shrinking open spaces, resulting in increased impermeable surfaces, urban heat island effects, and frequent flooding occurrences. Modern stormwater management has increasingly been characterised by integrated grey-green approaches; however, cities in the Global North benefit from established policies, technical expertise, and financial resources that enable the systematic and large-scale integration of Blue-Green Infrastructure (BGI) through district-wide geospatial assessment frameworks, unlike many cities in the Global South. Despite growing interest in nature-based stormwater solutions, there remains a dearth of geospatial empirical research from India examining the placement, distribution, performance, and functionality of BGI integrated with existing stormwater management systems in cities such as Mumbai. Furthermore, hydrological modelling using tools such as the Storm Water Management Model (SWMM) for the design, planning, and implementation of BGI in Indian cities remains largely unexplored. This study explores the role of BGI strategies in improving urban stormwater management within high-density Indian cities under a 25-year return period extreme rainfall scenario. Using an integrated approach that combines QGIS-based spatial analysis with EPA-SWMM hydrologic-hydraulic modelling, the research examines runoff behaviour, identifies flooding hotspots, and evaluates the effectiveness of Low Impact Development (LID)-based BGI measures such as permeable pavements, infiltration trenches, and green roofs applied at the ward level in Mumbai’s F/North and G/North Wards. Detailed land use classification, spatial mapping, and rainfall simulation corresponding specifically to a 25-year return period rainfall event was used to assess pre- and post-intervention conditions. The findings indicate that the applied BGI measures led to a 12.6% reduction in peak runoff (137.6 m³/s to 120.2 m³/s) and a 5.5% decrease in total runoff volume (783,510 m³ to 740,410 m³). More importantly, the peak flooding flow rate decreased by 45% (94.1 m³/s to 51.7 m³/s), demonstrating that BGI measures can efficiently reduce peak flooding flows by extending runoff hydrographs during extreme rainfall events. These findings are specifically applicable to the simulated 25-year return period extreme rainfall scenario and may vary under different rainfall intensities or return periods. Less extreme events could potentially experience even greater relative reductions or prevent flooding altogether, while also easing downstream hydraulic loads. Overall, strategically placed BGI interventions can significantly reduce surface runoff and peak flow, thereby enhancing stormwater resilience within spatially constrained urban environments. This study provides a replicable, data-driven framework for catchment-scale stormwater planning in dense Indian cities under extreme rainfall conditions, offering practical insights into methods, local contextual considerations, and spatial planning strategies for policymakers and urban planners seeking to retrofit and adapt existing infrastructure under increasing hydrologic stress and climate variability. Full article

Urban environments increasingly rely on interconnected digital infrastructures like IoT devices, SDN-enabled networks, and cloud platforms to support essential municipal services. Ensuring the resilience of these systems requires advanced, data-driven mechanisms capable of detecting and mitigating cyber disruptions. This study presents Persian Eagle, a hybrid machine learning and deep learning framework designed to enhance the cyber-resilience of urban digital infrastructures by providing high-precision detection of Distributed Denial of Service (DDoS) attacks. DDoS attacks disrupt service availability by flooding targets with massive malicious traffic orchestrated through botnets, and in critical infrastructures, disruptions can be life-threatening. The proposed framework integrates multi-stage data preprocessing, SMOTE-based class balancing, and a four-phase feature-selection pipeline combining filtering, statistical ranking, PCA, and XGBoost. Seven complementary classifiers, including Random Forest, SVM, Gaussian Naive Bayes, XGBoost, MLP, LSTM, and Autoencoder, are bonded through a stacking cooperative with a Gradient Boosting meta-learner. The framework was evaluated on CICDDoS2019 and CICIDS2017 datasets, and achieved near-perfect performance up to 99.9998% accuracy, demonstrating strong generalization across diverse attack scenarios. By offering a scalable, transparent, and data-driven detection mechanism, Persian Eagle maintains urban digital-risk management and supports the continuity and resilience of critical smart-city services. Full article

Flooding poses a significant threat to commercial real estate investment, disrupting business operations, escalating maintenance costs, and heightening investment uncertainty, particularly in coastal and low-lying urban environments. This study examines the social dimensions of climate vulnerability by investigating how flood risk shapes stakeholders’ decisions to invest in commercial properties within flood-prone urban areas, with a focus on Lekki Phase 1, Lagos, Nigeria. A quantitative survey design was adopted. Data were collected from 87 commercial property investors through a structured questionnaire (FIIFRZQ) measured on a four-point Likert-type scale. The instrument demonstrated acceptable overall internal consistency (Cronbach’s α = 0.72), with subscale α values ranging from 0.62 to 0.81. Multiple regression analysis was used to assess the joint and individual contributions of seven factor categories (environmental, legal, economic, neighbourhood, structural, locational and behavioural) to investors’ willingness to invest in commercial property that is at risk of flooding. The seven predictors collectively explained 61.2% of the variance in investment willingness (R² = 0.612; F(7, 79) = 17.91; p < 0.001). Five factors, namely legal, environmental, structural, economic, and locational, were statistically significant contributors to investment willingness, while neighbourhood and behavioural factors were not. Johnson’s relative weights analysis confirmed legal and environmental considerations as the dominant drivers. The findings illuminate the interplay between climate vulnerability and investor behaviour in urban real estate markets, with actionable implications for policymakers, real estate practitioners, and investors navigating decision-making in flood-exposed urban environments. Full article

Rapid urbanization, climate change, and biodiversity loss are intensifying environmental pressures on arid coastal cities through extreme heat, water scarcity, salinity intrusion, and increasing flood risks. Despite substantial investment in urban green spaces across the Gulf region, many public parks provide limited ecological functionality and climate adaptation benefits. This study evaluated the ecological performance of three coastal parks in Muscat, Oman Sarooj Beach Park (23,080 m²), Ghubrah Beach Park (34,818 m²), and Al Athaiba Beach Park (17,370 m²), to identify opportunities for more resilient landscape design. The assessment revealed that although green space occupied 76.8–82% of park areas, tree canopy cover remained low (8–12%), limiting thermal comfort, habitat provision, and ecological performance. Based on these findings, a Functional and Climate-Responsive Planting Strategy (FCRPS) was developed by integrating the 10–20–30 biodiversity guideline with performance-based planting criteria tailored to arid and saline environments. The framework was applied to the proposed Majis Beach Ecological Park in Sohar, Oman, to demonstrate the implementation of ecological urbanism and nature-based solutions in a hyper-arid coastal environment. The resulting design incorporates biodiversity-enhancing planting, blue–green infrastructure, wetland restoration, and climate-responsive spatial planning. The study demonstrates how multifunctional landscapes can enhance biodiversity, improve thermal comfort, strengthen stormwater management, and support community well-being while providing a transferable framework for resilient public park design in arid coastal cities. Full article

To address the critical safety challenges of flooding induced by ship–ice collisions in Arctic shipping routes, this study proposes an Observe–Orient–Predict–Decide–Act (OODA-P)-enhanced closed-loop intelligent damage control decision-support framework integrated with predictive simulation. To address the limitations of existing systems—namely, weak polar adaptability and the absence of a decision feedback loop—this research presents three core findings: (1) A fast time-domain floating condition model was developed by coupling topside icing with progressive flooding. Numerical simulations indicate that neglecting ice accretion leads to an underestimation of the long-term heel angle and transverse stability by 4.4% and 4.5%, respectively, validating the necessity of incorporating coupled ice loads. (2) A serial dual-channel prediction and evaluation mechanism, integrating “situation evolution prediction” and “decision efficacy evaluation,” was designed. This mechanism can proactively forecast long-term deterioration trends in the floating condition within 0.3147 s of acquiring damage information, capable of identifying and flagging potentially high-risk emergency plans before their execution, thus preventing adverse outcomes. (3) The proposed framework was validated through typical polar scenarios and 111 damage control training sessions across three batches, with the full-loop logic flow completing in under 3 s. Compared with the traditional OODA loop, the average emergency response time was reduced from 26.9 to 22.7 min (a 15.5% reduction), while the initial response success rate improved from 74.7% to 97.3% in a simulated training environment. By enabling “virtual trial-and-error” prior to execution, this framework demonstrates the potential to augment traditional experience-based damage control with proactive, simulation-driven decision support, marking a step towards more intelligent interventions. Through the explicit coupling of topside icing and progressive flooding into real-time predictions, this work provides a foundation for further development of polar-adaptable intelligent damage control systems. Full article

The development of Software-Defined Networks (SDNs) introduces new challenges in network security, particularly in detecting Distributed Denial of Service (DDoS) attacks and network anomalies. Due to the centralized architecture of SDN, traditional detection methods are often insufficient in dynamic environments. Therefore, machine learning techniques are increasingly applied to improve detection effectiveness. This paper analyzes the impact of network topology on the performance of machine learning-based detection methods in SDN environments. A controlled experimental setup based on the RYU controller and OpenFlow 1.3 was implemented using Mininet. Two network topologies (linear and hierarchical) were evaluated under multiple attack scenarios, including TCP SYN flood and TCP/UDP port scanning. Two supervised learning models, Random Forest (RF) and K-Nearest Neighbors (KNN), were implemented and compared using standard evaluation metrics: accuracy, precision, recall, F1-score, and detection time. The results show that Random Forest significantly outperforms KNN, achieving up to 100% accuracy and detection times as low as 4.24 s, while KNN exhibits lower stability and reduced recall in anomaly detection scenarios. The study demonstrates that network topology has a measurable impact on both detection performance and latency. The observed effects varied across attack scenarios and machine learning models. Hierarchical topology generally improved detection sensitivity in DDoS scenarios, while linear topology often enabled lower detection latency during selected anomaly detection experiments. The results indicate that both machine learning model selection and network topology should be jointly considered when designing intrusion detection systems for SDN environments. These findings contribute to improving the effectiveness and responsiveness of security mechanisms in modern programmable networks. Full article

Multiple factors such as global climate warming and environmental degradation have increased natural disaster frequencies, threatening the safety of citizens’ lives and properties seriously. Existing literature primarily focuses on livelihood diversification, resilience, and vulnerability in flood-prone areas, with limited research systematically examining farmers’ livelihood risks and management strategies across multiple dimensions. To address this gap, this study advances the understanding of multidimensional livelihood risks by systematically identifying the key risk perceptions and management strategy choices of farmers, thereby providing empirical evidence essential for designing targeted interventions and sustainable adaptation policies in flood-prone regions. Specifically, this study employs an unordered multinomial logistic model to examine farmers’ risk management strategy choices, drawing on a field survey of 540 farmers from floodplain areas in Sichuan Province, China. The analysis systematically covers four livelihood risk dimensions (health, environmental, financial, social) and five management strategies (expansion, adjustment-oriented, contraction, aid-oriented, dependency-based). The results indicate that: (1) The most significant livelihood risk is environmental, and the most commonly selected risk management strategy is adjustment-oriented management; (2) When farmers face health risks, they tend to adopt dependency-based management strategy; in dealing with financial and social risks, farmers perceive no significant difference in the selection of the five management strategies. Accordingly, targeted strategies are proposed: insurance and information for environmental risks, medical security for health, employment training for social, and income diversification for financial risks. Full article

The relationship between water and sediment in the lower reaches of the Yellow River is uncoordinated, leading to frequent floods. In this area, the floodplain is situated below the main channel and embankment foundations, increasing the likelihood of overbank flooding. Ecological protective forests serve as a nature-based mitigation measure by reducing flow velocities along embankments and lowering the risk of structural failure during near-bank flood events. To assess the role of ecological protective forests, laboratory experiments were conducted, and field data informed parameterization and geometry selection. A total of 24 scenarios were designed, combining four forest arrangements (A1, A2, A3, and A4), two submergence degrees (H₀/H = 0.5 and 1.0), and three water and sediment conditions. Results show that sediment deposition increases with vegetation density. Under constant vegetation density and embankment-aligned flow, a larger along-flow to cross-flow spacing ratio promoted deposition upstream, whereas a smaller ratio extended deposition further downstream. Deposition thickness was greater under fully submerged conditions than under semi-submerged conditions. Among the arrangements, sediment deposition effectiveness followed the order A1 > A2 > A4 > A3, with arrangement A1 providing the strongest promotion of deposition. Under varying flow–sediment conditions, the A1 arrangement enhanced sediment deposition by 6.8% to 20.6%. Flow structure was also modified: under semi-submerged conditions, the vertical profile of longitudinal velocity approximated a logarithmic distribution, whereas full submergence produced a different profile due to combined drag from tree trunks and canopy. Vertical sediment concentration profiles were similar under both submerged states, with minimum values near the water surface and maximum concentrations near the bottom. These changes confirm that ecological protective forests contributed to reducing flow velocity and diminishing sediment transport capacity. Full article

Reservoir conformance control is a necessary production measure in the oil field, which significantly impacts the efficiency of enhanced oil recovery (EOR). Polymers, hydrophobic associating polymers (HAPs), polymer microgels (MGs), and preformed particle gel (PPG) are typical polymer/gel dispersion fluids that are widely used as conformance control agents. Currently, there is still no combined conformance control method to realize the equilibrium production of the reservoir. This paper first evaluates the reservoir adaptability of polymers, HAPs, and MGs by the three-parallel core displacement experiments. Then, the displacement equilibrium factor (DEF) was established by comprehensively considering the profile improvement, oil increment, and oil recovery to optimize the fluid switching time. Based on the above oil displacement experiments, a scatter plot of the DEF with respect to the ultimate recovery of each layer can be plotted, which has an inflection point when the DEF is 45%. When the DEF is lower than 45%, the difference in the oil displacement effect of each layer is enhanced. Therefore, the best time to switch the injection fluid is when the DEF is reduced to 45%. Finally, based on the above results, a graph guiding the combined conformance control method under different reservoir variation coefficients and reservoir median permeability was established, and an equilibrium production method for heterogeneous reservoirs was developed. The five-parallel core flooding experiments with the DEF < 45% as the switching guidance can increase the oil recovery by 17.79% based on association polymer flooding, which is 9.68% higher than that of the conventional conformance control method. This paper can provide theoretical and experimental support for the optimal design of conformance control in oilfields. Full article

Traditional reservoir flood control operations in China have long relied on a fixed flood-limited water level (FLWL), which frequently results in the underutilization of water resources during flood seasons. Dynamic FLWL regulation and joint reservoir operation have emerged as core strategies to optimize floodwater resource utilization while ensuring flood control safety. However, these approaches typically treat the flood control storage capacity of individual reservoirs as fixed constraints, failing to consider the potential for reallocating this capacity within a cascade reservoir system. This study explores the concept of “equivalent utilization of flood control storage capacity” among cascade reservoirs. Focusing on the four major reservoirs (Wudongde, Baihetan, Xiluodu, and Xiangjiaba) in the lower reaches of the Jinsha River, a methodology for analyzing the equivalent index of their flood control storage capacity is established. The core of this methodology involves a two-round scheduling simulation under various design flood scenarios. The first round of simulation adheres to standard operating rules, while the second round allows upstream reservoirs to retain additional flood volume—with downstream reservoirs correspondingly reducing their outflow—on the premise that downstream safety targets are satisfied. The equivalent index is defined as the ratio of the reduced storage capacity utilized downstream to the additional storage capacity utilized upstream. Nine design flood scenarios (covering three typical years with 1%, 2%, and 5% exceedance probabilities) for flood control in the Sichuan–Chongqing reach were analyzed, with the tightly coupled Wudongde–Baihetan and Xiluodu–Xiangjiaba reservoir pairs treated as two integrated units. The results indicate that the equivalent indices between these two reservoir groups range from 0.96 to 0.999, demonstrating near-perfect functional interchangeability of their flood control storage capacities for the specified research objective. For practical engineering application, a value of 0.96 is recommended as the lower-bound equivalent index. This study provides a methodological framework and specific index to support the dynamic, coordinated, and more efficient utilization of flood control storage capacity in large-scale cascade reservoir systems. Full article