Search Results (306)

Flooding is a recurrent problem in many Brazilian cities, resulting in significant losses that affect health, assets, finance, and the environment. The uncertainty regarding extreme rainfall events due to climate change makes this challenge even more severe, compounded by inadequate urban planning and the occupation of risk areas, particularly for the municipality of Catu, in the state of Bahia, which also suffers from recurrent floods. Critical hotspots include the Santa Rita neighborhood and its surroundings, the main supply center, and the city center—the municipality’s commercial hub. The focus of this research is the unprecedented quantification of the socioeconomic impact of these floods on the low-income population and the region’s informal sector (street vendors). This research focused on analyzing and modeling the destructive potential of intense rainfall in the Santa Rita region (Supply Center) of Catu, Bahia, and its effects on the local economy across different recurrence intervals. A hydrological simulation software suite based on computational and geoprocessing technologies—specifically HEC-RAS 6.4, HEC-HMS 4.11, and QGIS— 3.16 was utilized. Two-dimensional (2D) modeling was applied to assess the flood-prone areas. For the socioeconomic impact assessment, a loss procedure based on linear regression was developed, which correlated the different return periods of extreme events with the potential losses. This methodology, which utilizes validated, indirect data, establishes a replicable framework adaptable to other regions facing similar socioeconomic and drainage challenges. The results revealed that the area becomes impassable during flood events, preventing commercial activities and causing significant economic losses, particularly for local market vendors. The total financial damage for the 100-year extreme event is approximately US $30,000, with the loss model achieving an R² of 0.98. The research concludes that urgent measures are necessary to mitigate flood impacts, particularly as climate change reduces the return period of extreme events. The implementation of adequate infrastructure, informed by the presented risk modeling, and public awareness are essential for reducing vulnerability. Full article

Flooding is an increasing climate risk in the UK, yet schools remain marginal in resilience planning. Flood events disrupt education, heighten pupil anxiety, increase staff workload and unsettle communities, but these experiences are rarely documented in ways that inform policy. This study examines how schools in the East and West Midlands regions of the UK have experienced and adapted to flooding. Eight qualitative case studies were undertaken in flood-affected schools using semi-structured interviews with key staff, site visits and documentary evidence. Interview transcripts were thematically analysed using NVivo to explore past flood events, levels of preparedness, and readiness for measures such as Property Flood Resilience, Sustainable Drainage Systems and Climate Action Plans. Findings show wide variation in awareness, emergency procedures and engagement with local authorities. Most schools had faced flooding or near misses but lacked formal guidance or flood-specific plans, leading to improvised responses led internally by staff. Despite limited funding, inconsistent communication and exclusion from wider planning, schools demonstrated adaptive potential and willingness to support community preparedness. The study offers evidence to guide headteachers, policymakers and local authorities in strengthening school-based flood resilience and supporting the development of a resilience innovation blueprint for flood-prone schools in the UK. Full article

The use of resource-constrained Low-Power and Lossy Networks (LLNs), where the IPv6 Routing Protocol for LLNs (RPL) is the de facto routing standard, has increased due to the Internet of Things’ (IoT) explosive growth. Because of the dynamic nature of IoT deployments and the lack of in-protocol security, RPL is still quite susceptible to routing-layer attacks like Blackhole, Lowered Rank, version number manipulation, and Flooding despite its lightweight architecture. Lightweight, data-driven intrusion detection methods are necessary since traditional cryptographic countermeasures are frequently unfeasible for LLNs. However, the lack of RPL-specific control-plane semantics in current cybersecurity datasets restricts the use of machine learning (ML) for practical anomaly identification. In order to close this gap, this work models both static and mobile networks under benign and adversarial settings by creating a novel, large-scale multiclass RPL attack dataset using Contiki-NG’s Cooja simulator. To record detailed packet-level and control-plane activity including DODAG Information Object (DIO), DODAG Information Solicitation (DIS), and Destination Advertisement Object (DAO) message statistics along with forwarding and dropping patterns and objective-function fluctuations, a protocol-aware feature extraction pipeline is developed. This dataset is used to evaluate fifteen classifiers, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbors (KNN), Random Forest (RF), Extra Trees (ET), Gradient Boosting (GB), AdaBoost (AB), and XGBoost (XGB) and several ensemble strategies like soft/hard voting, stacking, and bagging, as part of a comprehensive ML-based detection system. Numerous tests show that ensemble approaches offer better generalization and prediction performance. With overfitting gaps less than 0.006 and low cross-validation variance, the Soft Voting Classifier obtains the greatest accuracy of 99.47%, closely followed by XGBoost with 99.45% and Random Forest with 99.44%. Full article

Accurate daily runoff forecasting is essential for flood control and water resource management, yet existing models struggle with the seasonal non-stationarity and inter-basin variability of runoff sequences. This paper proposes a Season-Aware Ensemble Forecasting (SAEF) method that integrates SVM, LSSVM, LSTM, and BiLSTM models to leverage their complementary strengths in capturing nonlinear and non-stationary hydrological dynamics. SAEF employs a seasonal segmentation mechanism to divide annual runoff data into four seasons (spring, summer, autumn, winter), enhancing model responsiveness to seasonal hydrological drivers. An Improved Arctic Puffin Optimization (IAPO) algorithm optimizes the model weights, improving prediction accuracy. Beyond numerical gains, the framework also reflects seasonal runoff generation processes—such as rapid rainfall–runoff in wet seasons and baseflow contributions in dry periods—providing a physically interpretable perspective on runoff dynamics. The effectiveness of SAEF was validated through case studies in the Dongjiang Hydrological Station (China), the Elbe River (Germany), and the Quinebaug River basin (USA), using four performance metrics (MAE, RMSE, NSEC, KGE). Results indicate that SAEF achieves average Nash–Sutcliffe Efficiency Coefficient (NSEC) and Kling–Gupta efficiency (KGE) coefficients of over 0.92, and 0.90, respectively, significantly outperforming individual models (SVM, LSSVM, LSTM, BiLSTM) with RMSE reductions of up to 58.54%, 55.62%, 51.99%, and 48.14%. Overall, SAEF not only strengthens predictive accuracy across diverse climates but also advances hydrological understanding by linking data-driven ensembles with seasonal process mechanisms, thereby contributing a robust and interpretable tool for runoff forecasting. Full article

Natural hazards pose an increasing threat to infrastructures, lives, and livelihoods in alpine regions due to climate change and the growing demand for settlement space. While grey protective structures are commonly deployed to provide immediate safety, their sustainability, and thus protective function, is limited by cost-intensive maintenance. Nature-based solutions (NbS) can alleviate these shortcomings by offering cost-effective, adaptive protection that strengthens over time, making their deployment a key factor in building resilience to climate-induced hazards. This paper introduces a systematic methodology for the strategic deployment of NbS to enhance climate resilience. It integrates a three-level hazard classification system with an expert-led assessment rating 74 NbS against 29 hazards. A subsequent Principal Component Analysis (PCA) synthesises these into six functional groupings based on their shared mitigation characteristics. The core of this framework introduces two key innovations: a novel Mitigation Score and a Hazard Mitigation Profile. Together, they evaluate NbS effectiveness dynamically through the different phases of natural hazards, surpassing traditional static ratings by evaluating NbS performance across the hazard management cycle—from predisposition to post-event recovery. Significant variation in mitigation scoring was observed for individual hazard classes and types. Erosion processes (e.g., sheet, rill, and gully erosion) achieved the highest mitigation scores (1.90), as they can be addressed by many highly effective NbS (21–33 types). Conversely, flood-related hazards, such as fluvial and pluvial floods, showed moderate scores (1.64–1.66) with a balanced mix of mitigative and supportive NbS, while options for mitigating impact floods and coastal floods were far more limited (1.00–1.42). The resulting methodology provides a crucial, practical link between specific climate-related threats and viable, nature-based responses, serving as a robust framework to guide the decisions of planners, engineers, and policymakers. By enabling a more strategic and temporally aware deployment of NbS, our findings inform the development of adaptive management strategies to ensure their long-term effectiveness. Full article

The siting of renewable energy systems (RESs) in regions vulnerable to multiple climate hazards presents a critical challenge for sustainable infrastructure planning. Traditional approaches, primarily driven by static assessments of solar and wind potential, often neglect the compounded risks posed by floods, droughts, and windstorms, resulting in investments that are operationally vulnerable and economically unsustainable. This study proposes a novel spatio-temporal artificial intelligence (AI) framework for multi-objective RES deployment that integrates satellite-derived resource maps, high-resolution hazard data, and dynamic climate time series into a unified optimization pipeline. The methodology employs a gated recurrent unit (GRU)-based encoder to capture temporal hazard dynamics, combined with a multi-objective evolutionary algorithm (NSGA-II) to balance energy yield and resilience. A case study in South Africa’s Vhembe District demonstrates the framework’s effectiveness: the proposed model reduces the average hazard exposure by 31.6% while preserving 96.4% of the baseline energy output. Attention-based saliency analysis reveals that flood and windstorm hazards are the dominant drivers of site exclusion. Compared to conventional siting methods, the proposed framework achieves superior trade-offs between performance and risk, ensuring alignment with South Africa’s Just Energy Transition and Climate Adaptation strategies. The results confirm the value of spatio-temporal embeddings and hazard-aware multi-objective optimization in guiding resilient, data-driven energy infrastructure development. This model offers direct benefits to energy planners, climate adaptation agencies, and policymakers seeking to implement resilient, data-driven renewable energy strategies in hazard-prone regions. Full article

Accurate water-level prediction is a critical component for ensuring safe maritime navigation, optimizing port operations, and mitigating coastal flooding risks. However, the complex, non-linear spatiotemporal dynamics of water systems pose significant challenges for current forecasting models. The proposed framework introduces three key innovations. First, a dual-weight graph construction mechanism integrates geographical proximity with Dynamic Time Warping (DTW)-derived temporal similarity to better represent hydrodynamic connectivity in coastal and estuarine environments. Second, a state-aware weighted loss function is designed to enhance predictive accuracy during critical hydrological events, such as storm surges and extreme tides, by prioritizing the reduction in errors in these high-risk periods. Third, the WS-STGTN architecture combines graph attention with temporal self-attention to capture long-range dependencies in both space and time. Extensive experiments are conducted using water-level data from five stations in the tidal-influenced lower Yangtze River, a vital artery for shipping and a region susceptible to coastal hydrological extremes. The results demonstrate that the model consistently surpasses a range of baseline methods. Notably, the WS-STGTN achieves an average reduction in Mean Squared Error (MSE) of 27.6% compared to the standard Transformer model, along with the highest coefficient of determination ($R^2\approx 0.96$) across all datasets, indicating its stronger explanatory power for observed water-level variability. This work provides a powerful tool that can be directly applied to improve coastal risk management, marine navigation safety, and the operational planning of port and coastal engineering projects. Full article

As urban areas in developing countries, including South Africa, continue to grapple with the adverse challenges of climate change and rapid population growth, there is an increasing call for nature-inspired solutions. This is because nature-based solutions (NbSs) can significantly enhance urban resilience by managing stormwater, reducing flooding and creating livable spaces within urban centers. One such NbS is permeable pavement, which has gained attention for its ability to allow water to infiltrate rather than run off. However, while its use is growing in developed nations, the story is not the same in South Africa, where the literature is silent on its usage and issues of flooding and other associated disasters have persisted. Therefore, this study adopts a post-positivist approach to investigate the application and challenges of permeable pavements as an NbS in South African urban areas. The study reveals a low level of permeable pavement use, albeit an encouraging level of awareness among built environment professionals. Covariance-based structural equation modelling further revealed the significant causes of this poor application. The findings provide valuable insights for policymakers to create incentives and frameworks that promote permeable pavement adoption in urban areas facing environmental challenges. Moreover, this research contributes to the limited literature on NbSs in South Africa, offering a foundation for future studies and addressing the pressing need for innovative solutions to flooding and urban resilience. Full article

Intensifying urbanization in Central Europe is increasingly pushing flood retention areas onto private farmland, yet the agronomic and socio-economic trade-offs remain poorly quantified. We conducted a narrative review of published field data and post-event assessments from 2014–2023 along the transboundary Sava River. Information was collected from research articles, case studies, and environmental monitoring reports, and synthesized in relation to national and EU regulatory thresholds to evaluate how floods altered soil functions and agricultural viability. Water erosion during floods stripped up to 30 cm of topsoil in torrential reaches, while stagnant inundation deposited 5–50 cm of sediments enriched with potentially toxic elements, occasionally causing food crops to exceed EU contaminant limits due to uptake from the soil. Flood sediments also introduced persistent organic pollutants: 13 modern pesticides were detected post-flood in soils, with several exceeding sediment quality guidelines. Waterlogging reduced maize, pumpkin, and forage yields by half where soil remained submerged for more than three days, with farm income falling by approximately 50% in the most affected areas. These impacts contrast with limited public awareness of long-term soil degradation, raising questions about the appropriateness of placing additional dry retention reservoirs—an example of nature-based solutions—on agricultural land. We argue that equitable flood-risk governance in the Sava River Basin requires: (i) a trans-boundary soil quality monitoring network linking agronomic, hydrological, and contaminant datasets; (ii) compensation schemes for agricultural landowners that account for both immediate crop losses and delayed remediation costs; and (iii) integration of strict farmland protection clauses into spatial planning, favoring compact, greener cities over lateral river expansion. Such measures would balance societal flood-safety gains with the long-term productivity and food security functions of agricultural land. Full article

This study investigates Egyptian seafarers’ perceptions, barriers, and adoption intentions towards renewable and low-carbon energy technologies. Recognizing the maritime sector’s significant contribution to global emissions and Egypt’s strategic role via the Suez Canal, the authors conducted a cross-sectional survey of 120 seafarers covering masters, engineers, and cadets. A questionnaire gauged familiarity with renewable energy, perceived relevance to maritime work, preferred energy sources, and factors influencing choice and perceived enablers, and results were analyzed using descriptive statistics and Fisher–Freeman–Halton exact tests. Respondents showed moderate–high awareness of renewable energy. Climate change was primarily associated with sea level rise, rising temperatures, and flooding. Most participants considered renewable energy highly relevant to maritime operations, with stronger endorsement from masters and second mates than from first mates. Solar, wind, and hydrogen were viewed as having the greatest future potential, while availability and cost effectiveness were critical selection factors. Advanced technology and better training were the most valued enablers, whereas high investment costs, limited infrastructure, safety concerns, and training gaps were key barriers. The findings suggest that, although Egyptian seafarers recognize the importance of renewable energy, the main barriers consist of establishment cost, needed infrastructure, safety, and necessity for training. Full article

Existing studies on adaptive capacity often focus on isolated theoretical aspects of the concept, without offering practical tools for climate policy. Moreover, gaps remain in integrating public participation into adaptation strategies and in extending research beyond specific climate-related threats, such as flooding. Current climate adaptation plans usually rely on public statistics, which are not accurate enough to reflect adaptive capacity at the local level. Improving such plans requires incorporating local knowledge and adequately addressing the needs of vulnerable groups. This article proposes a survey-based tool for measuring social adaptive capacity, providing policymakers with detailed insights into a community’s ability to cope with climate change. The tool was tested while developing a climate adaptation plan for a medium-sized city in Poland. A total of 238 responses were analysed, applying basic and non-parametric statistical methods across four key variables: risk perception of climate change, perceived adaptive capacity, adaptation motivation, and adaptation behaviour. Findings revealed that residents were aware of climate change and believed in the necessity of adaptation. To translate this awareness into sustainable action, local authorities should raise individual responsibility, offer technical and financial guidance, provide various forms of financial assistance, and strengthen social capital, which could increase participation in grassroots initiatives. Full article

Levees are critical for flood defence, but their integrity is threatened by hazards such as piping and seepage, especially during high-water-level periods. Traditional manual inspections for these hazards and associated emergency response elements, such as personnel and assets, are inefficient and often impractical. While UAV-based remote sensing offers a promising alternative, the effective fusion of multi-modal data and the scarcity of labelled data for supervised model training remain significant challenges. To overcome these limitations, this paper reframes levee monitoring as an unsupervised anomaly detection task. We propose a novel, fully automated framework that unifies geophysical hazards and emergency response elements into a single analytical category of “abnormal targets” for comprehensive situational awareness. The framework consists of three key modules: (1) a state-of-the-art registration algorithm to precisely align infrared and visible images; (2) a generative adversarial network to fuse the thermal information from IR images with the textural details from visible images; and (3) an adaptive, unsupervised segmentation module where a mean-shift clustering algorithm, with its hyperparameters automatically tuned by Bayesian optimization, delineates the targets. We validated our framework on a real-world dataset collected from a levee on the Pajiang River, China. The proposed method demonstrates superior performance over all baselines, achieving an Intersection over Union of 0.348 and a macro F1-Score of 0.479. This work provides a practical, training-free solution for comprehensive levee monitoring and demonstrates the synergistic potential of multi-modal fusion and automated machine learning for disaster management. Full article

The real-time forecast estimates of total water levels (TWL) associated with coastal storms by the Boston Office of the National Weather Service (NWS), and the analysis, identification, and field mapping of storm tide pathways (STP) by the Center for Coastal Studies (CCS) within the forecast region, has led to improved model forecasts, enhanced allocation of resources prior to storm impact (e.g., placement of flood control measures, identification of evacuation routes, development of applications to visualize and communicate threats, etc.), and increased public awareness of the practical implications of sea level rise and storm-related coastal flooding. Both NWS modeling and STP mapping are discussed here. The coupling of these methods began in 2016–2017 in Provincetown, MA, and its utility was highlighted during the new storm of record for most of southern New England, a nor’easter in January 2018. The use of this information by managers, stakeholders, and the public has increased since combining the TWL modeling and STP mapping in an online portal in 2021, and it continues to be used by emergency managers and the public to plan for approaching coastal storms. Full article

Monitoring urban pluvial floods remains a challenge, particularly in dense city environments where drainage overflows are localized, and sensor-based systems are often impractical. Physical sensors can be costly, prone to theft, and difficult to maintain in areas with high human activity. To address this, we developed an innovative flood detection framework that utilizes publicly accessible CCTV imagery and large language models (LLMs) to classify flooding conditions directly from images using natural language prompts. The system was tested in Bandung, Indonesia, across 340 CCTV locations over a one-year period. Four multimodal LLMs, ChatGPT-4.1, Gemini 2.5 Pro, Mistral Pixtral, and DeepSeek-VL Janus, were evaluated based on classification accuracy and operational cost. ChatGPT-4.1 achieved the highest overall accuracy at 85%, with higher performance during the daytime (89%) and lower accuracy at night (78%). A cost analysis showed that deploying GPT-4.1 every 15 min across all locations would require approximately USD 59,568 per year. However, using compact models like GPT-4 nano could reduce costs by up to seven times, with minimal loss of accuracy. These results highlight the trade-off between performance and affordability, especially in developing regions. This approach offers a scalable, passive flood monitoring solution that can be integrated into early warning systems. Future improvements may include multi-frame image analysis, automated confidence filtering, and multi-level flood classification for enhanced situational awareness. Full article

Debris flows are rapid mass movements of water-laden debris that flow down mountainsides into valley channels and eventually settle on valley floors. The risk of debris flows can be significantly increased after wildfires. Following the destructive 2021 debris flows in Glenwood Canyon, the Colorado Geological Survey (CGS) initiated a LiDAR-Based Alluvial Fan Mapping Project to improve geologic hazard delineation of alluvial and high-angle fans in response to developing wildfire-ready watersheds. These landforms, shaped by episodic sediment-laden flows, pose significant risks and are often misrepresented on conventional geologic maps. CGS delineated fan-shaped landforms with improved precision using 1-m resolution LiDAR-based DEMs, DEM-derived terrain metrics, hydrologic analysis, and geospatial analysis tools within the ArcGIS Pro platform. Our results reveal previously unmapped or misclassified alluvial or high-angle fans in areas undergoing increasing development pressure, where low-gradient terrain indicates a high hazard potential. Through this study, over 3200 alluvial and high-angle fan polygons were delineated across six Colorado counties, encompassing approximately 81 km² of alluvial fans and 54 km² of high-angle fans. High-resolution LiDAR data, geospatial analytical techniques, and systematic QA/QC protocols were used to support refined hazard awareness. The resulting dataset enhances proactive land-use planning and wildfire resilience by identifying areas prone to debris flow and flood hazards. These maps are intended for regional screening and planning purposes and are not intended for site-specific design. These maps also serve as a critical resource for prioritizing geologic evaluations and guiding mitigation planning across Colorado’s wildfire-affected landscapes. Full article