Search Results (172)

This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas. Floods are among the most frequent and devastating natural hazards, particularly in developing countries such as Myanmar, where monsoon-driven rainfall and inadequate flood-control infrastructure exacerbate disaster impacts. This study presents a satellite-driven and interpretable framework for high-resolution flood susceptibility and risk assessment by integrating multi-source remote sensing and geospatial data with ensemble machine-learning models—Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM)—implemented on the Google Earth Engine (GEE) platform. Eleven satellite- and GIS-derived predictors were used, including the Digital Elevation Model (DEM), slope, curvature, precipitation frequency, the Normalized Difference Vegetation Index (NDVI), land-use type, and distance to rivers, to develop flood susceptibility models. The Jenks natural breaks method was applied to classify flood susceptibility into five categories across Myanmar. Both models achieved excellent predictive performance, with area under the receiver operating characteristic curve (AUC) values of 0.943 for XGBoost and 0.936 for LightGBM, effectively distinguishing flood-prone from non-prone areas. XGBoost estimated that 26.1% of Myanmar’s territory falls within medium- to high-susceptibility zones, while LightGBM yielded a similar estimate of 25.3%. High-susceptibility regions were concentrated in the Ayeyarwady Delta, Rakhine coastal plains, and the Yangon region. SHapley Additive exPlanations (SHAP) analysis identified precipitation frequency, NDVI, and DEM as dominant factors, highlighting the ability of satellite-observed environmental indicators to capture flood-relevant surface processes. To incorporate exposure, population density and nighttime-light intensity were integrated with the susceptibility results to construct a natural–social flood risk framework. This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas. Full article

Climate change and sea-level change (SLC) are intensifying flooding in U.S. coastal communities, with disproportionate impacts on Black and minority neighborhoods that face displacement, economic hardship, and heightened health risks. In Norfolk, Virginia, sea levels are projected to rise by at least 0.91 m (3 ft) by 2100, placing underserved neighborhoods such as Oakleaf Forest at particular risk. This study investigates the compounded impacts of flooding at both the building and urban scales, situating the work within the framework of the UN Sustainable Development Goals (UN SDGs). A mixed-method, community-based approach was employed, integrating literature review, field observations, and community engagement to identify flooding hotspots, document lived experiences, and determine preferences for adaptation strategies. Community participants contributed actively through mapping sessions and meetings, providing feedback on adaptation strategies to ensure that the process was collaborative, place-based, and context-specific. Preliminary findings highlight recurring flood-related vulnerabilities and the need for interventions that address both environmental and social dimensions of resilience. The study proposes multi-scale, nature-based solutions (NbS) to mitigate flooding, restore ecological functions, and enhance community capacity for adaptation. Ultimately, this work underscores the importance of coupling technical strategies with participatory processes to strengthen resilience and advance climate justice in vulnerable coastal neighborhoods. Full article

The Iraqi coastline in the northern Persian Gulf is highly vulnerable to the impacts of future sea level rise. This study introduces a novel approach in the Arc Geographic Information System (ArcGIS) for inundation risk of the 58 km Iraqi coast of the northern Persian Gulf through a combination of multi-data sources, machine-learning predictions, and hydrological connectivity by Landsat. The Prophet/Neural Prophet time-series framework was used to extrapolate future sea level rise with 11 satellite altimetry missions that span 1993–2023. The coastline was obtained by using the Landsat-8 Operational Land Imager (OLI) imagery based on the Normalised Difference Water Index (NDWI), and topography was obtained by using the ALOS World 3D 30 m DEM. Global Land Use and Land Cover (LULC) projections (2020–2100) and population projections (2020–2100) were used as future inundation values. Two scenarios were compared, one based on an altimeter-based projection of sea level rise (SLR) and the other based on the National Aeronautics and Space Administration (NASA) high-emission scenario, Representative Concentration Pathway 8.5 (RCP8.5). It is found that, by the IPCC AR6 end-of-century projection horizon (relative to 1995–2014), 154,000 people under the altimeter case and 181,000 people under RCP8.5 will have a risk of being inundated. The highest flooded area is the barren area (25,523–46,489 hectares), then the urban land (5303–5743 hectares), and finally the cropland land (434–561 hectares). Critical infrastructure includes 275–406 km of road, 71–99 km of electricity lines, and 73–82 km of pipelines. The study provides the first hydrologically verified Digital Elevation Model (DEM)-refined inundation maps of Iraq that offer a baseline, in the form of a comprehensive and quantitative base, to the coastal adaptation and climate resilience planning. Full article

This study develops an immersive marine Information and Communication Technology (ICT) convergence framework designed to enhance coastal climate resilience by improving accessibility, visualization, and communication of scientific research on Dokdo (Dok Island) in the East Sea. High-resolution spatial datasets, multi-source marine observations, underwater imagery, and validated research outputs were integrated into an interactive virtual-reality (VR) and web-based three-dimensional (3D) platform that translates complex geophysical and ecological information into intuitive experiential formats. A geospatially accurate 3D virtual model of Dokdo was constructed from maritime and underwater spatial data and coupled with immersive VR scenarios depicting sea-level variability, coastal morphology, wave exposure, and ecological characteristics. To evaluate practical usability and pro environmental public engagement, a three-phase field survey (n = 174) and a System Usability Scale (SUS) assessment (n = 42) were conducted. The results indicate high satisfaction (88.5%), strong willingness to re-engage (97.1%), and excellent usability (mean SUS score = 80.18), demonstrating the effectiveness of immersive content for environmental education and science communication crucial for achieving Sustainable Development Goal 14 targets. The proposed platform supports stakeholder engagement, affective learning, early climate risk perception, conservation planning, and multidisciplinary science–policy dialogue. In addition, it establishes a foundation for a digital twin system capable of integrating real-time ecological sensor data for environmental monitoring and scenario-based simulation. Overall, this integrated ICT-driven framework provides a transferable model for visualizing marine research outputs, enhancing public understanding of coastal change, and supporting sustainable and adaptive decision-making in small island and coastal regions. Full article

Many tropical islands and coastal communities suffer from high energy costs, unreliable electrical supplies, poverty, and underemployment, which are all exacerbated by climate change. Multi-use Ocean Thermal Energy Conversion (OTEC) systems could align with the goals and values of these underserved and remote communities. Developing multi-use OTEC systems could help meet the United Nations’ Sustainable Development Goals #7 (Affordable and Clean Energy) and #13 (Climate Action). Multiple uses of OTEC water and power are explored in this study, including seawater air conditioning, desalination, support for aquaculture in tropical regions, and other uses. A use case for an onshore OTEC plant at the location of the existing OTEC plant in Kona, Hawaii, is examined to determine if sufficient thermal resources exist for OTEC power generation year-round, and to determine the potential for each value-added use. Potential environmental effects are evaluated using a new open-source numerical model for determining the risk from the discharge of large volumes of cold deep seawater in the ocean. Companies currently using the cold deep seawater pumped ashore at the Kona location were surveyed to determine their dependence on and interest in expanded OTEC and cold-water availability at the site. The analysis indicates that multi-use OTEC is feasible, with seawater air conditioning (SWAC), aquaculture, and desalination being the most compatible immediate additions, while future potential exists for adding extraction of critical minerals from seawater and e-fuel generation. Full article

Tsunamis and submarine earthquakes pose severe risks to coastal regions, demanding rapid and reliable monitoring systems. While the Deep-ocean Assessment and Reporting of Tsunamis (DART) system has been globally deployed, its dependence on pressure sensors and one-to-one communication limits its applicability to the Korean East Sea. This paper introduces the Korean Tsunami and Earthquake Monitoring System, which integrates seafloor seismometers and proposes a dedicated Medium Access Control (MAC) protocol optimized for multi-node underwater acoustic communication. The study performs a comprehensive analytical derivation of closed-form expressions for channel utilization and energy consumption under diverse node configurations and acoustic conditions. The analytical results confirm that the proposed MAC protocol maintains stable performance, supports multi-node operation, and enables long-term monitoring within the limited energy budget of underwater devices. The derived results also provide practical design implications for underwater network planning, including guidelines on node placement, frame duration, and control packet timing for efficient data delivery. Although empirical validation remains as future work, the findings establish theoretical benchmarks and engineering insights for the design of next-generation underwater monitoring systems tailored to Korean coastal environments. Full article

To quantitatively assess the storm surge induced by Super Typhoon Doksuri (2023) along the complex coastline of Fujian Province, a high-resolution Finite-Volume Coastal Ocean Model (FVCOM) was developed, driven by a refined Holland–ERA5 hybrid wind field with integrated physical corrections. The hybrid approach retains the spatiotemporal coherence of the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis in the far field, while incorporating explicit inner-core adjustments for quadrant asymmetry, sea-surface-temperature dependency, and bounded decay after landfall. A series of numerical experiments were conducted, including paired tidal-only and full storm-forcing simulations, along with a systematic sensitivity ensemble in which bottom-friction parameters were perturbed and the anomalous (typhoon-related) wind component was scaled by factors ranging from 0.8 to 1.2. Static sea-level rise (SLR) scenarios (+0.3 m, +0.5 m, +1.0 m) were imposed to evaluate their influence on extreme water levels. Storm surge extremes were analyzed using a multi-scale coastal buffer framework, comparing two extreme extraction methods: element-mean followed by time-maximum, and node-maximum then assigned to elements. The model demonstrates high skill in reproducing astronomical tides (Pearson r = 0.979–0.993) and hourly water level series (Pearson r > 0.98) at key validation stations. Results indicate strong spatial heterogeneity in the sensitivity of surge levels to both bottom friction and wind intensity. While total peak water levels rise nearly linearly with SLR, the storm surge component itself exhibits a nonlinear response. The choice of extreme-extraction method significantly influences design values, with the node-based approach yielding peak values 0.8% to 4.5% higher than the cell-averaged method. These findings highlight the importance of using physically motivated adjustments to wind fields, extreme-value analysis across multiple coastal buffer scales, and uncertainty quantification in future SLR-informed coastal risk assessments. By integrating analytical, physics-based inner-core corrections with sensitivity experiments and multi-scale analysis, this study provides an enhanced framework for storm surge modeling suited to engineering and coastal management applications. Full article

This study focuses on a rapidly urbanizing coastal plain where river networks serve as critical pathways for pollutant transport to nearshore waters. Under frequent sluice control and sluggish hydrodynamics, pollutants accumulate in channels and are subsequently flushed during intense rainfall or sluice-opening events, increasing pollutant loads in downstream estuaries. Based on 2017–2024 water quality monitoring data, integrated multi-source environmental factor analysis and unmanned patrol boat technology, systematic water quality assessment and pollution source identification were conducted. Significant spatial heterogeneity was observed: phosphorus and nitrogen pollution dominated in the eastern region, whereas the permanganate index was more prominent in the western part of the network. Identification of abrupt water quality change sections revealed industrial wastewater as the primary contributor to phosphorus and nitrogen, whereas permanganate index pollution originated widely from aquaculture, agriculture, and industrial discharges. Atmospheric deposition likely provides a non-negligible contribution to phosphorus and nitrogen input, with fluxes strongly correlated to rainfall. Sediment release posed internal risks of carbon and phosphorus, with intensity positively linked to pollution levels. This study elucidates the water quality characteristics and multi-source pollution mechanisms in typical coastal river networks under rapid economic development. Therefore, it provides a scientific basis for precise regional water environment management and coastal water quality protection. Full article

The coastal environment is a dynamic system shaped by both natural processes and human activities. In recent decades, increasing anthropogenic pressure and climate change—manifested through sea-level rise and more frequent extreme events—have accelerated coastal retreat, highlighting the need for improved management strategies and standardized tools for coastal risk assessment. Existing approaches remain highly heterogeneous, differing in structure, input data, and the range of factors considered. To address this gap, this study proposes an index-based methodology of general validity designed to quantify coastal erosion risk through the combined analysis of hazard, vulnerability, and exposure factors. The approach was developed for multi-scale and multi-risk applications and implemented across 54 representative sites along the Calabrian coast in southern Italy, demonstrating strong adaptability and robustness for regional-scale assessments. Results reveal marked spatial variability in coastal risk, with the Tyrrhenian sector exhibiting the highest values due to the combined effects of energetic wave conditions and intense anthropogenic pressure. The proposed framework can be easily integrated into open-access GIS platforms to support evidence-based planning and decision-making, offering practical value for public administrations and stakeholders, and providing a flexible, accessible tool for integrated coastal risk management. Full article

As an important marginal sea in the western Pacific, sea-level changes in the South China Sea not only respond to global warming but are also regulated by regional ocean dynamics and climate modes, exerting profound impacts on the socioeconomic development and engineering safety of coastal regions. To address the widespread issues of low accuracy and robustness in existing sea-level prediction models when handling nonlinear, multi-scale sequences, as well as the complexity of sea-level change mechanisms in the South China Sea, this study constructs a hybrid model combining Singular Spectrum Analysis and Long Short-Term Memory neural networks (SSA-LSTM). The coral skeletal oxygen isotope ratio (δ¹⁸O) used in this study is a key indicator for characterizing the marine environment, defined as the per mille difference in the ¹⁸O/¹⁶O ratio of a sample relative to a standard. Based on coral δ¹⁸O data from the South China Sea, the sea level from 1850 to 2015 is reconstructed. SSA is then applied to decompose the sea-level data into trend and periodic components. The trend component, accounting for 37.03%, and components 2 to 11, containing major periodic information, are extracted to reconstruct the sea-level series. The reconstructed series retains 95.89% of the original information. The trend component is modeled through curve fitting, while the periodic components are modeled using an LSTM neural network. Optimal hyperparameters for the LSTM are determined through parameter sensitivity analysis. An integrated SSA-LSTM model is constructed to predict sea level in the South China Sea, and its predictions are compared with those from a Singular Spectrum Analysis-Autoregressive Integrated Moving Average (SSA-ARIMA) model. The results indicate that from 1850 to 2015, sea level in the South China Sea exhibits periodic fluctuations with a significant overall upward trend. Specifically, the growth rate from 1921 to 1940 reaches 5.49 mm/yr. Predictions from the SSA-LSTM model are significantly higher than those from the SSA-ARIMA model. The SSA-LSTM model projects that from 2016 to 2035, sea level in the South China Sea will continue to rise at a fluctuating rate of 0.75 mm/yr, with a cumulative rise of approximately 15 mm. This study provides a novel methodology for investigating the mechanisms of sea-level change in the South China Sea and offers a scientific basis for coastal risk management. Full article

This study presents a systematic literature review of 307 peer-reviewed articles on collision avoidance approaches regarding the integration of maritime autonomous surface ships (MASSs) in coastal waters supply chains. The bibliographic data were retrieved from the ISI Web of Science Database and analyzed using Bibliometrix (version 4.3.3) in R and VOSviewer (version 1.6.20) to map the intellectual, thematic, and network structure of the research area. Three main research clusters were revealed through bibliographic coupling analysis: (1) autonomous collision risk management; (2) methodological approaches to maritime autonomy; and (3) adaptive maritime safety modeling. Content analysis of the identified research clusters enabled the development of a 68-item hierarchical task analysis (HTA) framework for MASS collision avoidance across three operational scenarios: (1) ship-to-object, (2) ship-to-ship, and (3) multi-ship. The results provide a comprehensive overview of the current state of research, identify methodological and safety interdependencies in autonomous navigation, and offer an organized and structured perspective to support the safer and more efficient integration of MASSs into coastal waters supply chains. Full article

Land subsidence in Maceió, Brazil, has triggered a significant urban crisis, resulting in widespread evacuations, population displacement, and, in some cases, the partial or complete destruction of neighborhoods. However, the full extent and underlying mechanisms beyond the mining epicenter have remained unclear. This study presents a comprehensive, city-wide subsidence assessment (2016–2024) that tests a multi-mechanistic hypothesis. SBAS-InSAR (Sentinel-1) ground-motion data are integrated with geological and geomorphological context, well-density mapping, and physical–environmental and morphological metrics to delineate and characterize subsiding zones. The results reveal several patterns of deformation: in addition to the central bowl associated with rock salt mining, a peripheral, elongated corridor extends along the Mundaú Lagoon shoreline, diffuse low-gradient zones occur within the coastal urban belt, and a peri-urban subsidence corridor is identified. The identifyed subsidence areas cover approximately 55 km² (10.8% of the city), with about 5 km² exhibiting rates exceeding 10 mm yr⁻¹. These patterns correspond to sedimentary plains and areas of intensive well use, extending far beyond the salt mining crisis zone. The primary contribution of this work is the identification of multiple subsidence mechanisms through an integrated analytical workflow, demonstrating that subsidence in Maceió constitutes a compound hazard that progressively increases city-wide risks of flooding, coastal and lagoonal erosion and slope instabilities, with direct consequences for structural integrity. The findings underscore the urgent need for risk-management strategies that address mining legacies, uncontrolled groundwater abstraction, and proper urban planning to prevent future crises. Full article

Short-term bus load forecasting in distribution networks faces severe challenges of non-stationarity, high-frequency disturbances, and multi-scale coupling arising from renewable integration and emerging loads such as centralized EV charging. Conventional statistical and deep learning approaches often exhibit instability under abrupt fluctuations, whereas decomposition-based frameworks risk redundancy and information leakage. This study develops a hybrid forecasting framework that integrates variational mode decomposition (VMD), locally weighted scatterplot smoothing (LOWESS), and a multi-channel differential bidirectional long short-term memory network (Δ-BiLSTM). VMD decomposes the bus load sequence into intrinsic mode functions (IMFs), residuals are adaptively smoothed using LOWESS, and effective channels are selected through correlation-based redundancy control. The Δ-target learning strategy enhances the modeling of ramping dynamics and abrupt transitions, while Bayesian optimization and time-sequenced validation ensure reproducibility and stable training. Case studies on coastal-grid bus load data demonstrate substantial improvements in accuracy. In single-step forecasting, RMSE is reduced by 65.5% relative to ARIMA, and R² remains above 0.98 for horizons h = 1–3, with slower error growth than LSTM, RNN, and SVM. Segment-wise analysis further shows that, for $h=1$, the RMSE on the fluctuation, stable, and peak segments is reduced by 69.4%, 62.5%, and 62.4%, respectively, compared with ARIMA. The proposed Δ-BiLSTM exhibits compact error distributions and narrow interquartile ranges, confirming its robustness under peak-load and highly volatile conditions. Furthermore, the framework’s design ensures both transparency and reliable training, contributing to its robustness and practical applicability. Overall, the VMD–LOWESS–Δ-BiLSTM framework achieves superior accuracy, calibration, and robustness in complex, noisy, and non-stationary environments. Its interpretable structure and reproducible training protocol make it a reliable and practical solution for short-term bus load forecasting in modern distribution networks. Full article

Coastal flooding driven by sea-level rise and land subsidence poses severe risks to low-lying communities. This study evaluates the causes and impacts of coastal sinking in Sayung, Demak, Central Java, using multi-temporal Landsat imagery (1977, 2024), tidal gauge data, and GPS measurements. A set of spectral indices—Normalized Difference Vegetation Index (NDVI), Weighted Modified Normalized Difference Water Index (WMNDWI), Land Surface Water Index (LSWI), and Normalized Difference Built-up Index (NDBI)—were calculated and integrated as input features for a Random Forest machine learning model to detect and classify environmental changes. Results indicated an average land subsidence rate of approximately 6 cm/year ± 0.8 cm/year, validated against InSAR-based measurements, and a classification accuracy of 91% (RMSE of 0.8 cm/year). A substantial decline in vegetation indices was observed, reflecting the conversion of agricultural land into built-up areas and water bodies. Extensive flooding and shoreline retreat were documented, with high-risk zones concentrated along densely developed coastlines. These findings highlight the urgent need for integrated management strategies, including stricter groundwater regulation, continuous remote-sensing-based monitoring, and large-scale mangrove restoration, to safeguard ecological functions and enhance the socio-economic resilience of coastal communities in the face of accelerating climate change impacts. Full article

This study examines participatory approaches to manage geo-hydrological risks associated with climate change, focusing on floods, landslides, and coastal erosion. The objective is to map hazards, participatory methods and tools, communication channels, stakeholder consultations, and governance scales involved. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for systematic reviews and covering the period 2000–2024, it analyses 236 peer-reviewed articles from Europe. It also examines 49 practical case studies from three Italian Public Consultation platforms, complementing the Europe-wide academic corpus to inform transferability to Italian governance setting. Results highlight a dominant academic emphasis on flood risks and climate change adaptation, likely driven by recent disasters and global policy initiatives, whereas landslides, coastal erosion, and integrated geo-hydrological risks remain underrepresented. Surveys, semi-structured interviews, and workshops are the most common consultation approaches, with more structured tools mainly preferred in multi-hazard settings to ensure comparability. Dissemination relied largely on face-to-face and online channels, while innovative approaches such as creative workshops and citizen-science initiatives are emerging. Stakeholder involvement typically included citizens, local authorities, experts, and voluntary associations, whereas key intermediaries such as local media, insurance agencies, cultural institutions, and universities are seldom engaged. Overall, the review identifies priorities for thematic diversification, integration of multi-hazard perspectives, improved methodological reporting, and broader inclusivity to strengthen participatory climate-risk governance. Full article