Search Results (703)

Efficient operation of rainwater pumping stations is essential for mitigating urban flooding under climate change. This study focuses on the Samcheok Osipcheon watershed, located in Gangwon-do, South Korea, and proposes a deep learning-based inflow prediction framework for the Samcheok-si drainage system using SWMM-simulated datasets. A total of 900 rainfall scenarios were generated and used to train three models: ANN, CNN, and LSTM. All models reproduced inflow hydrographs with high accuracy, but the CNN model showed overfitting with oscillations in the recession limb. The LSTM model demonstrated the best performance, achieving an NSE of 0.97 and a PPE of 3.45%. Based on the predicted inflow, two pump operation strategies were evaluated. The proactive operation considering upstream surcharge conditions, combined with second-level control, reduced peak water levels from 2.585 m to 2.439 m (approximately 5.6%) compared to the conventional operation. In addition, second-level pump operation reduced excessive discharge and stabilized detention basin water levels. The results indicate that the proposed framework can support real-time pump operation, enhance the resilience and sustainability of urban drainage systems, and contribute to sustainable urban flood mitigation. Full article

With the expanding application of digital technologies in urban renewal, more effective ways of incorporating dispersed public experience and needs into the renewal process still require further exploration. To address this issue, this research innovatively proposes an AI-assisted renewal method for historic districts driven by urban memory, constructing a continuous methodological chain from the identification of public evaluations to problem translation, to scheme generation and feedback validation. This research integrates the concept of interessement devices from Actor-Network Theory (ANT) with generative AI technologies for case application and validation. Taking Hanzheng Street as a case study, this research extracts the public’s urban memory of the historic district from online comments and identifies renewal demands. These demands were further associated with urban image elements to clarify their spatial carriers and support the subsequent generation of scene-based renewal schemes. On this basis, AI-generated images are further used to present renewed scenarios, and public evaluations of the renewal effects are collected. The results show that urban memory of Hanzheng Street can be summarized into five themes, which were further translated into five obligatory passage points (OPPs), one core issue, and corresponding renewal demands for scene units. The renewal schemes generated through this method achieved a relatively high level of public recognition overall, with mean evaluation scores ranging from 4.10 to 4.27, an overall satisfaction mean of 4.19, and a Top-2 proportion of 82.8%. By incorporating public experience into the formation of renewal schemes, this research provides a people-oriented and effective pathway for participation and feedback in the renewal of historic districts, while also offering methodological reference for the renewal of similar historic districts. Full article

This article interrogates the ontological paradox of Jordan’s abandoned vernacular villages (kirbeh), which persist as “villages of two times”—simultaneously abandoned yet present for nearby communities. Existing heritage frameworks, focused on material authenticity and physical integrity, cannot fully account for places that endure through absence rather than preservation. In response, we propose a biaxial ontological framework that explains how fragmentation generates new significance over time—a process conventional fragmentation theory overlooks because it treats breakage as loss rather than as what we term “productive fragmentation.” Specifically, the biaxial framework reveals that as material fragments decay and disperse (horizontal axis), they simultaneously acquire temporal depth and existential meaning (vertical axis). This dual process, which we term “productive fragmentation,” is the paper’s core contribution. Drawing on twenty-four semi-structured interviews across five villages, the study advances this biaxial framework by fusing fragmentation theory with concepts of deep urbanity, generative decay, time rupture, and existential displacement. The key finding is that disintegration generates new significance: fragments become more, not less, meaningful as they decay. The framework distinguishes a horizontal axis (spatial dispersal as collage and palimpsest) from a vertical axis (coexistence of multiple temporalities and anchoring of identity across generations). The implication is a paradigm shift: abandoned vernacular heritage embodies a distinct form of life—lived in the enduring presence of absence. By this phrase, we mean that community members experience the abandoned village not as a dead past but as an active presence—through memories, return visits, stories, and portable fragments like soil or keys—even as its material fabric decays. Absence here is not emptiness but a different mode of being present. Full article

The Global Navigation Satellite System (GNSS) provides meter-level positioning in open environments, but its accuracy degrades severely in dense urban areas due to signal blockage and multipath effects. To address this problem, this paper proposes a hierarchical collaborative fusion positioning method based on GNSS, 5G, and the Inertial Navigation System (INS) with cross-source observation quality assessment. The proposed method integrates dual-domain error suppression, adaptive-shrinkage Unscented Kalman Filter (UKF) estimation, and observation-quality-aware adaptive weighting to mitigate systematic bias, random gross errors, and observation degradation. Unlike conventional fixed-weight or single-source-quality fusion schemes, the proposed method jointly combines gross-error detection, residual-driven covariance shrinkage, and adaptive weight regulation in a unified framework. Experiments were conducted in open outdoor, semi-occluded outdoor, and fully occluded indoor scenarios. The proposed method achieved a horizontal RMSE of 1.61 m in the semi-occluded outdoor environment. Compared with the the long short-term memory (LSTM)-aided UKF baseline, the positioning RMSE was reduced by 32.4%, and the positioning interruption rate was reduced by 49.5%. Full article

This paper examines the impact of the 1923 Turkish–Greek Population Exchange on the urban and architectural heritage of Tirilye, a historic coastal settlement in Bursa, Türkiye. The study addresses how migration-related transformations shaped both the tangible and intangible dimensions of heritage, focusing particularly on traditional houses and their adaptive reuse strategies. The research aims to identify patterns of continuity and transformation in residential architecture and to interpret population exchange heritage as a multi-layered cultural process. The study adopts a qualitative multi-method approach combining literature review, archival research, field surveys, architectural and typological analyses, and oral history interviews. The monuments and twenty-eight traditional houses were comparatively analysed at urban and building scales in terms of plan organisation, façade typology, construction techniques, and functional transformation. The findings demonstrate that Tirilye largely preserved its historic urban fabric despite demographic rupture. Traditional houses retained many original spatial and architectural characteristics while adapting to new social and economic conditions. The study reveals a hybrid architectural nature combining the effects of various living traditions and highlights the continuity of production-related spaces associated with olive cultivation and sericulture. The paper proposes understanding population exchange heritage as a dynamic process shaped by continuity, adaptation, reuse, and collective memory. Full article

This article analyzes how twentieth- and twenty-first-century Afro-Colombian women poets from the Pacific region challenge and reframe the feminization of the land and the naturalization of the Black female body within colonial and Eurocentric epistemologies. Drawing on a framework that conceptualizes body, territory, spirituality, and community as an interdependent continuum, the article conducts close textual analysis to demonstrate how these poets construct territory and the Black female body as sentient sites. These sites are simultaneously shaped by historical violence, forced displacement, extractive economies, and racialized gender constructs, while preserving ancestral knowledge and collective memory. The findings show that Valencia Córdoba develops the body–territory through metaphor and anaphora as a generative space; Grueso Romero deploys orality and the sea as transatlantic archives of ancestry and identity; and Truque articulates urban displacement as an ontological rupture that affects memory and Black subjectivity. Ultimately, the article advances the concept of body–territory as a decolonial aesthetic and analytical tool through which Afro-Colombian women’s poetry articulates environmental justice, gendered racialization, and forms of resistance within the Afrodiasporic diaspora. Full article

Climate change and urbanization have increased the precipitation variability and extreme hydrological events, highlighting the need for accurate river water level prediction. This study proposes a two-step sequential prediction framework based on a Long Short-Term Memory (LSTM) model and evaluates the impact of hydrological connectivity among observation stations on predictive performance. In Step 1, water levels at upstream and downstream stations are predicted. In Step 2, these predictions are incorporated as additional inputs for forecasting water levels at a target station. Input variables are selected using information gain (IG), and multicollinearity is assessed with the variance inflation factor (VIF). Results show that at Pojin Bridge, where short-term fluctuations are significant, incorporating predicted upstream and downstream water levels improves the coefficient of determination (R²) by approximately 3.9% to 9.24% as lead time increases. In contrast, at Andong Bridge, where hydrological responses are relatively stable, the additional inputs reduce model performance. These findings indicate that the effectiveness of incorporating hydrological connectivity depends on station-specific characteristics. The study provides practical guidance for designing data-driven river forecasting models under varying hydrological conditions. Full article

Large-scale WebGIS visualization of 3D building models is often constrained by large requested payloads, client-side memory pressure, and runtime state-parsing overhead. This study proposes a five-dimensional orthogonal decoupling framework and a 16-bit state-word-driven scheduling method for 3D building models. The Boundary-based Spatial Proxy–Geometric Detail–Component Complexity–Texture Appearance–Semantic Information (B-D-C-T-S) framework organizes model representations into five separately addressable and schedulable dimensions, covering spatial proxies, geometry, components, textures, and semantics. A compact 16-bit structured state word is used to represent runtime states and reduce dependence on repeated text-based state parsing, supporting fixed-offset bitwise decoding, exclusive-OR (XOR)-based differencing, constraint checking, and incremental updating. A centroid-assigned Home Tile strategy is further introduced to reduce redundant semantic payloads for cross-tile objects. The method was evaluated using a single-building BIM model and an urban-scale photogrammetric mesh dataset. Under the tested initial-view setting, staged decoupled loading reduced the first-screen requested payload by 93.1% compared with monolithic loading. State-word-based C-field extraction achieved an approximately 144-fold speedup over JSON deserialization and C-field lookup. The Home Tile strategy reduced the total semantic payload by 44.1% in the semantic-redundancy test. In the 1.12 GB first-screen memory test, state-word-driven D1 tile scheduling loaded only 22.7 MB of physical payload, with stable resident memory of approximately 88.1 MB. These results indicate that the proposed method supports object-level state representation, selective resource activation and scheduling, Home Tile semantic routing, incremental updating, and first-screen memory control within tiled Web3D pipelines. Full article

The Eastern Mediterranean, notably Cyprus, is a climate change hotspot facing severe heatwaves. Accurate numerical weather prediction of these extremes requires precise land–atmosphere modeling and initial and boundary conditions. This study assesses replacing the default USGS Land-Use and Land-Cover (LULC) dataset with the 10 m ESA WorldCover 2021 dataset in the Weather Research and Forecasting (WRF) model to simulate the 15–29 July 2023 Cyprus heatwave. The updated LULC increased urban representation six-fold. Statistical validations showed significant improvements in 2 m temperature, relative humidity, and 10 m wind speed predictions across 85% of observational sites. Dynamically, it restored urban thermal memory, effectively capturing the daytime Urban Cool Island effect and nocturnal heat release. Furthermore, radiosonde validations showed that the update corrected nocturnal Planetary Boundary Layer Height (PBLH) underestimations and dampened exaggerated daytime convective mixing. However, crucial limitations remain. High-frequency diagnostics indicated the model still suffers from damped thermal inertia, missing the abrupt temperature spikes and rapid nocturnal cooling typical of semi-arid microclimates. Additionally, the updated configuration failed to capture severe atmospheric stagnation during peak heatwave conditions, highlighting that deep-rooted kinetic errors persist within default boundary layer parameterizations despite static surface improvements. Full article

Environmental sound classification is an important component of smart city sensing systems, supporting applications such as urban noise analysis, public safety monitoring, and real-time situational awareness. However, high-accuracy models are often difficult to deploy on low-power edge devices because of memory, computational, and latency constraints. This study aims to address this deployment gap by developing a lightweight compression pipeline for a hybrid convolutional and Kolmogorov–Arnold Network-based model. The proposed pipeline consists of three stages. First, structured channel pruning is applied to remove redundant convolutional filters while preserving hardware-efficient dense operations. Second, selective quantization-aware training is applied to the most computation-dominant layers, namely the third convolutional layer and the fully connected layer. Third, knowledge distillation is used to recover accuracy by training the compressed model under the guidance of the baseline model. Experiments were conducted on ESC-10, ESC-50, FSC22, and UrbanSound8K. The proposed pipeline reduced the average parameter count from 511,033 to 50,774 and reduced the model size while maintaining competitive accuracy across all benchmarks. The final model preserved the baseline accuracy of 96.75% on ESC-10, while accuracy decreased only from 88.25% to 86.50% on ESC-50, from 87.92% to 86.38% on FSC22, and from 85.13% to 84.52% on UrbanSound8K. These results show that the proposed compression pipeline provides an effective accuracy–efficiency trade-off for real-time audio classification on resource-constrained devices. Therefore, the resulting compressed model supports the scalable deployment of distributed acoustic sensing systems for real-time smart city monitoring and decision-making. Full article

Traffic accidents pose a persistent challenge for urban mobility, public safety, and sustainable development in smart cities, particularly in rapidly growing urban environments. This study presents a data-driven spatiotemporal analysis of traffic accidents in Ecuador, aimed at supporting evidence-based urban traffic management and road safety planning. Using large-scale historical accident records, the proposed approach combines spatial clustering and temporal forecasting techniques to characterize accident concentration patterns and temporal dynamics at national and metropolitan scales. Spatial accident hotspots are identified using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), enabling the detection of high-risk zones without imposing assumptions on cluster shape or size. This analysis reveals strong spatial concentration of accidents, with a limited number of clusters accounting for a substantial proportion of fatalities and injuries. Complementary temporal analysis is conducted using a multi-model ensemble framework to examine accident trends and seasonal patterns. This approach integrates SARIMA for linear stochastic modeling and Prophet for additive trend analysis, alongside two Long Short-Term Memory (LSTM) architectures: a direct 12-month vector output and a recursive horizon-3 model. By synthesizing these statistical and neural network-based methods through inverse-RMSE weighting, the study captures both stable seasonal cycles and non-linear, short-to-medium-term variations in accident frequency. Results show that traffic accidents in Ecuador exhibit stable diurnal and seasonal structures, alongside pronounced spatial heterogeneity across urban regions. The combined spatial and temporal insights provide a coherent representation of accident risk patterns, facilitating the prioritization of critical zones and high-risk periods. The resulting hotspot maps and multi-model forecasting horizons offer actionable information for smart city stakeholders, supporting targeted infrastructure interventions, adaptive enforcement strategies, and data-informed urban mobility policies. This work contributes to the broader understanding of traffic safety analytics as a core component of smart city decision-support systems. Full article

Based on the UN SDGs global agenda and China’s national urban renewal strategy, this study highlights the key role of historic architectural decorative patterns in supporting cultural continuity in urban renewal and facilitating heritage building-informed regeneration. Focusing on the sustainable development of urban renewal and heritage building-informed design and regeneration of historic buildings, this study explores the quantification of the cultural memory value of decorative patterns. It integrates a quantitative indicator system into the Gaussian Strategy Enhanced Whale Optimization Algorithm-Back Propagation Neural Network (GSWOA-BP) to enable intelligent pattern generation. First, cultural genes are extracted from architectural heritage, followed by digital quantification and analysis, to generate context-appropriate pattern designs. These are then applied to urban renewal scenarios, ultimately promoting the transmission and revitalization of architectural heritage through digital means. This study provides theoretical support and a technical pathway for the intelligent design of historic architectural decorative patterns, facilitates cultural continuity in heritage building-informed design for urban renewal, and presents a heritage building-informed generative design framework. Full article

Air quality affects large urban areas, where rapid urban development and human activities place constant pressure on ecosystems and public health. In this context, large-scale air quality assessment, supported by short-term forecasts, can provide useful information for environmental management and decision-making in urban areas, thus supporting evidence-based urban environmental management. The aim of this work is to design an affordable, smart real-time air pollution monitoring and prediction system for urban planning in overpopulated locations, which is deeply related to community health. The system focuses on real-time monitoring and forecasting of air quality. Prediction tasks were limited to gaseous pollutants CO and CO₂. Measurements were obtained over four months from a low-cost sensor platform installed in a highly populated neighborhood district in Baghdad, Iraq. Air quality prediction of gas concentrations was done using three types of time-series algorithms: Long Short-Term Memory, or LSTM; Gated Recurrent Unit, or GRU; and Temporal Convolutional Network, or TCN, models. Among these, the LSTM architecture showed more stable behavior and a higher predictive ${\mathrm{R}}^2$, ranging from 98.2% to 98.9%. Generally, the findings suggest that combining low-cost sensing technologies with artificial intelligence can offer a feasible and scalable solution for urban air quality monitoring. This approach may support cost-effective strategies for monitoring air quality in resource-constrained urban environments. Full article

This paper proposes DB-LIO (database-driven LiDAR-inertial odometry), a simultaneous localization and mapping (SLAM) system that addresses memory scalability challenges in extended autonomous operation. Existing LiDAR-SLAM systems accumulate keyframe history in memory, leading to $O\left(N\right)$ growth and out-of-memory failures during extended operation. To overcome this limitation, DB-LIO introduces three core design elements. First, it proposes a spatially indexed keyframe management scheme that persistently stores keyframes in SQLite with R-Tree spatial indexing, enabling $O(\log N+k)$ spatial queries that tightly couple cache eviction with factor-graph optimization requirements—a design that ensures every keyframe potentially involved in the next optimization cycle resides in cache. Second, it presents a four-level memory bounding architecture—SLAM-engine keyframe trimming with transparent on-demand reloading, a DB-level least recently used (LRU) cache with a spatial active window, Scan Context descriptor pool bounding, and iSAM2 sliding window compaction with a sparse global anchor graph—that collectively bounds the dominant memory consumers to $O\left(C\right)$. Third, the DB-based persistent storage enables a localization mode that can reload previously built maps—including full point clouds, six-degree-of-freedom poses, timestamps, and inter-keyframe relationships—and perform pose estimation using the stored map, which is particularly valuable for agricultural robots and other autonomous systems requiring map reuse. Experiments on a custom orchard dataset demonstrate an 81.9% reduction in memory usage compared with that of the in-memory baseline (2888 MB → 524 MB), while preserving equivalent trajectory accuracy (absolute trajectory error (ATE) root mean square error (RMSE) 0.305 ± 0.001 m vs. 0.296 m). Validation on the KITTI odometry benchmark confirms that the proposed localization mode generalizes across different LiDAR types (Livox Mid360, Velodyne HDL-64E) and environments (orchard, urban driving). Full article

Mexico City experiences severe water stress driven by aquifer overexploitation and recurrent droughts. Effective water management requires operational spatial monitoring systems capable of spatially reconstructing meteorological anomalies across multiple temporal scales. In this work we developed an explainable deep learning framework using Long Short-Term Memory (LSTM) networks to spatially reconstruct three drought indices—the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Reconnaissance Drought Index (RDI)—across five accumulation scales (3, 6, 12, 18, and 24 months). To strictly isolate genuine meteorological deviations, we adopted a hybrid statistical approach: SPI was computed following the standard WMO methodology using Gamma distribution fitting, while SPEI and RDI were computed using empirical monthly standardized anomalies to ensure robustness in non-stationary urban climates without forcing distributional assumptions. Model generalization was evaluated using a leave-one-microsite-out validation strategy, training on two stations and testing on a spatially isolated third station, with inter-station distances ranging from 1.8 to 6.7 km, sufficient to capture urban microclimatic heterogeneity while remaining within the same regional climate zone. We quantified feature importance using SHapley Additive exPlanations (SHAP) to provide mathematical transparency. The LSTM achieved predictive performance at long-term scales by effectively capturing deep sequential memory, while short-term reconstructions reflected the inherent noise of urban convective precipitation. The framework demonstrates reliable intra-urban spatial generalization capacity, supporting the development of diagnostic tools for metropolitan water stress assessment. Full article