Search Results (260)

Due to the rapid acceleration of urbanisation and the increasing occurrence of extreme rainfall events, underpasses have become critical hotspots of urban flooding vulnerability. In this study, we investigated 36 underpasses in Hangzhou using the Urban Flood Inundation Model (UFIM) to systematically evaluate their drainage performance. A high-resolution hydraulic simulation framework was developed by integrating terrain data, drainage pipe networks, pumping stations, and land-use information. Based on the maximum tolerable hourly rainfall derived from multi-scenario simulations, the facilities were divided into high-, medium-, and low-vulnerability groups. Our quantitative and spatial analyses reveal a pronounced core–periphery disparity: 41.7% of the underpasses were highly vulnerable (drainage threshold ≈ 61.3 mm/h), exhibiting significant spatial agglomeration in the older urban core. In contrast, facilities in newly developed peripheral areas demonstrated better drainage performance (threshold up to 75.6 mm/h). Furthermore, the backwater effect from downstream rivers at flood stages significantly constrains pump efficiency by increasing the static head requirement. Based on these spatial vulnerabilities and thresholds, targeted infrastructure optimisation and spatial planning strategies are proposed, shifting the focus from uniform engineering upgrades to vulnerability-based drainage capacity enhancements. Full article

In order to improve the assessment of the dynamic risk of urban waterlogging, this study addresses the limitations of existing methods in capturing the responses of surface roads and subway systems to inundation, as well as the resulting spatiotemporal risks. Using the Hanyang District in Wuhan as a case study, the research proposes a framework for assessing urban waterlogging risks based on the dynamic inundation responses of surface and underground transport systems under various rainfall scenarios. The waterlogging process is simulated using seven representative rainfall scenarios with a hydrodynamic model that integrates a one-dimensional pipe network, a two-dimensional surface overland flow model, and a generalized underground space model. A coupled road–subway transportation network is developed to analyze traffic capacity degradation, path redistribution, and cascading failures caused by waterlogging disturbances. Quantified dynamic response indicators are integrated into the H-E-V-C framework to assess dynamic urban waterlogging risk. The results indicate that direct failure caused by water accumulation is typically the primary catalyst for extensive degradation of the transportation network, while the expansion of congestion and localized overload failures further exacerbate cascading effects. Different rainfall patterns influence not only peak risk but also the duration and spatial development of high-risk areas. Incorporating the dynamic response of the transport system enables a more accurate assessment of the degradation of emergency accessibility and the ongoing accumulation of localized high-risk areas. These findings highlight the importance of dynamic risk assessment in identifying time-varying urban vulnerabilities and supporting the planning of sustainable urban drainage, traffic management, and phased early warning systems. 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

Urban flooding poses escalating threats to high-density cities, yet the nonlinear mechanisms linking rainfall characteristics and urban morphology to waterlogging severity remain poorly understood. This study proposes a rainfall-stratified explainable machine learning framework to distinguish deep from shallow inundation at the block scale, taking Shanghai as a case study. Four models (XGBoost, random forest, SVM, and logistic regression) were compared via nested cross-validation and Bayesian optimization, with XGBoost identified as the optimal model. Three physically distinct rainfall dimensions and multi-dimensional urban morphological indicators were incorporated as predictive features. SHAP-based attribution and PDP were employed to unveil the driving mechanisms behind inundation severity, characterizing scenario-dependent shifts in driver dominance and nonlinear threshold effects. Urban morphology primarily governs spatial risk under non-extreme rainfall, with building shape coefficient (BSC) remaining the primary driver overall. Meteorologically, waterlogging severity surges beyond critical thresholds for maximum hourly rainfall (>18.40 mm/h) and total volume (>139 mm), while duration exhibits an inverted U-shaped response. Morphologically, a high BSC (>0.39 m⁻¹) is consistently associated with elevated deep inundation probability, whereas higher SDBV (>54,155 m³) and greater DR (>582 m) are associated with a severity-attenuating effect. These findings provide threshold-driven insights for integrating morphological resilience into urban renewal and sustainable flood adaptation strategies in high-density metropolises. Full article

The rapid growth of the low-altitude economy and urban air mobility (UAM) is reshaping urban transport and infrastructure systems. However, current planning practices still tend to treat green spaces, stormwater facilities, and drone infrastructure as separate subsystems. This paper proposes a Garden Hydrology UAV collaborative infrastructure framework for resilient urban low-altitude logistics and inspection. Pocket parks and sponge city facilities (rain gardens, detention basins) are redesigned as multi-functional UAV bases that integrate take-off/landing and charging with stormwater retention and recreation. A SWMM-based hydrological model provides time-varying inundation and storage states, which are mapped into dynamic node availability constraints for UAV operations, using EPA SWMM 5.2. A multi-objective optimization model is formulated to minimize logistics operation cost, hydrological risk exposure and noise impact on sensitive receptors, while respecting airspace and battery constraints. A stylized 4 km² high-density district is used to evaluate three scenarios: depot-only operations, garden–UAV integration without hydrological coupling, and the full collaborative framework with SWMM-based node availability and high-precision navigation. Simulation results show that the integrated design reduces makespan by up to 19.7%, energy use by 22.3%, and hydrological risk exposure by 63.4%, while lowering noise exposure by 21.3%, relative to the baseline. The study suggests that garden and sponge city infrastructures can become key physical supports of smart low-altitude networks under the low-altitude economy. Full article

River flooding and urban inundation require forecasting systems that can anticipate future risk, rather than systems that only estimate the current water state. However, real-world closed-circuit television (CCTV)-based flood datasets often contain imbalanced or temporally inconsistent risk labels. In addition, most image-based approaches remain limited to static scene understanding. This study proposes a dataset reformulation and temporal multi-task forecasting framework for CCTV-based flood-risk prediction. First, we introduce a site-relative relabeling strategy that converts noisy frame-level danger annotations into four risk levels using visual flood indicators and lightweight environmental cues. Second, we transform the original frame-based dataset into site-hour sequences for multi-horizon forecasting at 1 h, 3 h, and 6 h. Third, we evaluate image-only, weather-only, and naive multimodal configurations to examine the role and limitations of heterogeneous sensor fusion. On the reformulated dataset, the image-only temporal model achieved the best overall performance, with a mean Intersection over Union (mIoU) of 0.892, Dice score of 0.940, macro-averaged F1 score (Macro-F1) of 0.532, and high-risk recall of 0.642. In contrast, naive multimodal fusion reduced Macro-F1 to 0.267 and high-risk recall to 0.070. This result indicates that additional weather inputs do not automatically improve prediction when cross-modal signals are noisy, weakly correlated, or temporally misaligned. The ablation results further showed that removing temporal modeling decreased Macro-F1 to 0.227 and high-risk recall to 0.000. These findings demonstrate that dataset reformulation and temporal modeling are essential for extending CCTV-based flood analysis from static estimation to future risk forecasting. They also suggest that robust cross-modal alignment is required before multimodal sensing can provide reliable performance gains. Full article

Urban flooding in coastal megacities remains a critical challenge, with recurrent inundation driven by extreme rainfall, inadequate drainage, and topographic vulnerability. This study investigated the spatio-temporal dynamics of flooding in Mumbai between 2018 and 2025 using Sentinel-1 SAR data (VV and VH polarizations) along with automated thresholding and unsupervised classification techniques. The VV polarization consistently detected a larger flood extent than VH, with maximum inundation reaching 152 km² in 2024, compared to 67 km² with VH, highlighting VV’s superior sensitivity to surface water. Ward-wise analysis revealed that Chembur West (16.47 km²), Matunga (12.33 km²), and Ghatkopar (5.43 km²) were the most flood-prone areas, while Colaba and Marine Lines experienced lower exposure due to higher elevation and better drainage infrastructure. Annual flood variation corresponded with intense rainfall events, particularly those exceeding 300 mm/day in 2020, 2023, and 2024. Validation with Brihanmumbai Municipal Corporation (BMC) reported flood data confirmed a strong spatial agreement with SAR-derived flood zones, supporting the reliability of the geospatial model. The integration of remote sensing, rainfall data, and ward-level analysis offers a scalable framework for urban flood risk mapping. These findings emphasize the need for resilient drainage planning, green infrastructure, and real-time flood monitoring systems. Full article

Global climate change and extreme precipitation events increasingly challenge urban infrastructure resilience, particularly in topographically vulnerable regions like Taiwan. Traditional flood monitoring relies heavily on the manual visual interpretation of extensive surveillance networks, a process that imposes high cognitive loads and risks delayed emergency responses. This study presents a comprehensive Cyber-Physical System (CPS) architecture for an automated Water Image Monitoring Platform. Integrating approximately 10,000 cameras and multi-modal data—including precipitation records and spatial alerts—the platform leverages advanced semantic segmentation (DeepLabV3+ with Xception71) to delineate inundation boundaries. To ensure robustness under adverse conditions such as low illumination, fog, and specular glare, we implemented targeted optimizations, including HSV pre-processing, Deblur GAN architectures, and attention mechanisms. Results demonstrate a significant performance evolution, with the event recall rate rising from 88% in 2022 to 99.7% by 2025. A key driver of this success is the synergy between stationary nodes and vehicle-mounted CCTV units, which provide critical dynamic geographic coverage. Furthermore, the deployment of edge computing reduced warning latency 10 times—from 19.2 to 2 s—while virtual water level gauges maintained a mean error within ±10 cm. Despite these gains, a Human-in-the-Loop (HITL) architecture remains strategically necessary for ethical accountability and error filtering. This CPS provides a foundational model for autonomous, resilient urban disaster management. Full article

Robust tsunami casualty estimation is vital for Peru’s central coast. While static maps ignore evacuation dynamics, precise agent-based models (ABMs) are often too computationally demanding for rapid screening. To bridge this gap, we propose an efficient geospatial workflow coupling TUNAMI-N2 simulations with shortest-path routing. Evaluating four subduction scenarios across Chorrillos and Villa El Salvador, the model tracks census-block evacuation progress. By intersecting evacuation trajectories with tsunami arrival times, casualties are calculated using empirical depth-dependent fragility functions. Results highlight that delayed reaction times significantly increase mortality. Furthermore, a counterintuitive dynamic emerges in spatially constrained corridors lacking vertical evacuation: higher walking speeds can paradoxically increase fatalities by advancing evacuees into deeper inundation zones before being overtaken. This highlights that behavioral preparedness must be coupled with structural urban interventions. Ultimately, our scalable approach enables DRR (Disaster Risk Reduction) managers to rapidly map mortality hotspots and prioritize critical infrastructure improvements in highly exposed coastal zones. Full article

Existing flood risk models have improved predictive accuracy, but they prioritize natural and hydrological factors while giving limited attention to fine-grained urban morphology. This study develops an interpretable deep learning framework to examine how high-resolution, three-dimensional urban form shapes two dimensions of flood risk: inundation risk, measured by grid-level inundated area, and infrastructure risk, measured by flood-related disruptions, including water supply interruption, power outage, road blockage, and collapse-related damage. Using Zhengzhou, China, as a case study, we combine multi-source spatial data, convolutional neural networks, ablation analysis, SHAP interpretation, and Gaussian Mixture Model classification to examine how fine-grained urban morphology affects these two risk dimensions. Incorporating urban morphology improved inundation risk prediction, reducing MSE from 0.0431 to 0.0371. The improvement was greater for infrastructure risk, with accuracy increasing from 0.7327 to 0.8218, and ROC-AUC from 0.83 to 0.95. SHAP results show that inundation risk is associated with vegetation, elevation, hydrological proximity, and localized spatial disorder, whereas infrastructure risk is amplified by vertical intensity, imperviousness, building concentration, porosity, and shape. Spatially, very high infrastructure-risk areas accounted for only 2.30% of the city but 12.88% of the central districts, while 74.62% of very high infrastructure-risk zones were concentrated in dense mid- to high-rise morphology. These findings suggest that flood-resilient planning should move beyond hydrology-sensitive flood management toward morphology-sensitive planning. Full article

Coastal cities are prone to flooding due to extreme rainfall, rising sea levels, and urbanization. This study develops a non-stationary flood risk prediction framework for a coastal city to assess the combined effects of rainfall, tide, and land surface change on future flood inundation and socioeconomic risk. Future rainfall was predicted by integrating the time-varying parameter distribution (TVPD) model with CMIP6 data through a genetic algorithm; future tides were estimated using the TVPD model; and land use in 2035 was simulated using the Markov–PLUS model. Flood inundation and the associated socioeconomic risks were then evaluated. The results showed that the integrated rainfall prediction approach reduced RMSE by 13.4% compared with the individual models. The land use simulation also showed acceptable performance, with a Kappa coefficient of 0.79 and an FOM value of 0.15. Under the combined effects of rainfall, tide, and land use change, the future peak inundation volume increased by 19.97% on average relative to the baseline period, while the affected population and economic losses increased by 72,603 people and US$12.61 billion, respectively. These results indicate that flood risk in coastal cities may be substantially exacerbated under a non-stationary environment, and the proposed framework can provide support for future flood risk assessment and adaptation planning. Full article

Urban flood modelling in heavily engineered catchments requires model structures that capture not only surface runoff processes but also hydraulic infrastructure and operational controls. This study applies a TELEMAC-2D rain-on-grid framework to two urban sub-catchments of the Emscher River (North Rhine-Westphalia, Germany) to quantify the relative effects of surface calibration, explicit infrastructure representation, and operational rules on the simulated flood response. A stepwise model development workflow was implemented, including land use-based calibration of Manning’s n and SCS Curve Numbers, explicit integration of culverts and bridges, and rule-based representation of retention basins and pumping stations. Model performance was evaluated using hydrograph shape, peak discharge, peak timing, event volume, and inundation behaviour across different antecedent moisture conditions (AMC). The results show that surface calibration alone was insufficient to consistently reproduce observed hydrographs. In the Rossbach sub-catchment area, integrating retention basins, pumping stations, and operational rules improved model performance from NSE = −0.129 under AMC I to NSE = 0.773 under AMC III. RMSE decreased from 3.380 to 1.515 m³ s⁻¹, peak discharge error from −6.198 to −0.492 m³ s⁻¹, and volume bias from −0.664 to +0.038. A targeted, routing-focused calibration further improved timing behaviour but increased volume bias, indicating residual deficiencies in the representation of rapid urban conveyance pathways. The findings show that reliable urban flood simulation in infrastructure-rich catchments depends not only on calibrating surface parameters but also on explicitly representing hydraulic structures, operational controls, and antecedent wetness conditions. Full article

Driven by sea-level rise and frequent compound coastal flooding, accurate inundation simulation is essential for disaster mitigation and urban planning. To address the topologically disconnected overestimation errors inherent in the traditional bathtub model, this study proposes a dynamic coastal inundation simulation framework based on an 8-neighbor seed-spread algorithm. Within this framework, a digital elevation model (DEM) is resampled to a 10 m spatial resolution, and a high frequency tidal sequence with a 5-min temporal resolution is reconstructed from typical spring tides. The vertical datums of both the topography and tidal water levels are strictly unified to the Mean Sea Level (MSL) to maintain physical consistency. Comparative experiments across multiple water level scenarios reveal a distinct threshold effect and non-linear expansion characteristics in inundation responses under complex geomorphological conditions. Because the traditional bathtub model fails to account for the blocking effects of inland physical barriers, its overestimation increases significantly once the water level exceeds critical flood protection thresholds. By generating high resolution Time of Arrival (ToA) maps, the proposed framework provides a robust spatial–temporal basis for precise coastal risk assessment, evacuation planning, and defense resource allocation. Full article

Digital Elevation Model (DEM) resolution plays a critical role in hydraulic flood modelling by influencing inundation accuracy, spatial precision and computational efficiency. However, limited studies have simultaneously evaluated both inundation accuracy and computational performance across multiple DEM resolutions in event-based urban flood modelling. This study aims to evaluate the impact of DEM spatial resolution on the performance of HEC-RAS 1D and 2D models in simulating an event-based urban flood that occurred on 3 March 2025. A 1 m LiDAR-derived DEM was resampled to 2 m, 5 m, 8 m, 10 m, 20 m, 25 m, and 30 m resolutions to assess the effects of terrain generalization on hydraulic response. Simulated inundation extents were validated against observed flood areas derived from aerial imagery, and computation time was recorded for each scenario. Results reveal a clear trade-off between spatial accuracy and computational demand. In the 1D simulations, deviation from observed inundation increased from 0.76 ha at 1 m to 2.50 ha at 30 m, while computation time remained relatively stable. The 2D simulations were more sensitive to DEM resolution, with deviation increasing from 0.33 ha to 3.12 ha and longer runtimes at finer resolutions. Among the evaluated scenarios, the 10 m DEM provided the most balanced performance in both 1D and 2D models. For rapid assessment and operational flood management, where computational efficiency and timely decision-making are critical, a 1D modelling approach combined with a 10 × 10 m DEM is recommended as a practical and efficient solution. Full article

Floods increasingly threaten communities and infrastructure in Uganda due to climate variability and land use changes. This study assessed flood hazard, vulnerability, and risk in the Mpanga River Catchment using the Rainfall–Runoff–Inundation (RRI) model integrated with the Analytical Hierarchy Process (AHP). The RRI model showed good performance during calibration (NSE = 0.83) and validation (NSE = 0.71), enabling the generation of hazard maps for different return periods. Results revealed a clear escalation in flood extent with increasing return period, where inundation expanded from about 120.5 km² in the 5-year event to nearly 348.4 km² under the 100-year flood scenario. Vulnerability was evaluated through AHP using nine indicators (Land use, population density, distance to river, elevation, rainfall, slope, drainage density, Total Wetness Index, and soil type); however, only Land Use and population density were retained in the final mapping due to data relevance and weight dominance. Combining hazard and vulnerability produced risk maps that revealed most of the catchment falls under low to moderate risk, with high-risk areas concentrated in upstream urbanized zones. Validation with satellite-derived flood maps confirmed model reliability. Evaluation of mitigation strategies showed dams and channel improvements to be the most effective in reducing flood extent. The study provides a practical framework for flood risk management in data-scarce environments, supporting evidence-based planning and interventions. Full article