Search Results (170)

In Taiwan, hydro-meteorological data are fragmented across multiple agencies, limiting the effectiveness of coordinated flood response. To address this challenge and the increasing uncertainty associated with extreme rainfall, a real-time disaster prevention platform has been developed. This system integrates multi-source data and geospatial information through a cluster-based architecture to enhance pluvial flood management. Built on a Service-Oriented Architecture (SOA) and incorporating Internet of Things (IoT) technologies, AI-based convolutional neural networks (CNNs), and 3D drone mapping, the platform enables automated alerts by linking sensor thresholds with real-time environmental data, facilitating synchronized operational responses. Deployed in New Taipei City over the past three years, the system has demonstrably reduced flood risk during severe rainfall events. Region-specific action thresholds and adaptive strategies are continually refined through feedback mechanisms, while integrated spatial and hydrological trend analyses extend the lead time available for emergency response. Full article

A systems-oriented review of machine learning (ML) over the entire flood management spectrum, encompassing fluvial flood control, pluvial flood management, and resiliency-risk characterization was undertaken. Deep learners like long short-term memory (LSTM) networks perform well in predicting reservoir inflows and outflows. Convolution neural networks (CNNs) and other object identification algorithms are being explored in assessing levee and flood wall failures. The use of ML methods in pump station operations is limited due to lack of public-domain datasets. Reinforcement learning (RL) has shown promise in controlling low-impact development (LID) systems for pluvial flood management. Resiliency is defined in terms of the vulnerability of a community to floods. Multi-criteria decision making (MCDM) and unsupervised ML methods are used to capture vulnerability. Supervised learning is used to model flooding hazards. Conventional approaches perform better than deep learners and ensemble methods for modeling flood hazards due to paucity of data and large inter-model predictive variability. Advances in satellite-based, drone-facilitated data collection and Internet of Things (IoT)-based low-cost sensors offer new research avenues to explore. Transfer learning at ungauged basins holds promise but is largely unexplored. Explainable artificial intelligence (XAI) is seeing increased use and helps the transition of ML models from black-box forecasters to knowledge-enhancing predictors. Full article

Urban pluvial flooding (UPF) is an increasingly critical issue due to rapid urbanization and intensified precipitation driven by climate change that yet remains understudied in the Caribbean. This study analyzes the effects of UPF resulting from the transformation of a natural karstic landscape into an urbanized area considering a sub-watershed in Chetumal, Southern Mexican Caribbean, as a case study. Hydrographic numerical modeling was conducted using the IBER 2.5.1 software and the SCS-CN method to estimate surface runoff for a critical UPF event across three stages: (i) 1928—natural condition; (ii) 1998—semi-urbanized (78% coverage); and (iii) 2015—urbanized (88% coverage). Urbanization led to the orthogonalization of the drainage network, an increase in the sub-watershed area (20%) and mainstream length (33%), flow velocities rising 10–100 times, a 52% reduction in surface roughness, and a 32% decrease in the potential maximum soil retention before runoff occurs. In urbanized scenarios, 53.5% of flooded areas exceeded 0.5 m in depth, compared to 16.8% in non-urbanized conditions. Community-based knowledge supported flood extent estimates with 44.5% of respondents reporting floodwater levels exceeding 0.50 m, primarily in streets. Only 43.1% recalled past flood levels, indicating a loss of societal memory, although risk perception remained high among directly affected residents. The reported UPF effects perceived in the area mainly related to housing damage (30.2%), mobility disruption (25.5%), or health issues (12.9%). Although UPF events are frequent, insufficient drainage infrastructure, altered runoff patterns, and limited access to public shelters and communication increased vulnerability. Full article

The governance of cities in low-elevation zones faces many challenges. Notable among these are losses associated with regular pluvial floods and, more so, the threat of impending extreme floods due to climate change and their impacts on residents, especially amongst socially vulnerable groups. This is exacerbated by the reliance on traditionally exclusive approaches to governance. This paper discusses the flood resilience aspect of urban planning by examining the extent of emphasis on inclusiveness in urban flood resilience literature. We relied on the synthesis of inclusive development and flood resilience literature. The findings suggest that, while inclusive development is a burgeoning aspect of development research, and studies on evaluating urban flood resilience are commonplace, the concept of inclusive urban flood resilience is still in its infancy. Furthermore, we found that while inclusive development is neither static nor finite to allow for measuring it in absolute terms, it can be applied or assessed through any or all of its guiding principles. Consequently, together with the well-established methods of implementing and assessing urban flood resilience, we present a preliminary framework for inclusive urban flood resilience as a guide for future scholarly contributions to this composite field. Scholars and practitioners of urban planning in low-elevation zones are encouraged to move away from top–down siloed approaches that result in exclusions and rely more on integrated, inclusive, and socio-ecological pathways to preserve the integrity of cities. Full article

Mapping urban pluvial flooding (UPF) in data-scarce regions poses significant challenges, particularly when drainage systems are inadequate or outdated. These limitations hinder effective flood mitigation and risk assessment. This study proposes an innovative approach to address these challenges by integrating deep learning (DL) models with traditional methods. First, deep convolutional generative adversarial networks (DCGANs) were employed to enhance drainage network data generation. Second, deep recurrent neural networks (DRNNs) and multi-criteria decision analysis (MCDA) methods were implemented to assess UPF. The study compared the performance of these approaches, highlighting the potential of DL models in providing more accurate and robust flood mapping outcomes. The methodology was applied to Lahore, Pakistan—a rapidly urbanizing and data-scarce region frequently impacted by UPF during monsoons. High-resolution ALOS PALSAR DEM data were utilized to extract natural drainage networks, while synthetic datasets generated by GANs addressed the lack of historical flood data. Results demonstrated the superiority of DL-based approaches over traditional MCDA methods, showcasing their potential for broader applicability in similar regions worldwide. This research emphasizes the role of DL models in advancing urban flood mapping, providing valuable insights for urban planners and policymakers to mitigate flooding risks and improve resilience in vulnerable regions. Full article

Pluvial flooding in South Jakarta poses significant economic disruptions, requiring efficient mitigation strategies. This study focuses on optimizing mobile pump deployment as a non-structural flood control measure. Despite the use of mobile pumps in flood response, there is limited research on their systematic optimization for pluvial flood mitigation. This study presents a transferable framework for deploying mobile pumps to mitigate pluvial flood risks in urban areas, demonstrated through a case study in South Jakarta, Indonesia. The findings indicate that flood depths of 75 cm have a 20–50% probability of occurrence, and rainfall in South Jakarta follows a distinct hourly distribution, with 56.6% of the rainfall occurring in the first hour and 43.4% in the second. Radar imagery from the BMKG is used here as the main tool for real-time rainfall detection. The optimization framework considers channel capacity, flood frequency, impact severity, accessibility, and operational protocols. Among 29 flood-prone locations analyzed, 8 of them require mobile pump intervention. Seven locations benefit from integration with weather prediction tools and SCADA systems, while three require dedicated operational procedures (SOPs). Simulation results indicate that placing mobile pumps near the upstream section of the flooded area yields the most effective flood reduction. A minimum pump capacity of 0.5 m³/s is recommended for optimal performance. This study demonstrates that strategic mobile pump deployment, coupled with predictive tools, significantly reduces pluvial flood risks in South Jakarta and offers a transferable framework for other urban areas. Full article

Climate change and rapid urbanization have increased the risk of urban flooding, making timely and accurate flood prediction crucial for disaster response. However, conventional physics-based models are limited in real-time applications due to their high computational costs. Recent advances in deep learning have enabled the development of efficient surrogate models that capture complex nonlinear relationships in hydrological processes. This study presents a deep learning-based surrogate model designed to efficiently reproduce the spatiotemporal evolution of urban pluvial flooding using data from physics-based models. For the Oncheon-cheon catchment in Busan, the spatiotemporal evolution of inundation at a 10 m spatial resolution was simulated using the physics-based model for various synthetic inundation scenarios to train the deep learning model based on a Convolutional Neural Network (CNN). The training dataset was constructed using synthetic rainfall scenarios based on probabilistic rainfall data, while the model was validated using both a synthetic flood event and a historical flood event from July 2020 with observed ground-based rainfall measurements. The model’s performance was evaluated using quantitative metrics, including the Hit Rate (HR), False Alarm Ratio (FAR), and Critical Success Index (CSI), by comparing results against both synthetic and real (historical) flood events. Validation results demonstrated high reproducibility, with a CSI of 0.79 and 0.73 for the synthetic and real experiments, respectively. In terms of computational efficiency, the deep learning model achieved a speedup 16.4 times the parallel version and 82.2 times the sequential version of the physics-based model, demonstrating its applicability for near real-time flood prediction. The findings of this study contribute to the advancement of urban flood prediction and early warning systems by offering a cost-effective, computationally efficient alternative to conventional physics-based flood modeling, enabling faster and more adaptive flood risk management. Full article

The effects of climate change and increasing urbanization mean that urban areas are facing a greater risk of serious flooding. The paper aimed to adopt a data-driven approach to capture surface flood-prone features, providing a basis for surface flood susceptibility. This research developed an enhanced framework En-XGBoost, which consists of three modules: the core module, preprocessing module, and postprocessing module. Data augmentation, random extraction strategies, and local enhancement were introduced to improve the model’s performance. En-XGBoost was tested in Fuzhou, China. The main findings were as follows: (1) Neighborhood information extraction strategy outperformed information extraction strategy in extracting detailed flood-prone features, producing clearer boundaries between different flood susceptibility levels, and refining the flood risk areas. (2) Crucial explanatory variables were identified as major drivers of flood risk, with location-specific factors influencing the flood causes, necessitating localized analysis for specific sites. (3) The local enhancement, data augmentation, and random strategies improved model performance, with data augmentation proving more effective for stronger models and having limited impact on weaker ones. Model performance requires an appropriate alignment between data complexity and model complexity. En-XGBoost provided support for capturing surface flood-prone features. Full article

Flood models based on high-resolution digital elevation models (DEMs) are important for identifying urban land inundation during extreme rainfall events. Urban pluvial and fluvial floods are influenced by distinct processes that are interconnected; thus, they can transform into one another. Conventional flood models struggle to delineate inundation caused by drainage system overflow (urban pluvial flood) and that caused by rivers (urban fluvial flood). In this study, we proposed a novel method for identifying urban pluvial–fluvial floods using a high-resolution DEM. We developed a DEM-based surface pluvial and fluvial inundation tracking model (DEM-SPFITM) that incorporated flood development and mutual transformation processes. When combined with a surface flood control model (SFCM), this approach enabled tracking of the flow paths and exchanged water volume associated with both flood types. The case study results indicate that the proposed method effectively captures the interplay between pluvial and fluvial flooding, enabling the separate identification of flood extent, depth, and velocity under extreme rainfall conditions for both pluvial and fluvial flooding. Compared to the conventional approach, which independently simulates pluvial and fluvial flooding using the SFCM and subsequently overlays the results to estimate pluvial–fluvial flooding inundation, the proposed method demonstrates superior accuracy and computational efficiency. Simulations of three extreme rainstorms indicated that pluvial flooding primarily contributed to extensive land inundation, characterised by shallower depths and lower velocities, with a limited influence of flood depth on velocity. Meanwhile, fluvial flooding further exacerbated land inundation, leading to significant pluvial–fluvial coexistence. In areas adjacent to these flood zones, fluvial flooding predominated, resulting in greater inundation depths and a more pronounced effect of flood depth on velocity. As rainfall intensity and total rainfall increased, the area of fluvial inundation diminished significantly, whereas pluvial–fluvial coexistence intensified and was distributed in zones with relatively large inundation depths and higher flow velocities. This research presented a novel method for distinguishing between urban pluvial–fluvial floods, providing valuable insights for integrated urban flood management and joint flood risk zoning. Full article

Climate change and urbanization are increasingly threatening urban environments through pluvial flooding, prompting the widespread use of coupled hydrological–hydrodynamic models. These models provide accurate urban flood simulations and forecasting capabilities, and they can analyze the benefits of low-impact development stormwater control measures in surface-flooding processes. However, most studies have primarily focused on analyzing the stormwater control effects for specific flood events, lacking an analytical framework that accounts for uncertainty. This research proposes a framework for evaluating uncertainty in urban pluvial-flood stormwater control, combining urban-scale simulation, stormwater control modeling, and uncertainty analysis, while constructing nonlinear dependencies between the features reflecting the surface-flood-control benefits. Based on uncertainty analysis and copula methods, this research aims to support sustainable urban planning and provide a sustainable decision-making approach for urban stormwater management. The results show that the uncertainty assessment method based on generalized likelihood uncertainty is effective. By comparing the posterior joint distribution with the prior joint distribution, for different governance performance metrics, the joint, synergistic, conditional, and combined governance effects all exhibit consistent trends as the metrics change. The current research presents an innovative method for simulating and analyzing stormwater control benefits at the urban scale, providing valuable insights for urban sustainable development and flood mitigation strategies. Full article

The overuse of concrete in historical areas, currently observed in various urban watersheds in Poland, may pose a significant threat to the water balance of catchments, leading even to pluvial flooding. The distorted water balance may be, to some extent, restored by sustainable green architecture designs. This paper presents an attempt at the numerical assessment of changes in the water balance caused by revitalization in three main historical squares in cities in Lublin Voivodeship, Poland. A proposal for rain garden installation, allowing the partial restoration of the water balance, is also introduced. Numerical calculations of the runoff generation were performed in SWMM 5 software for real weather conditions recorded in Lublin during the period 1 June–31 August 2024. The performed simulations show that an increase in the imperviousness of the studied urban catchments results in a significant increase in runoff characteristics, with a 78.2–90.9% increase in volume and a 108–141.7% increase in peak flows. The introduction of the proposed rain gardens allows the partial reduction in the runoff volume and peak flows, down by 18.1–30.2% and 17.9–32.0%, respectively. Full article

Driven by climate change and rapid urbanization, pluvial flooding is increasingly endangering urban environments, prompting the widespread use of coupled hydrological–hydrodynamic models that enable more accurate urban flood simulations and enhanced pluvial flood forecasting. The simulation method for urban river flooding caused by heavy rainfall has garnered growing attention. However, existing studies primarily concentrate on prediction using hydrodynamic models or machine learning models, and there remains a dearth of a comprehensive prediction framework that couples both models to simulate the temporal evolution of river flood changes. This research proposes a novel framework for simulating urban pluvial river flooding by integrating physically based models with deep learning approaches. The sample set is enhanced through data augmentation and Generative Adversarial Networks, and scheduling control signals are incorporated into the encoder–decoder architecture to enable urban pluvial river flooding forecasting. The results demonstrate strong model performance, provided that the model’s structural complexity is aligned with the available training data. After incorporating scheduling information, the simulated water level process exhibits a “double-peak” pattern, where the first peak is noticeably lower than that under non-scheduling conditions. The current research introduces an innovative method for simulating and analyzing large-scale urban flooding, offering valuable perspectives for urban planning and flood mitigation strategies. Full article

Pluvial flooding, driven by increasingly impervious surfaces and intense storm events, presents a growing challenge for urban areas worldwide. In Baltimore City, MD, USA, climate change, rapid urbanization, and aging stormwater infrastructure are exacerbating flooding impacts, resulting in significant socio-economic consequences. This study evaluated the effectiveness of a soil profile rehabilitation scenario using a 2D hydrodynamic modeling approach for the Tiffany Run watershed, Baltimore City. This study utilized different extreme storm events, a high-resolution (1 m) LiDAR Digital Terrain Model (DTM), building footprints, and hydrological soil data. These datasets were integrated into a fully coupled 2D hydrodynamic model, the City Catchment Analysis Tool (CityCAT), to simulate urban flood dynamics. The pre-soil rehabilitation simulation revealed a maximum water depth of 3.00 m in most areas, with hydrologic soil groups C and D, especially downstream of the study area. The post-soil rehabilitation simulation was targeted at vacant lots and public parcels, accounting for 33.20% of the total area of the watershed. This resulted in a reduced water depth of 2.50 m. Additionally, the baseline runoff coefficient of 0.49 decreased to 0.47 following the rehabilitation, and the model consistently recorded a peak runoff reduction rate of 4.10 across varying rainfall intensities. The validation using a contingency matrix demonstrated true-positive rates of 0.75, 0.50, 0.64, and 0 for the selected events, confirming the model’s capability at capturing real-world flood occurrences. Full article

Under the combined influence of climate change, accelerated urbanization, and inadequate urban flood defense standards, urban pluvial flooding has become an increasingly severe issue. This not only poses significant challenges to social stability and economic development but also makes accurate flood risk assessment crucial for improving urban flood control and drainage capabilities. This study uses Jinan, a typical foothill plain city in Shandong Province, as a case study to compare the performance of differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO) in calibrating the SWMM. By constructing a hydrological–hydrodynamic coupled model using the SWMM and LISFLOOD-FP, this study evaluates the drainage capacity of the pipe network and surface inundation characteristics under both historical and design rainfall scenarios. An agent-based model (ABM) is developed to analyze the dynamic risks and vulnerabilities of population and building agents under different rainfall scenarios, capturing macroscopic emergent patterns from individual behavior rules and analyzing them in both time and space dimensions. Additionally, using multi-source remote sensing data, dynamic population vulnerability, and flood hazard processes, a quantitative dynamic flood risk analysis is conducted based on cloud models. The results demonstrated the following: (1) PSO performed best in calibrating the SWMM in the study area, with Nash–Sutcliffe efficiency (NSE) values ranging from 0.93 to 0.69. (2) Drainage system capacity was low, with over 90% of the network exceeding capacity in scenarios with return periods of 1 to 100 years. (3) The vulnerability of people and buildings increased with higher flood intensity and duration. Most affected individuals were located on roads. In Event 6, 11.41% of buildings were at risk after 1440 min; in the 20-year flood event, 26.69% of buildings were at risk after 180 min. (4) Key features influencing vulnerability included the DEM, PND, NDVI, and slope. High-risk areas in the study area expanded from 36.54% at 30 min to 38.05% at 180 min. Full article

In the last two decades, South Korea has seen an increase in extreme rainfall coinciding with the proliferation of impermeable surfaces due to urban development. When underground drainage systems are overwhelmed, pluvial flooding can occur. Therefore, recognizing drainage systems as key flood-conditioning factors is vital for identifying flood-prone areas and developing predictive models in highly urbanized regions. This study evaluates and maps urban pluvial flood susceptibility in Seoul, South Korea using the machine learning techniques such as logistic regression (LR), random forest (RF), and support vector machines (SVM), and integrating traditional flood conditioning factors and drainage-related data. Together with known flooding points from 2010 to 2022, sixteen flood conditioning factors were selected, including the drainage-related parameters sewer pipe density (SPD) and distance to a storm drain (DSD). The RF model performed best (accuracy: 0.837, an area under the receiver operating characteristic curve (AUC): 0.902), and indicated that 32.65% of the study area has a high susceptibility to flooding. The accuracy and AUC were improved by 7.58% and 3.80%, respectively, after including the two drainage-related variables in the model. This research provides valuable insights for urban flood management, highlighting the primary causes of flooding in Seoul and identifying areas with heightened flood susceptibility, particularly relating to drainage infrastructure. Full article