In March 2019, flooding of the Missouri River and its tributaries destroyed infrastructure and farmland and affected communities, including those in the state of Nebraska. The objective of this study was to assess emergency preparedness and satisfaction with flood response, recovery, and relief efforts 5 years following the 2019 floods in rural eastern Nebraska. Using stratified simple random sampling, this study surveyed 13 Nebraska communities to assess emergency preparedness and satisfaction with flood response, recovery, and relief efforts 5 years following the 2019 Missouri River Flood. Descriptive statistics are reported. Households impacted by the 2019 Nebraska flood reported worsening physical and mental health symptoms and identified major gaps in communication, long-term mental health support, and infrastructure resilience. Self-reported preparedness improved post-flood. Inadequate early warnings and poor information dissemination eroded trust. There are persistent mental and physical health impacts resulting from exposure to the 2019 Missouri River floods that can impact communities' ability to respond and recover from subsequent hazards. Evaluating the impacts of previous disasters is a critical component of increasing community resiliency and local public health and emergency preparedness capacity to serve these populations.

Which exit to choose? A multi-fields cellular automata model for simulating passenger evacuation of subway station platforms under flood disaster

Flooding is the deadliest and most damaging natural hazard worldwide. Despite efforts to make communities more flood-resilient, global flood impacts are on the rise due to ongoing climate change. Amid the expected increase in the intensity and frequency of flood disasters, there is an urgent need to understand the roles of network governance in ensuring coordinated efforts in response to floods. The goal of this study is to examine interorganizational coordination and collaboration in the aftermath of the New South Wales (NSW) Floods in Australia in 2022. Specifically, this study addresses two primary research questions: (1) Which agencies coordinated and collaborated during the response to the NSW Floods? (2) What were the characteristics of the interorganizational coordination system in response to the NSW Floods in 2022? This study uses Natural Language Processing techniques to extract relevant information from the NSW Government (2022) Flood Inquiry Report and the NSW Legislative Council Report (2022) to answer the research questions. The findings indicate that several agencies played a critical coordinating role and collaborated during the response to the NSW Floods. In addition, the analysis of the collaborative networks indicates strong connections between certain entities and weak or absent connections among other entities in the response network. These results provide valuable insights into the relationships and thematic focus of flood-related policies in Australia and underscore the need for enhanced collaboration and coordination among key stakeholders, including government agencies, to ensure a unified and effective approach to flood resilience.

Abstract Floods are among the most devastating natural disasters worldwide, necessitating effective disaster management strategies to mitigate their impacts. This study focuses on the identification of optimal safe assembly areas in the case of urban flash floods using Geographic Information System (GIS) and machine learning techniques. The Bartın River Basin, which has a history of severe flood events, was selected as the study area. A total of 79 micro-basins covering 2,342.87 km 2 were analyzed using 17 parameters related to topography, hydrology, infrastructure, and demography. After normalization, Principal Component Analysis (PCA) was applied to reduce dimensionality from 17 to 2–16 components. Seven clustering algorithms (K-Means, Agglomerative Hierarchical, DBSCAN, MeanShift, Birch, Mini Batch K-Means, and Spectral) were tested, and their performances were compared using the Silhouette Score Index (SSI). Results indicate that the K-Means algorithm with 2 principal components and 3 clusters achieved the best performance (SSI > 0.5), identifying micro-basins 9, 34, and 41 as the most suitable assembly areas. Post-clustering validation revealed that these areas combine low flood risk indicators with high accessibility. More than 85% of the basin’s 206,715 inhabitants can reach a safe assembly point within 30 min (≤ 30 km at an average evacuation speed of 30 km/h). Notably, micro-basin 9 alone provides access for 68.3% of the population within 5–15 km, highlighting its strategic importance. Historical flood data (2020–2024) further confirmed that two of the identified basins are located in zones with fewer past flood events, reinforcing their reliability. The proposed framework bridges theoretical optimization and real-world feasibility, providing actionable insights for disaster planners. Future research will focus on large-scale evacuation simulations and the integration of population flows and shelter capacities to further strengthen operational applicability.

Spatial prediction of emergency supply demand under urban flood disaster events: A case study of the “23·7” rainstorm in Beijing

Due in significant part to drainage degradation, sediment accumulation, tidal backflow, and disjointed institutional coordination, flood disasters continue to occur in Sidoarjo Regency every rainy season. In order to improve preparedness, expedite emergency responses, and facilitate methodical post-disaster rehabilitation, this project creates an organized Standard Operating Procedure (SOP) for flood control in the Afvoer Pucang region. Using interviews, field observations, and data from important organizations such PUBMSDA Sidoarjo, BPBD East Java, BPBD Sidoarjo, and the Brantas River Basin Center (BBWS), a qualitative case study design was used. The Miles and Huberman paradigm, which prioritizes reduction, organization, and iterative verification, was used to examine the data. The results demonstrate that a successful SOP needs to function throughout three interrelated stages. In order to improve community preparedness, the pre-flood phase concentrates on regular channel maintenance, hydrometeorological monitoring, public outreach, and disaster simulation exercises. Quick decision-making, command post activation, technical coordination for pump and gate operations, evacuation of susceptible individuals, and prompt logistics distribution are crucial during the emergency response phase. Structured damage assessments, expedited cleanup, and regular process reviews to improve institutional coordination are the main focuses of the post-flood phase.

The flash floods that struck Aceh Province in November 2025 devastated Aceh Province, particularly several severely affected areas. The Indonesian National Armed Forces (TNI) moved quickly to address the flood disaster. One form of their involvement was in the distribution of logistics. Several factors need to be considered in distributing logistical aid in Aceh Province to ensure its smooth operation, supported by relevant agencies such as the National Disaster Management Agency (BNPB) and the National Disaster Management Agency (BNPA). The purpose of this study was to determine the TNI's involvement in the distribution of logistics for the flash flood disaster in Aceh. This study used a qualitative literature study method with a descriptive qualitative approach. Data collection techniques used documentation. The data were obtained from official websites, national news sites, and relevant scientific journals. The results showed that the TNI's involvement significantly contributed to the smooth distribution of logistics to communities affected by the flash floods in Aceh. Transportation readiness and coordination with relevant agencies accelerated aid distribution.

Flood disasters in North Sumatra Province show an increasing trend, both in terms of the frequency of occurrences and the magnitude of their impacts, thereby necessitating adaptive and responsive crisis communication strategies that address community needs. In this context, Instagram has become an important medium for disseminating disaster-related information to the public. This study aims to analyze the role of Instagram in the dissemination of flood disaster information in North Sumatra through a case study of the official accounts @bpbdprovinsisumut and @infobmkgsumut, using the perspectives of contemporary communication theory, crisis communication theory, and agenda-setting theory. The research employs a descriptive qualitative approach with content analysis techniques applied to posts and user interactions during the disaster period. The findings indicate a complementary division of roles: the @infobmkgsumut account is more dominant in the pre-disaster phase by delivering early warning information based on meteorological data, while the @bpbdprovinsisumut account focuses on the emergency response and post-disaster phases through evacuation information and the coordination of aid. The use of visual elements such as infographics and field documentation has proven effective in simplifying technical messages so they are easily understood by the public. However, the communication pattern applied remains predominantly one-way and institutional in nature, with limited emphasis on empathy and the narratives of victims’ experiences. In addition, the management of two-way interactions in the comment sections has not been optimally utilized as a channel for reporting residents’ emergency conditions. This study concludes that Instagram has evolved into a dynamic social space in disaster management, yet strengthening human-centered message framing strategies is still necessary.

Flood disasters pose persistent socio-economic and environmental challenges, particularly in tropical regions such as Sumatra, Indonesia. Traditional hydrological and GIS-based approaches often struggle to capture complex interactions among terrain, rainfall, land use, and human activities. This review critically examines recent applications of Geospatial Artificial Intelligence (GeoAI) for flood disaster mapping, focusing on machine learning models, geospatial data sources, and computational workflows. Analysis of selected studies highlights that satellite imagery and digital elevation models remain dominant data inputs, while Random Forest, Support Vector Machines, Convolutional Neural Networks, and hybrid models are most frequently applied. Workflow patterns reveal recurring stages of data preprocessing, model training, and post-processing, yet gaps persist in model explainability, feature selection, and generalization across regions. The study underscores the importance of integrating multi-source data, standardizing workflows, and fostering interdisciplinary collaboration to enhance operational flood risk management. Findings provide a foundation for advancing GeoAI research and translating methodological innovations into practical flood preparedness and mitigation strategies.

ABSTRACT This poem examines the lived experience of flood disaster in Sumatra through a lyric narrative that foregrounds survival, memory, and collective resilience. Drawing on the imagery of water, mud, and disrupted domestic space, the poem portrays catastrophe not as a singular event but as a process that reorganizes relationships between body, landscape, and community. The river functions as both a material force and a symbolic agent, erasing boundaries while revealing ethical interdependence among survivors. Attention to sensory perception, touch, sound and breath reflects trauma's embodied nature and aligns with poetry therapy approaches that privilege somatic memory and narrative reconstruction. Recovery is framed as an ongoing practice of meaning-making, where survival emerges through communal bonds rather than physical structures. The work contributes to disaster narratives by integrating ecological vulnerability with human agency, offering poetry as a reflective medium for processing collective trauma and imagining continuity after rupture.

Urban flood disaster management is an interdisciplinary field that integrates hydrology, geology, engineering, and urban planning, with prediction, assessment, and optimization serving as its core components. However, a comprehensive and systematic synthesis of recent developments in this domain remains limited, constraining both theoretical understanding and practical advancement. To address this gap, this study conducts an in-depth analysis of urban flood management research as a systematic review, with a particular focus on advances in prediction, assessment, and optimization. Utilizing a multistep holistic review, combining bibliometric and scientometric analysis with structured literature categorization, the research critically examines and synthesizes relevant findings. This study analyzed 166 research papers related to urban flood management within the Web of Science database. Through co-citation and keyword co-occurrence analyses, five dominant research dimensions are identified: physics-based simulation methods, data-driven approaches, risk assessment tasks, optimization strategies, and miscellaneous emerging topics. Based on these insights, we propose a task-oriented framework that systematically integrates prediction, assessment and optimization across the four phases of disaster management: mitigation, prevention, emergency response and recovery. This framework aids scholars and practitioners in understanding and implementing effective techniques and strategies. The study’s findings shed light on key trends and potential future directions, providing a roadmap for further exploration of urban flood management and guiding professionals in related fields.

Flooding is one of the most frequent hydrometeorological disasters and has a significant impact on social, economic, and health aspects of society, requiring efforts to improve preparedness through education on flood disasters in schools, especially in flood-prone areas. The purpose of this study was to determine the effect of education on students' knowledge and preparedness in facing flood disasters. The method used in this study was quantitative with a pre-post test one group design and involved 50 ninth-grade students. The measurement instrument was a questionnaire sheet given before and after education. Data analysis used a paired t-test. The results showed an increase in the average knowledge score from 5.60 (pre-test) to 7.00 (post-test) with a significance value of p < 0.001, which means that education has a significant effect on increasing students' knowledge. A significant improvement was seen in the material on the causes of flooding, mitigation and capabilities, as well as preparedness and information utilization, with the post-test showing a correct answer percentage of over 90%. Education on flood disasters has proven effective in improving students' understanding of the causes, mitigation, and flood response measures.

Flood disasters are increasing worldwide due to climate change, posing growing risks to infrastructure and human life. Korea, where nearly 70% of annual rainfall occurs during the summer monsoon, is particularly vulnerable to extreme precipitation events intensified by El Niño and La Niña. This study investigates how terrain resolution influences flood simulation accuracy by comparing a 1 m LiDAR digital elevation model (DEM) with a DEM generated from a 1:5000 topographic map. Flood depth and velocity fields produced by the two DEMs show notable quantitative differences: for final flood depth, the 1:5000 DEM yields a mean absolute error of approximately 56.9 cm and an RMSE of 76.4 cm relative to LiDAR results, with substantial local over- and underestimations. Flow velocity and maximum velocity also show significant deviations, with RMSE values of 58.0 cm/s and 68.4 cm/s, respectively. Although the 1:5000 DEM captures the general inundation pattern, these discrepancies—particularly in narrow channels and urbanized floodplains—demonstrate that coarse-resolution terrain data cannot reliably reproduce hydrodynamic behavior. We conclude that while 1:5000 DEMs may be acceptable for reconnaissance-level hazard screening, high-resolution LiDAR DEMs are essential for accurate flood depth and velocity simulation, supporting their integration into engineering design, urban flood risk assessment, and disaster management frameworks.

The flood disaster that hit Medan City caused a multidimensional impact on the community, especially the psychological impact experienced by the victims. In addition to material losses, flood victims often face emotional distress in the form of anxiety, stress, trauma, and a sense of loss. This study aims to examine the role and effectiveness of individual counseling in dealing with the psychological problems of post-flood victims. The research method uses a qualitative approach with case studies through in-depth interviews, observations, and documentation of victims who receive individual counseling services. The results show that individual counseling plays an important role in helping victims understand their emotional state, manage stress, and rebuild hope and resilience after disasters. Individualized counseling also provides a safe space for victims to express their feelings, gain support, and develop adaptive strategies in dealing with trauma. These findings confirm that psychological interventions based on individual counseling can be one of the effective approaches in disaster victim recovery, especially floods, so that it can strengthen psychosocial aspects and improve the quality of life after disasters. Thus, this research contributes to the development of more responsive and sustainable counseling services in the context of disaster management in Indonesia.

Abstract. Urban areas are increasingly experiencing more frequent and intense pluvial flooding due to the combined effects of climate change and rapid urbanization – a trend expected to continue in the coming decades. This highlights the growing need for effective flood forecasting and disaster management systems. While recent advances in GPU computing have made high-resolution hydrodynamic modeling feasible at the urban scale, operational use remains limited, particularly for large domains where single-GPU processing falls short in terms of memory and performance. This study demonstrates the capabilities of the hydrodynamic model RIM2D (Rapid Inundation Model 2D), enhanced with multi-GPU processing, to perform high-resolution pluvial flood simulations across large urban domains such as the whole state of Berlin (891.8 km2) within operationally relevant timeframes. We evaluate RIM2D’s performance across spatial resolutions of 2, 5, and 10 m using GPU configurations ranging from 1 to 8 units. Two flood scenarios are analyzed: the real-world pluvial flood of June 2017 and a standardized 100-year return period (HQ100) event used for official hazard mapping. Results show that RIM2D can deliver detailed flood extents, flow characteristics, and impact estimates for the 48 h 2017 event in 8 min at 10 m resolution, 34 min at 5 m, and approximately 5.5 h at 2 m using 8 A100 GPUs – fast enough to be integrated into real-time early warning systems. Multi-GPU processing proves essential not only for enabling high-resolution simulations (e.g., dx= 2 m or finer), but also for making simulations at resolutions finer than 5 m computationally feasible for flood forecasting and early warning applications. Additionally, we find that beyond 4 GPUs, runtime improvements become marginal for 5 and 10 m resolutions, and similarly, more than 6 GPUs offer limited benefit at dx= 2 m resolution, illustrating the balance between computational nodes of the used GPUs and number of raster cells of the model. Moreover, simulations at a finer dx= 1 m resolution demand more than 8 GPUs to be run. Overall, this work demonstrates that large-scale, high-resolution flood simulations can now be executed rapidly enough to support operational early warning and impact-based forecasting. With models like RIM2D and the continued advancement of GPU hardware, the integration of detailed, real-time flood forecasting into urban flood risk management is both technically feasible and urgently needed.

Hybrid adaptation to urban riverine floods: a cost-benefit analysis in Vilanova i la Geltrú (Spain)

The flash flood disaster that struck Batu Busuak, Padang City, in November-December 2025 caused massive damage to residential areas. Within three weeks, this area experienced four consecutive flash floods that swept away 11 houses, severely damaged 46 houses, and forced hundreds of families to evacuate. This community service activity aimed to provide humanitarian aid in the form of necessities to flood victims through international collaboration between Sekolah Tinggi Agama Islam (STAI) As-Sunnah Deli Serdang and Bayaan Association Singapore. The service partners were flood-affected communities in Koto Panjang Ikua Koto Sub-district, Koto Tangah District, and Batu Busuak, Lambung Bukik Sub-district, Pauh District, Padang City. Implementation methods included multi-institutional coordination, international fundraising, local procurement of aid, and door-to-door and distribution-point delivery. A total of IDR 57,000,000 was collected and used to procure necessity packages for 150 affected households. The activity was conducted on December 24-27, 2025, and was led directly by the Chairman of STAI As-Sunnah. The results showed that aid distribution successfully reached victims in isolated locations using adaptive methods according to field conditions. Community response was very positive despite high psychosocial trauma, where residents lived in constant vigilance against subsequent flood threats. This activity was also followed by sustainability programs, including plans to build mosques and bore wells, undertaken by Yayasan Peduli Kemanusiaan Arrisalah as long-term recovery efforts. The Singapore-Indonesia international collaboration in this activity demonstrated the effectiveness of global Islamic philanthropy networks for local emergency response.

The frequency and intensity of flood disaster events are increasing. It is well documented that community flood vulnerability and impacts vary depending on household attributes. Nevertheless, vulnerable households in high flood risk areas rarely invest time and effort to reduce their vulnerability. A mixed-methods case study investigated the key factors that shaped flood risk perceptions, awareness levels, and information accessibility among households with three levels of vulnerability. Flood concerns were found to increase with household vulnerability and having a vulnerable family member elevated flood disaster risk. Disaster awareness is affected by household attributes such as evacuation assistance for vulnerable family members, as well as by support from government/community groups. Personal networks, including families and neighbours, are primary flood information verification sources, with WhatsApp being the main social media platform utilised. The findings suggest that governments should tap into trusted community and social networks to disseminate flood risk communication to reduce disaster vulnerability.

Over a million people were affected by the massive flooding, landslides, infrastructure collapse, and humanitarian disaster caused by Cyclone Ditwah, which hit Sri Lanka in late November 2025. This article looks at how well disaster communication systems performed during Ditwah, highlights significant shortcomings, particularly in terms of timely notifications and linguistic inclusivity, and analyzes how these shortcomings affected the catastrophe's scope. Recommendations for improving disaster communication systems to better serve all communities are included in the article's conclusion.

Prioritisation Recommendation Mapping (PrioReMap): A method for supporting relief coordination in flood disaster response