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This study’s objective was to understand and analyze how and to what extent a gender perspective is handled on research issues and governing social equity to strengthen and facilitate the strength of cultural resilience in dealing with flood disasters. A conceptual study through a collective literature review and using the community’s risk perception and map in the Focus Group Discussion and Stakeholder Meetings were conducted to explore and review the trajectory understanding and governance of social equity. The updated flood reports or projects related to social equity for recommending several international policies are sourced from the UN Woman Program Agenda 2015 to 2022’s document: “Plan for Equal: Gender equality, social justice, sustainability, including in the wake of COVID-19”, and territorial policies, river watershed, and society’s response to flooding from UN-ISDR. The UN Woman project, UN-ISDR, showed a bright perspective of gender in its approach to disaster management. Based on the database search results, specific flood disasters and gender research and vice versa in Timor Island have not been found in any previous research, but it is argued by previous expert opinions with studies related to different territories. Reviewing the projected sustainability of developing adaptive and preventive solutions to flood disasters from a socio-political perspective, all UN documents dealing with gender inclusion are supported by their own planning program for gender equality and social equity in flood-prone areas in Asia Pacific. The similarities and differences that were analyzed with social equity have summarized the two sides and reciprocity of how global and local feminists viewed gender itself, revisiting its solution rather than its gap between gender equality and/or gender mainstreaming. Timorese culture represented a unique profile of Asia Pacific’s flood management, cultural resilience, and social equity in terms of governing a sustainable inclusiveness solution in the borderland of Timor Island. [ABSTRACT FROM AUTHOR]

The frequency of global floods has increased, posing significant threats to economic development and human safety. Existing flood risk zoning studies in disaster prevention lack integration of the natural–economic–social chain and urban resilience factors. This study addresses this gap by constructing flood disaster risk and intensity indices using data from 31 provinces and 295 prefectural-level cities in China from 2011 to 2022. These indices incorporate natural (rainfall), economic (GDP), and social (population, built-up area) indicators to assess the flood likelihood and loss degree, providing comprehensive risk and intensity ratings. The study also examines the impact of resilience factors—environmental (green space), infrastructural (rainwater pipeline density), and natural resource (watershed areas)—on flood intensity. Findings reveal that high-risk regions are mainly in the Yangtze River Basin and southern regions, while high-intensity regions are primarily in the middle and lower Yangtze River and certain northwestern cities. Increasing rainwater pipeline density mitigates flood impacts in high-risk, high-intensity areas, while expanding green spaces and pipelines are effective in high-risk, low-intensity regions. This paper proposes a comprehensive flood hazard zoning mechanism integrating natural, economic, and social factors with urban resilience, offering insights and a scientific basis for urban flood management. [ABSTRACT FROM AUTHOR]

Floods, a common natural disaster, it affects more than half of all natural disasters, primarily due to high floods, high tides, heavy rainfall, and human activity. Distinguishing between the flooded and non-flood areas during the disaster is dependent on the quality of images collected from different sources. Since synthetic aperture radar (SAR) images can see through cloud cover and capture photos of the Earth's surface during bad weather, they are excellent for detecting floods. Image Enhancement is the approach that helps to increase the resolution of the SAR images, which helps to more accurately categorize the flooded and non-flooded areas. The quality of satellite and aerial imagery can be enhanced to recognize flooded and non-flooded areas. Our proposed approach uses a super-resolution technique to enhance the resolution of SAR images. We develop a super resolution-based generative adversarial network, or SSR-GAN (Satellite Super Resolution-based Generative adversarial network). In our approach to estimating generative models through an adversarial process, we concurrently train two different models: a generative model G and a discriminative model D. An image with low resolution is transformed into high pixel density. The peak signal-to-noise ratio (PSNR), the structural similarity index(SSIM), Multiscale Structural Similarity (MSSIM) and mean squared error (MSE) are the performance metrics used to compare the interpolation methods for enhancing resolution. In comparison to the existing approaches with our proposed model SSR-GAN, the values of PSNR, SSIM, and MSSIM are greater, and the MSE error value is lower. [ABSTRACT FROM AUTHOR]

Objectives: Flood disaster is a very destructive natural disaster. The main reasons for its generation include heavy rainfall, storm surge and dam break. When flood disaster occurs in populated areas such as cities and towns, the flood will directly threaten the safety of life and property of residents. Also it will cause paralysis of land and underground transportation, interruption of water and electricity transportation. In the process of flood rescue, quick and accurate identification of flooded roads is conducive to planning appropriate personnel transfer and material transportation routes, and reducing subsequent losses caused by floods. Aiming at the problem that roads in the flood disaster scenario cannot be automatically and correctly identified, this paper proposes an end-to-end flooded road detection method based on deep learning. Methods: The proposed method uses a three-branch encoder-decoder structure and uses strip convolution, in which the efficient extraction of linear features is realized. And the coordination dual attention mechanism can effectively guide the network, and realize the identification of road areas. The method can effectively utilize the historical optical remote sensing images before the disaster and the real-time optical remote sensing during the disaster. The image is applied to detect the flooded and non-flooded roads in the disaster-stricken area, and a comparative experiment is carried out on the self-built dataset. Results: The experimental results show that the precison and recall rate are 0.838 1 and 0.666 8 on pre-disaster road, 0.796 6 and 0.607 4 on post-disaster road, 0.780 0 and 0.661 4 on affected road respectively. Conclusions: The proposed method has achieved the goal of automatically identifying the flooded roads in the flood-stricken area. The ability of detecting flooded roads and non-flooded roads can provide strong support for flood disaster rescue and reduce losses of life and property caused by flood disasters. [ABSTRACT FROM AUTHOR]

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Within the framework of global climate change, there is a recurring occurrence of floods and waterlogging disasters, which pose significant risks to human lives and overall safety. The phenomenon of urbanization has led to an increased vulnerability of cities to flood disasters. Evacuation serves as a viable strategy for adapting to climate change, managing intense precipitation events, and mitigating the impact of flood disasters. Hence, the meticulous choice of shelters has emerged as a crucial element in enhancing the effectiveness of evacuations. Nevertheless, the process of choosing shelters sometimes neglects to consider the potential for flooding and has not undergone thorough testing. Hence, the objective of this research is to systematically choose shelters within the central metropolitan region, incorporate flood risk considerations into the shelter site model, and assist cities in mitigating the consequences of floods resulting from climate change. To begin with, employ a two-dimensional shallow water model in order to replicate instances of intense precipitation and produce data on flood inundation. Subsequently, the simulation results are utilized to assess the flood risk using the entropy weight approach. This risk is then incorporated into the multi-objective optimization model to determine the optimal shelter placement. Ultimately, the ABM model was employed to create a simulation of crowd evacuation in urban areas, confirming the efficacy of shelter selection and thoroughly examining the influence of various shelters on the efficiency of community emergency evacuation. The study's findings indicate that a total of 26 shelters were chosen within the study area using a multi-objective methodology, ensuring the inclusion of all affected populations. The utilization of multi-objective models in the selection of shelter places has been shown to enhance evacuation efficiency, as confirmed by the ABM model. The simulation findings indicate a decline in the population's death rate from 1.94% to 0.98%, an increase in the evacuation rate from 97.4% to 98.4%, and a reduction in the number of casualties by 133 individuals.

Abstract Older adults in disaster contexts are often thought of as a passive, vulnerable population that lacks agency and capacities to cope in the aftermath. However, it can be argued that older adults may have underrecognized strengths that can be utilized pre-, peri-, and post-disaster. One of these strengths is older adults’ unique social capital that stems from long-standing connections with other members of their respective communities. Using data from in-depth, semistructured interviews with farmers in British Columbia 3–11 months after the 2021 floods, this research explored the experiences of older adult farmers’ recovery. The farmers discussed how they leveraged their social capital to aid in their recovery efforts from the flood event. By using their bonding social capital, older adult farmers transformed their existing, deep-rooted connections into post-disaster assistance. This, in turn, generated the idea of the therapeutic community, helping community members cope in the aftermath. This research indicated the need to further examine how older adults in disaster settings can be viewed as assets with community knowledge and skills as opposed to solely as a vulnerable population.

In order to improve the flood disaster prevention and mitigation capacity, taking Henan Province as the study area, the paper built a comprehensive flood disaster bearing capacity evaluation index system covering disaster prevention capacity, disaster resistance capacity, disaster relief capacity and recovery capacity, and assessed the comprehensive flood disaster bearing capacity of Henan Province by using AHP-entropy combination assignment method and comprehensive evaluation index model. The results show that a) the comprehensive flood disaster bearing capacity of Henan Province shows certain regular changes, and the distribution characteristics of comprehensive disaster bearing capacity and disaster relief capacity are basically the same, overall the central region > southwest region > northeast region > east region > southeast region northwest region. South region has the strongest disaster prevention capacity, while the north and central regions have weaker disaster prevention capacity; cities in the southwest-northeast regions have stronger disaster resilience, while cities in the north- west-southeast regions are weaker; recovery capacity basically decreases from northwest to southeast. The area with medium and above comprehensive disaster-bearing capacity accounts for 66.67% of the total area, and the area with lower medium capacity accounts for 33.33% of the total area. b) In terms of the disaster-bearing capacity of each city (pilot area), Xinyang City has the strongest disaster prevention caрасity and the pilot area of Jiyuan has the weakest disaster prevention capacity and disaster relief capacity. Zhengzhou City has the strongest disaster prevention capacity, disaster relief capacity and recovery capacity, and Sanmenxia City has the weakest disaster relief capacity. Zhumadian City has the weakest recovery capacity. The comprehensive disaster bearing capacity of flood disaster in Henan Province is at a medium and low level on the whole, so it is necessary to formulate reasonable strategies to improve the comprehensive disaster bearing capacity of flood disaster. [ABSTRACT FROM AUTHOR]

Recently, surveillance technology was proposed as an alternative to flood monitoring systems. This study introduces a novel approach to flood monitoring by integrating surveillance technology and LiDAR data to estimate river water levels. The methodology involves deep learning semantic segmentation for water extent extraction before utilizing the segmented images and virtual markers with elevation information from light detection and ranging (LiDAR) data for water level estimation. The efficiency was assessed using Spearman's rank-order correlation coefficient, yielding a high correlation of 0.92 between the water level framework with readings from the sensors. The performance metrics were also carried out by comparing both measurements. The results imply accurate and precise model predictions, indicating that the model performs well in closely matching observed values. Additionally, the semi-automated procedure allows data recording in an Excel file, offering an alternative measure when traditional water level measurement is not available. The proposed method proves valuable for on-site water-related information retrieval during flood events, empowering authorities to make informed decisions in flood-related planning and management, thereby enhancing the flood monitoring system in Malaysia. [ABSTRACT FROM AUTHOR]

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Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Flooding is a recurring issue in India, affecting 10–15 states annually during monsoon season and coastal regions due to cyclones. Microwave satellite data from SAR sensors like RISAT-1A, launched in February 2022, offers near real-time flood mapping crucial for relief efforts. Various acquisition modes, including medium resolution ScanSAR (MRS) mode, coarse resolution ScanSAR (CRS) mode and fine resolution Strimap (FRS) mode, provide suitable resolutions for flood mapping. Case studies demonstrate the behaviour of the backscatter coefficient in flood pixels, essential for flood map preparation. Validation against optical datasets shows a high accuracy of 91% in CRS, 94% in MRS and 94% in FRS, which is acceptable for near real-time mapping. The 67 and 91 flood maps were generated in 2022 and 2023 respectively, aiding state and central disaster management. [ABSTRACT FROM AUTHOR]

Although many studies have been conducted in the field of social resilience, the exact angles of how a local community acts in the face of crisis conditions and the impact of what are known as social resilience indicators in the scientific literature remain unclear. Conducting in-depth research using a qualitative and phenomenological approach on a local scale can provide a deeper understanding of the dimensions and layers of a local community's resiliency. This research was compiled to understand the resilience indicators of the local community in the face of crisis conditions, and with the help of rereading the experience of the residents of Saadi Shiraz neighborhood in the flood disaster of 2018, it tried to achieve the goal of the research. For this purpose, in addition to reviewing the theoretical literature of the research, while conducting 20 in-depth and semi-structured interviews with residents and analyzing the content of the interviews with the help of MAXQDA software, the findings were analyzed in 8 main axes and the form of a theoretical framework with 28 indicators effective on the resilience of the local community. has provided the findings of the research show the importance of "a sense of identity and belonging to the neighborhood" as well as "internal cohesion of the local community" and "flexibility and creativity of the local community" in promoting the social resilience of Saadi neighborhood. In addition, the weakness of the indicators of "public awareness and culture building" and "trust in urban management institutions" has been evident in how this local community faced the crisis. The present research findings can be cited and applied in the decision-making of urban management layers, especially in the field of crisis management at the scale of urban neighborhoods. [ABSTRACT FROM AUTHOR]

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In recent years, with the acceleration of urbanization and the frequent occurrence of extreme weather globally, the risk of urban flood disasters has gradually increased, and its potential consequences are immeasurable. Therefore, conducting risk assessment of urban flood disasters is of great significance, as it is one of the foundations and decision-making means for Disaster Prevention and Mitigation, and has become a hot topic and trend in current research. This paper starts by exploring the concept and formation mechanism of urban flood disasters, taking Hazard Factors, Disaster-prone Environment sensitivity, Vulnerability of Exposed Bodies, and Disaster Prevention and Mitigation Capabilities as primary indicators. Based on this, a risk assessment index system is established with 14 secondary indicators, such as annual average rainfall, distance to water systems, elevation, and terrain undulation. The spatialization of each indicator data point is processed through ArcGIS10.7, and the importance of hazard and sensitivity indicators is ranked using the Random Forest algorithm. The indicators are then weighted using a combination of the Analytic Hierarchy Process (AHP) and the entropy method, and the combined weights of each assessment indicator are calculated. Taking Wuhan City as the research area, the weights of each indicator are input into the established risk assessment model. ArcGIS spatial analysis techniques and raster calculation functions are utilized to solve the fuzzy comprehensive evaluation of the assessment model, obtaining zoning maps of risk levels for hazard, sensitivity, vulnerability, disaster prevention, and mitigation capabilities, as well as the distribution of comprehensive risk levels. The validity and rationality of the model results are verified by actual disaster data, providing important reference for urban flood disaster prevention in the future. [ABSTRACT FROM AUTHOR]

Under the combined influence of climate change and urban development, the risk of urban flooding caused by extreme weather events has increased significantly, making assessing flood vulnerability and resilience increasingly crucial for urban flood management. With the 45 counties in Qinghai Province as the research objects, the hazard risk of flood and exposure are combined to study their vulnerability. At the same time, resilience is evaluated by the indicators selected from four dimensions (society, economy, environment, and infrastructure). Through Z-scoring, the vulnerability and resilience of each county are clustered into four groups to explore their associations from a spatial balance perspective. Obstacle factor analysis is introduced to summarize the key factors affecting the improvement of urban resilience in Qinghai Provence. The results show that the eastern areas of Qinghai experience high vulnerability to flooding because of high levels of hazard and exposure. What is more, Xining, Haidong, and Haixi experience a high level of resilience. A strong spatial mismatch between vulnerability and resilience exists in Qinghai, with 24 counties (58%) being self-adapted, 8 counties (18%) over-abundant, and 11 counties deficient in terms of nature–nurture. The length of levee and number of beds in medical institutions are the main obstacles to resilience in Qinghai. The research results can provide a theoretical and scientific basis for future urban flood management and resilience development in the Qinghai–Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Climate change is global threat and the world is now facing its outcomes more than the last decades. Climate change has increased the frequency and intensity of flooding among other extreme weather events. The execution of knowledge management in different phases of disaster management can be effective in mitigating damages caused by floods to natural and human environments. The present study identified effective actions to implement the five stages of knowledge management in the stages before, during and after the flood event. For this purpose, a systematic review of sources and qualitative content analysis have been applied to evaluate the published research in four stages of items collection, descriptive analysis, selection and classification, and assessment. According to the findings, the pre-crisis stage requires 13 actions in knowledge creation, 20 actions in knowledge acquisition, 7 actions in knowledge organization and storage, 23 actions in knowledge sharing, and 20 actions in knowledge use and development. The crisis phase requires 12 actions in knowledge sharing and 10 actions in knowledge use. In the post-crisis stage, 5 actions in the knowledge sharing, 11 actions in the knowledge use, and 12 actions in the knowledge evaluation phases have been identified. To prevent and manage the flood crisis, knowledge improvement and management is an effective and necessary aspect that should be considered among the organizations and communities involved in emergency management.

Flooding poses severe ecological and environmental challenges, requiring comprehensive risk assessments to inform the development of sustainable management practices. This study proposes a framework that integrates Bayesian networks (BNs) and Geographic information systems (GIS) for flood disaster risk assessment, based on an evaluation indicator system consisting of three dimensions: hazard, vulnerability, and exposure. The proposed BNs-GIS model synergistically combines the probabilistic reasoning capabilities of BNs to capture interdependencies among risk factors with the spatial analysis power of GIS. The model incorporates diverse data sources, including hydrological models, remote sensing, infrastructure details, land use patterns, and ecological indicators, to generate a comprehensive risk profile. The framework was applied Yinchuan, a city in northwest China prone to flash flooding events. The model incorporated sensitivity analyses to quantify uncertainties stemming from input data and model parametrizations, to enable robust risk estimation. Our findings demonstrate the efficacy of the BNs-GIS approach in identifying areas with elevated flood risk and assessing the reliability of risk estimates. This integrated methodology contributes to ecological assessment by providing a probabilistic yet spatially-explicit evaluation of flood hazards. The study contributes to the development of indicator-based monitoring and assessment programs, supporting informed decision-making for sustainable management practices in flood-prone urban ecosystems. By providing a comprehensive understanding of flood risks, this approach can help policymakers and urban planners develop more effective and sustainable flood management strategies. While the model shows promise, limitations include its reliance on the quality and availability of input data, and the need for local expertise in model parameterization. Future work should focus on incorporating real-time data and dynamic updating capabilities to enhance the model’s applicability in rapidly changing urban environments.

Flood disasters pose serious threats to human life and property worldwide. Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts. This study proposes a comprehensive framework for integrating driving-factor optimization and interpretability, while considering spatial heterogeneity. In this framework, the Optimal Parameter-based Geographic Detector (OPGD), Recursive Feature Estimation (RFE), and Light Gradient Boosting Machine (LGBM) models were utilized to construct the OPGD–RFE–LGBM coupled model to identify the essential driving factors and simulate the spatial distribution of flood disasters. The SHapley Additive ExPlanation (SHAP) interpreter was employed to quantitatively explain the driving mechanisms behind the spatial distribution of flood disasters. Yunnan Province, a typical mountainous and plateau area in Southwest China, was selected to implement the proposed framework and conduct a case study. For this purpose, a flood disaster inventory of 7332 historical events was prepared, and 22 potential driving factors related to precipitation, surface environment, and human activity were initially selected. Results revealed that flood disasters in Yunnan Province exhibit high spatial heterogeneity, with geomorphic zoning accounting for 66.1% of the spatial variation in historical flood disasters. The OPGD–RFE–LGBM coupled model offers clear advantages over a single LGBM in identifying essential driving factors and quantitatively analyzing their impacts. Moreover, the simulation performance shows a slight improvement (a 6% average decrease in RMSE and an average increase of 1% in R2) even with reduced factor data. Factor explanatory analysis indicated that the combination of the essential driving factor sets varied across different subregions; nevertheless, precipitation-related factors, such as precipitation intensity index (SDII), wet days (R10MM), and 5-day maximum precipitation (RX5day), were the main driving factors controlling flood disasters. This study provides a quantitative analytical framework for the spatial drivers of flood disasters at large scales with significant heterogeneity, offering a reference for disaster management authorities in developing macro-strategies for disaster prevention.

Heavy rains and floods cause human, material, and economic damage in cities worldwide. The severity of flooding has intensified due to accelerating urbanization. While much of the existing research on flood hazards emphasizes simulation and assessment, the correlation between indicators has yet to be explored. This study employs the Tree Gaussian Process sensitivity analysis method. Through rigorous sampling and correlation analysis, the model identifies critical determinants. Significantly, factors such as the water supply penetration rate (Var3), water pipeline density in built-up areas (Var4), centralized treatment rate of sewage treatment plants (Var6), agricultural land for forestry (Var13), and urban, village, and industrial and mining land (Var15) stand out as primary influencers on the flood-affected populace. These variables reflect a city’s flood management capability and its dedication to resource stewardship and ecological equilibrium, underscoring its critical role in flood risk assessment and strategic mitigation. The study further illuminates that the interplay of these variables can exacerbate flood consequences, suggesting a compounded impact when variables operate in tandem. Recognizing these synergistic effects reveals a more pronounced flood threat than previously estimated, indicating that viewing these factors in silos might underrepresent the risk involved. [ABSTRACT FROM AUTHOR]

Older adults in disaster contexts are often thought of as a passive, vulnerable population that lacks agency and capacities to cope in the aftermath. However, it can be argued that older adults may have underrecognized strengths that can be utilized pre-, peri-, and post-disaster. One of these strengths is older adults' unique social capital that stems from long-standing connections with other members of their respective communities. Using data from in-depth, semistructured interviews with farmers in British Columbia 3–11 months after the 2021 floods, this research explored the experiences of older adult farmers' recovery. The farmers discussed how they leveraged their social capital to aid in their recovery efforts from the flood event. By using their bonding social capital, older adult farmers transformed their existing, deep-rooted connections into post-disaster assistance. This, in turn, generated the idea of the therapeutic community, helping community members cope in the aftermath. This research indicated the need to further examine how older adults in disaster settings can be viewed as assets with community knowledge and skills as opposed to solely as a vulnerable population. [ABSTRACT FROM AUTHOR]

Copyright of Japanese Journal of JSCE / Doboku Gakkai Ronbunshu is the property of Japan Society of Civil Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aimed to explore the differences in various aspects of community disaster resilience and how to enhance disaster resilience tailored to different community types. The evaluation results were validated using the flood event that occurred in Zhengzhou on 20 July 2021 (hereinafter referred to as the "7.20" rainstorm disaster). The main results of the analysis showed that the respondents' overall evaluation of their community's resilience to the "7.20" disaster was relatively high. Commercial housing communities performed the best, followed by urban village communities, and employee family housing communities performed the worst. Specifically, commercial housing communities scored highest in three dimensions: human capital, physical infrastructure, and adaptation. Urban village communities scored highest in the three dimensions of social capital, institutional capital, and community competence, while employee family housing communities consistently ranked the lowest in each dimension. The most significant disparities were found in human capital, followed by community competence and social capital, adaptation, and, lastly, institutional capital and physical infrastructure. Targeted improvement strategies and measures are suggested for each type of community, offering valuable recommendations for relevant government agencies aiming to enhance community disaster resilience and disaster risk reduction. [ABSTRACT FROM AUTHOR]

Floods are common and inevitable natural disasters. Achieve Sustainable Development Goal (SDG) 11.5 is a critical challenge for coastal cities, especially those in deltaic lowlands such as in the case of Guangzhou, China. Regarding the spatial planning and design of such urban regions, it is crucial to study the impacts of flooding in compact or decentralized spatial development pathways. This reinforces the understanding of the relationship between strategic decisions for spatial planning and flood mitigation. However, the lack of a computer model to assess spatial evolution paths is a significant limitation. The non-dominated Sorting Genetic Algorithm II (NSGA-II) explores the possibility of a compact built-up land layout in 2030. The results showed that, concerning the 2030 decentralized scenario, the 2030 compact scenario presents a large increase in the integrated fitness function value from 0.618 to 0.771 (the increase is equivalent to 0.153 or about 24.75%). In addition, different development scenarios were constructed by setting different target weights. Compared to the decentralized scenario results, the fitness function values of the optimization results of each scenario showed better results at different levels. They could also serve as a reference for other similar coastal areas to achieve SDG 11.5 by 2030. [ABSTRACT FROM AUTHOR]

Subway stations are mainly in the form of underground sections, threatened by flood disasters in the rainy season, and prone to rainwater backflow, which seriously affects the travel of residents and the safe operation of subways. In order to further improve the effect of flood disaster prediction in subway stations, this paper proposes a flood disaster prediction method for subway stations based on RF-RFE and DNN neural network. Firstly, by collecting and analyzing the cases of flood disasters in subway stations, the method of literature review combined with expert interviews is used to construct the initial variable set of flood disasters in subway stations. Secondly, the random forest-recursive feature elimination (RFRFE) algorithm is used for calculation. The importance of the initial variables and the sorting of the correct rate of variable classification are completed, and important variables are selected from the initial variable set. finally, a DNN neural network prediction model is established, and the selected important variables are used as input samples to train the DNN neural network to complete the flood disaster prediction in subway stations. The research results show that: (1) variable selection can improve the accuracy of the prediction model. Compared with the DNN neural network prediction model of the initial variable set, the accuracy of the DNN neural network prediction model after data screening is improved by 4. 36%; (2) The combination of RF-RFE and DNN neural network algorithm has a good effect, the prediction model accuracy rate is 88. 1%, and the F1 score is 0. 9. [ABSTRACT FROM AUTHOR]

The frequent occurrence of urban flood disasters is a major and persistent problem threatening the safety of cities in China and elsewhere in the world. As this issue is so pervasive, exploring new methods for more effective risk prevention and urban flood disaster control is now being prioritized. Taking the case of the city of Zhengzhou as an example, this paper proposes using geological, hydrogeological, ecological, and environmental conditions together with appropriate engineering designs to address the problem of urban flooding. The strategy includes integrating urban sponge–hydrogeological conditions, ecological engineering, and the construction of deep underground water storage facilities. Field investigations, data collection and analysis, in situ observations, testing, and laboratory experiments, are analyzed to explain the formation mechanism and means to mitigate flood disasters in Zhengzhou. Our results suggest that the appropriate use of geological, ecological, and hydrogeological aspects, combined with effective engineering practices, can significantly improve the city's flood control capacity. These measures can solve the problem of the "once-in-a-millennium" occurrence of torrential rain disasters such as the "720" torrential rainstorm that has affected the city of Zhengzhou. [ABSTRACT FROM AUTHOR]

With global warming, the risk assessment of extreme precipitation has become the focus of academic circles and governments of various countries. Therefore, in order to accurately analyze the trend and risk distribution of future flood disasters in the northern foothills of the Qinling Mountains under the global warming scenario, based on CMIP6 rainfall data, geographical data and socio-economic data, the game combination weighting method, entropy weight method and analytic hierarchy process were used to build a flood disaster risk assessment model. Flood disaster risk was assessed for two scenarios (SSP245 and SSP585) and two periods (2040-2060 and 2070-2090). The results show that: a) the high-hazard areas are mainly distributed in the Lantian area and low-lying mountain areas. With the change of years and different development scenarios, the risk has a significantly increasing trend. b) The high vulnerability areas are mainly distributed in Zhouzhi County, Huyi District, northern Chang’an District and mountain road areas. c) The areas with a high risk of extreme rainfall and flood disaster are mainly concentrated in Lantian County and Lintong District. The risk of flood induced disasters increases over time in different scenarios. [ABSTRACT FROM AUTHOR]

Flood disaster is one of the most serious natural disasters. The poor drainage system, related data and technology limitations, and high vulnerability of townships lead to the urgent need for technical support in townships. Spatial information technology is mainly used in flood disaster research with passive remote sensing and GIS. However, passive remote sensing data is difficult to obtain and is mainly used for post-disaster assessment. In this paper, we propose a GIS-based method for township-level flooding and inundation risk analysis to serve predisaster assessment of flooding. The Hedian Town, Suizhou City, Hubei Province is taken as the study area, and the open source data information is applied to analyze the rainfall and waterlogging processes in different recurrence periods based on historical rainfall, soil and terrain characteristics, classify the flooding risk level, and provide evacuation suggestions. The results show that the research results are basically consistent with the field research and can reflect the flooding risk in the study area more accurately. The method of this paper can provide effective data and technical support for scientific decision-making on flood disaster at the township level. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Pliocene to Holocene Kashmir basin of NW Himalaya, India, is prone to various geological and climatic hazards routinely connected to the climatic and structural setting of the Himalayan intermontane basins. The topographic expression of the basin is a direct consequence of the active plate tectonic convergence between the lithosphere plates of India and Eurasia. However, the role of the tectonic framework and its contribution to flood hazards has remained an unresolved research question. Our previous work has contributed towards this problem, and here, we extend our previous work by producing robust evidence in support of the role of active faults and tectonic topography in shaping the flood hazards in the Kashmir basin and its implication for the other similar basins in the world. The Landsat 8 Operational Land Imager-Thermal Infrared Sensor (OLI-TIRS) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images were used to extract pre-2014 floodwater bodies and 2014 flooded areas in the Jhelum River valley of the Kashmir region, respectively. Water pixels were extracted using the modified normalized difference water index (MNDWI) methods, and similarly, the flood depth and duration (in days) were computed. The flood depth was correlated with the Jhelum River valley's topography, morphology, and geology. The slope and topography were also associated with the 2014 flood disaster to map the influence of the pre-disposed structural setting of the basin on flood vulnerability. The results suggest that flood disasters were mainly related to the unplanned built area, which remains the dominant factor in how flood hazards have turned into disasters. The results reveal the tectonically derived structural configuration of the basin mainly contributed towards the flood hazards, which is the primary predisposed structural framework to dictate flood hazards since the formation of the basin ∼4.0 million years ago. Therefore, the primary outcome of our work is the strong evidence that flood hazards are related to the structural setting of the basin, which includes topography and geology, while the transition of flood hazards into disasters is mainly because of unplanned urbanization.

The frequency of global floods has increased, posing significant threats to economic development and human safety. Existing flood risk zoning studies in disaster prevention lack integration of the natural–economic–social chain and urban resilience factors. This study addresses this gap by constructing flood disaster risk and intensity indices using data from 31 provinces and 295 prefectural-level cities in China from 2011 to 2022. These indices incorporate natural (rainfall), economic (GDP), and social (population, built-up area) indicators to assess the flood likelihood and loss degree, providing comprehensive risk and intensity ratings. The study also examines the impact of resilience factors—environmental (green space), infrastructural (rainwater pipeline density), and natural resource (watershed areas)—on flood intensity. Findings reveal that high-risk regions are mainly in the Yangtze River Basin and southern regions, while high-intensity regions are primarily in the middle and lower Yangtze River and certain northwestern cities. Increasing rainwater pipeline density mitigates flood impacts in high-risk, high-intensity areas, while expanding green spaces and pipelines are effective in high-risk, low-intensity regions. This paper proposes a comprehensive flood hazard zoning mechanism integrating natural, economic, and social factors with urban resilience, offering insights and a scientific basis for urban flood management.

The planting structure and spatial distribution of crops are the basis for the estimation of crop losses in flood disasters. In order to meet the accuracy requirements for rapid assessment of crop losses in flood disasters, it is urgent to build a real-time crop survey and disaster assessment method that combines remote sensing technology with ground surveys. Taking the rainstorm and flood disaster in Henan Province in 2021 as an example, this paper comprehensively used satellite remote sensing data and real ground object sample data obtained from field research to carry out a research on the fine classification of crops in Weihui City, Xinxiang, Henan Province, combined with the flood inundation duration calculation results based on remote sensing data, to assess the damage of crops in this flood disaster event in Weihui City. The results show that: a) The main types of flood disasters in Weihui City are high water level intrusion floods in rivers and waterlogging and stagnation in low-lying areas. b) The types of disaster-affected features are corn, the main crops, green forests around roads, and urban areas of Weihui City. The disaster assessment method for flood crops based on multi-source remote sensing data and real ground object samples proposed in this paper has high accuracy and timeliness. [ABSTRACT FROM AUTHOR]

Flooding is one of the world's most devastating natural disasters, and the effects of global climate change further intensify its impact. In the context of flood management, urban resilience emerges as a promising perspective. While existing urban resilience assessment systems predominantly encompass economic, social, ecological, and infrastructural factors, they often neglect crucial dimensions like social equity and human awareness. We aimed to assess urban flood resilience considering social equity and human awareness. We have developed an indicator system called the 3-Dimentional Disaster Urban Flood Resilience Index System (3D-UFRIS) to address the issue. We also introduced social media data to explore the use of big data in urban flood resilience assessment. Scrapy was used to collect data and AHP-EWM was used to calculate the results. Our findings reveal a layered distribution of urban flood resilience of Zhengzhou, categorized into five levels: highest, higher, medium, lower, and lowest resilience. Notably, the highest resilience areas, covering a mere 3.06% of the total area, were primarily situated in the Jinshui district, characterized by strong economic activity, high public awareness, and a history of waterlogging incidents. Conversely, the lowest resilience areas, encompassing the largest portion at 36%, were identified in Zhongmou County, Xinzheng, and Shangjie District, marked by lower public awareness and limited medical accessibility. This study presents a pioneering approach to comprehending urban disaster resilience, offering valuable insights into mitigating flood-related risks and enhancing urban planning strategies. [ABSTRACT FROM AUTHOR]

Copyright of South Architecture / Nanfang Jianzhu is the property of South Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Flooding is one of the most common natural hazards, causing disastrous impacts in Nigeria. Based on a systematic review of previous research work, this study highlights the causes of flood disaster and its effective mitigation strategies in Ilaro formation, Dahomey Basin southwestern Nigeria. The findings revealed that in most cases in Nigeria, flood disaster is caused by natural and human activities such as excessive rainfall, poor drainage facilities, poor waste disposal practices and weak policy implementation by institutions concerned with flood management. The study further revealed that increasing deforestation, and inability of land developers to adhere to physical development plans and schemes also caused flood disaster in the study area. The study concludes that flood disaster in Ilaro Formation affect physical developments such as houses, roads and other structures. In cases of serious flooding, lives can be lost in the process. This study recommends more community-based approaches to help raise environmental awareness and support the affected community residents through grants from government and donor agencies as this would enhance their coping strategies for curbing the problems and indeed remediating the flood disaster in the area. It was also suggested that land development control activities should be taken seriously to avoid the erection of developments on flood plains and flood prone areas. [ABSTRACT FROM AUTHOR]

The community has a strategy facing floods. This research aims to analyze (1) the community's mitigation, (2) the community's action during the flood, (3) the community's post-flood action, (4) the community's effort to avoid flood's effect. This research's approach is explanatory-sequential design. Data collection used questionnaires, in-depth interviews, observations, literature studies. Quantitative data analysis used descriptive statistics and qualitative data analysis used a 3-step technique. The results are (1) community mitigation: cleaning the lake and the river from waste, preparing the boat, preparing the empty land for the animal, and making higher the house. (2) community actions during flooding: picking up things to the place under the roof, building the higher place into the home, building the bamboo bridge, saving the vehicle in other families, saving the animal in the safety place (3) actions after flooding: cleaning mud post-flood, improving the broken part of the home, putting back things post-flood, broken the bamboo bridge. (4) efforts to avoid impact of flooding, having housing provided by the government and utilizing bridges. The conclusion that all community actions to deal with floods are collectivity for the community. [ABSTRACT FROM AUTHOR]

Aim: This study investigated anxiety, depression, and stress and their influencing factors among nurses during the COVID‐19 pandemic and after the flood in Henan Province, China. It aimed to provide the theoretical foundation for the management of relevant hospital departments, improvement of nursing quality, implementation of antiepidemic work, and other relevant studies. Background: In December 2019, COVID‐19 was reported in Wuhan, China and became a global pandemic. In July 2021, unprecedented flooding occurred in Henan Province, China. Under the dual pressure of COVID‐19 and the flood, nurses' mental health problems deteriorated. Methods: In August 2021, 1229 nurses from various departments of the First Affiliated Hospital of Xinxiang Medical University in Xinxiang City, Henan Province,China were invited to participate in a questionnaire survey using a general condition questionnaire and the Chinese version of the Depression–Anxiety–Stress Scale. Descriptive statistics were used to analyze the status of nurses' anxiety, depression, and stress. Analysis of variance, t‐test, and multiple linear regression models were used to analyze the factors influencing anxiety, depression, and stress among nurses. Results: Of the participants, 36.1%, 10.1%, and 15.5% had moderate to high levels of anxiety, stress, and depression, respectively. Moreover, 42 (3.4%) participants experienced high to severe levels of stress, anxiety, and depression. The scores showed significant differences based on gender, harmonious family relationships, department position, work intensity, sleep quality, physical exercise, participation in leisure activities, health status, involvement in emotion management‐related training, and attending self‐care‐related training (all P < 0.05). Gender, work intensity, harmonious family relationships, health condition, sleep quality, and participation in leisure activities influenced stress, anxiety, and depression scores. Department position influenced anxiety and stress scores, and average monthly earnings influenced anxiety scores. Conclusion: All the nurses experienced various levels of stress, anxiety, and depression. Related departments should pay special attention to male nurses and nurses with high work intensity, unharmonious family relationships, poor health, and sleep quality, and who engage in fewer leisure activities. Implications for nursing and health policy: It is recommended that departments allocate human resources and arrange schedules reasonably, encourage nurses to participate in more recreational activities, and implement emotion management and self‐care‐related training to relieve emotional distress, and ultimately maintain nurses' mental health. [ABSTRACT FROM AUTHOR]

Objectives: In the process of flood disaster, the observed data is multi-source heterogeneous (remote sensing image, social media text, geographic information data, etc.), which makes it difficult to use complementary advantages to fuse and apply to risk assessment and provide decision-making knowledge. We study the construction method of flood disaster knowledge graph based on multi-modal data. Remote sensing images and social media text knowledge are integrated and extracted to form multi-modal flood disaster knowledge graph. Methods: Based on the top-down method, the domain concept is subdivided and the flood disaster domain ontology layer is constructed. Remote sensing images are intelligently interpreted by deep residual full convolutional neural network, and the image interpretation information is converted into text by geographic inverse coding to realize transformation from image information to text knowledge. Knowledge is extracted from social media textual data based on naming entity recognition and relationship extraction technique. By training word vectors, the semantic similarity is used to calculate the correlation between text knowledge and image knowledge, so as to achieve the unified expression of multi-modal data knowledge. Results: Taking the flood disaster in Hubei Province,China as an example, the multi-source heterogeneous data are efficiently transformed into knowledge and correlated to form domain knowledge graph, which realizes the transformation from multi-source heterogeneous data to multi-modal knowledge, provides corresponding emergency measures in different periods of disasters, and realizes flood disaster assessment in Hubei Province by associating agricultural disaster area, crop type and crop value. Conclusions: This method combines deep remote sensing interpretation, text knowledge extraction and semantic similarity calculation to realize transformation from multi-source heterogeneous data to multi-modal knowledge. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An extensive number of farmlands in the Poyang Lake region of China have been submerged due to the impact of flood disasters, resulting in significant agricultural economic losses. Therefore, it is of great importance to conduct the long-term temporal monitoring of flood-induced water body changes using remote sensing technology. However, the scarcity of optical images and the complex, fragmented terrain are pressing issues in the current water body extraction efforts in southern hilly regions, particularly due to difficulties in distinguishing shadows from numerous mountain and water bodies. For this purpose, this study employs Sentinel-1 synthetic aperture radar (SAR) data, complemented by water indices and terrain features, to conduct research in the Poyang Lake area. The results indicate that the proposed multi-source data water extraction method based on microwave remote sensing data can quickly and accurately extract a large range of water bodies and realize long-time monitoring, thus proving a new technical means for the accurate extraction of floodwater bodies in the Poyang Lake region. Moreover, the comparison of several methods reveals that CAU-Net, which utilizes multi-band imagery as the input and incorporates a channel attention mechanism, demonstrated the best extraction performance, achieving an impressive overall accuracy of 98.71%. This represents a 0.12% improvement compared to the original U-Net model. Moreover, compared to the thresholding, decision tree, and random forest methods, CAU-Net exhibited a significant enhancement in extracting flood-induced water bodies, making it more suitable for floodwater extraction in the hilly Poyang Lake region. During this flood monitoring period, the water extent in the Poyang Lake area rapidly expanded and subsequently declined gradually. The peak water area reached 4080 km2 at the height of the disaster. The severely affected areas were primarily concentrated in Yongxiu County, Poyang County, Xinjian District, and Yugan County. [ABSTRACT FROM AUTHOR]

Flood is one of the conditions when overflowing water inundated the lower areas. The occurrence of floods always has both positive and negative impacts. If the flood occurs in residential areas, it will have a more negative impact. Therefore, people should have preparedness to prevent the effects of the flood. In 2022, a flood occurred in the Jampea Islands, Selayar Islands regency, and affected 90% of the population. It was caused by the high rainfall intensity that resulted in the higher river discharge and overflowing into settlements. It was unclear how much loss was caused by the flood, but this phenomenon has traumatized the community. This island has been occupied for hundreds of years. However, it has never been a flood that the water inundated 90% of the island's land. Since the flood is possible to occur when the high rainfall intensity happens. Hence, this research was conducted to find out how is community preparedness in disaster mitigation. The community was focused on students at the secondary school level (SMA and SMK). Students were selected by considering their readiness to be more proactive in disaster mitigation through learning practices and socialization in the family environment. The data were collected through an online questionnaire and observation, then analyzed with a diagram presentation system. The results found that the aspect of students' knowledge was still classified as low. While the institutional aspect is still relatively moderate. Therefore, the capacity of students still needs to be strengthened to prevent the magnitude of the risk of flooding. [ABSTRACT FROM AUTHOR]

Floods are a sudden and influential natural disaster, and synthetic aperture radar (SAR) can image the Earth's surface almost independently of time and weather conditions, making it particularly suitable for extracting flood ranges in time. Platforms such as Google Earth Engine (GEE) can provide a large amount of SAR data and preprocess it, providing powerful assistance for real-time flood monitoring and time series analysis. However, the application of long-term series data combined with recurrent neural networks (RNNs) to monitor floods has been lacking in current research, and the accuracy of flood extraction in open water surfaces remains unsatisfactory. In this study, we proposed a new method of near real-time flood monitoring with a higher accuracy. The method utilizes SAR image time series to establish a gated recurrent unit (GRU) neural network model. This model was used to predict normal flood-free surface conditions. Flood extraction was achieved by comparing and analyzing the actual flood surface conditions with the predicted conditions, using a parameter called Scores. Our method demonstrated significant improvements in accuracy compared to existing algorithms like the OTSU algorithm, Sentinel-1 Dual Polarized Water Index (SDWI) algorithm, and Z-score algorithm. The overall accuracy of our method was 99.20%, which outperformed the Copernicus Emergency Management Service (EMS) map. Importantly, our method exhibited high stability as it allowed for fluctuation within the normal range, enabling the extraction of the complete flood range, especially in open water surfaces. The stability of our method makes it suitable for the flood monitoring of future open-access SAR data, including data from future Sentinel-1 missions. [ABSTRACT FROM AUTHOR]

Accurate forecasts and estimates of disaster debris are critical for effective debris management planning. However, detailed postdisaster waste data to validate and improve debris predictions is often unavailable. In this study, a postdisaster waste dataset collected in Beaumont, Texas, following widespread flooding from Hurricane Harvey that included debris tonnages and coordinate locations of each debris removal in residential areas was investigated. The dataset was utilized to quantify the amount of debris produced, identify the factors that influenced debris generation in different areas of the city, and compare Beaumont debris tonnages to predictive models. The study found that the type of flooding (riverine versus urban) had the highest influence on debris generation. Riverine flooded areas generated twice the debris tonnage as urban flooded areas. Elevation appeared to influence debris generation when considered with the type of flooding. FEMA's correlation for flooded personal property was within the same magnitude of debris quantities, while other methods significantly overpredicted debris quantities (up to one order of magnitude) due to their generality. However, they can be adapted for flood-generated debris. Urban flooding is an increasingly prevalent issue following a natural hazard and generates considerable amounts of debris, but is not addressed in current disaster management plans. Estimation methods that consider urban flooding should be developed. [ABSTRACT FROM AUTHOR]

This study addresses the critical problem of understanding the changing dynamics of glacier meltwater in Lake Mertzbakher, a challenge heightened by ongoing global climate change. Employing the innovative method of the Google Earth Engine (GEE) platform, this research meticulously extracted surface water data at 60 time points during the years 2000, 2005, 2010, 2015, and 2021. This approach represents a significant advancement over previous methods by offering more frequent and precise data analysis. We incorporated meteorological factors such as temperature and precipitation to assess their influence on the monthly changes in the glacier lake area. Our findings indicate a pronounced outburst in July, leading to a substantial decrease in the lake’s area, which reaches its lowest in September. Through detailed partial regression analysis, we established a hierarchy of meteorological influences on the lake’s area, identifying minimum temperature (r = 0.245), mean temperature (r = −0.239), precipitation (r = 0.228), radiation (r = 0.154), and maximum temperature (r = 0.128) as key factors. Additionally, our use of a structural equation model unveiled the most impactful elements, with mean temperature (r = −3.320), minimum temperature (r = 2.870), radiation (r = 0.480), and precipitation (r = 0.470) leading the effects. These insights mark a substantial contribution to our understanding of glacier lake dynamics, offering crucial data for predicting and managing glacier lake floods. This study’s novel methodology and comprehensive analysis underscore its significance in enhancing disaster prevention and preparedness strategies amidst the challenges of global climate change.

Disaster is an event or series of events that threaten and disrupt community life. Flood disasters result in loss of life, environmental damage, and financial losses. Flood impacts affect the flood distribution locations and the socio-economic conditions of households in the post-flood period. The occurrence of flood-related damage experienced by households results in expenses for repairs and preventing further damage from future floods. These expenses constitute adaptation costs incurred by households and are influenced by various factors, leading to variations in the amount spent by each household. In general, this research aims to: 1) Identify household perceptions of seasonal floods in Barito Kuala Regency; 2) Identify the factors influencing adaptation costs in facing seasonal floods in Barito Kuala Regency. Based on the research outcomes, the adaptation strategies employed by the community in the Jejangkit District consist of active and passive adaptations. Active adaptation includes changing occupations and securing belongings, while passive adaptation involves knowledge of the seasonal calendar and reducing household expenses. Economic adaptation includes raising grain storage, working as laborers or motorcycle taxi drivers, and requesting money transfers from family members. Social and cultural adaptation involves solidarity systems in the form of providing food aid and assisting in evacuations through communal efforts.

Palembang is a city that is frequently hit by floods, as the center of national activities which stimulates infrastructure development activities to support the economy and population growth, resulting in reduced urban green space and even the disappearance of the rivers that used to flow in most of Palembang City. Floods also occurred quite badly in the study area, namely the Lambidaro Sub-watershed, this study aims to identify the dominant factors that affect flooding and determine the area distribution based on the level of flooding vulnerability of study area. The methods used are Analytical Hierarchy Process analysis combined with Geographic Information System analysis using primary and secondary data from valid sources. Based on the results of the study, it was found that most of the study areas were in areas with a moderate level of flood prone with an area of 3146.50 hectares, then areas with a high level of flooding reached 1429.34 hectares, and finally, areas with a low level of flooding were only 347.65 hectares. The conclusion related to this research requires more attention from related stakeholders to areas that are included in the high flood-prone level, which mostly hit the Bukit Baru Village, Tanjung Barangan Street.