Flood Emergency Management Research Articles

A Coupled Human and Natural Systems (CHANS) framework integrated with reinforcement learning for urban flood mitigation

To address the challenge of escalating urban flood risk and the deficiency in effective flood emergency management, this study introduces a novel Coupled Human and Natural Systems (CHANS) modelling framework that employs hierarchical reinforcement learning to optimise mobile pump scheduling and placement for urban flood risk mitigation. The CHANS framework integrates hydrodynamic and agent-based models within a multi-GPU computing environment for high-resolution, real-time flood inundation modelling and risk assessment to enrich Reinforcement Learning (RL) training. In the application to Ninh Kieu District in Can Tho City, Vietnam, the new RL-enabled modelling framework is used to evaluate optimal mobile pumping strategies for concurrent pluvial flooding and post-flooding events against the traditional deployment approaches. Results demonstrate that RL-based strategies can significantly enhance flood risk reduction, outperforming traditional methods by achieving 2× and 4× improvements in the concurrent and post-flooding periods/events, respectively. Incorporating human factors and adapting to local conditions, the RL agent provides valuable insights into mobile pump scheduling and deployment strategies. Sensitivity analysis confirms the robustness of the CHANS modelling framework and underscores the role of RL in optimising mobile pump scheduling and placement where traditional rule-based strategies are challenging.

Runoff prediction based on the IGWOLSTM model: Achieving accurate flood forecasting and emergency management

With the acceleration of global climate change and urbanization, the frequency and impact of flood disasters are increasing year by year, making flood emergency management increasingly crucial for safeguarding people’s lives, property, and societal stability. To enhance the accuracy of river flow prediction, this study employs an Improved Gray Wolf Optimization Algorithm (IGWO) to optimize parameters of the Long Short-Term Memory Network (LSTM) model. Experimental results demonstrate that the proposed algorithm significantly improves the accuracy of river flow prediction, achieving higher precision and better generalization compared to traditional machine learning algorithms. This method provides more reliable data support for flood warning systems, aiding in the accurate prediction of flood occurrence timing and intensity, thereby providing scientific basis for flood prevention and mitigation efforts. Moreover, this approach supports hydro-logical research, enhancing understanding of river water cycle processes and ecosystem changes.

A two-way coupled CHANS model for flood emergency management, with a focus on temporary flood defences

This study presents a novel Coupled Human And Natural Systems (CHANS) modelling framework that integrates a hydrodynamic model with an agent-based model at the memory level within a multi-GPU computing environment. This two-way coupled model captures real-time interactions between human activities and flood dynamics, with a focus on the deployment of temporary flood defences during the 2015 Desmond flood in Carlisle, UK. The findings reveal that temporary defences can significantly reduce flood inundation by 30% with early warnings and 15% through real-time decision-making, leading to financial savings of £30 million and £15 million, respectively. The study further explores the decision-making process for effective emergency flood management, emphasising the importance of early warnings and resources optimisation. The new CHANS model provides a valuable tool for testing and optimising emergency flood management strategies, highlighting the necessity of directly incorporating human activities into flood risk management.

Assessing resilience through social networks: A case study of flood disaster management in China

The study of resilience has gained significant traction in the field of disaster risk reduction (DRR) due to the escalating frequency and intensity of extreme weather events such as floods. Social capital accumulated within social networks comprising stakeholders serves as a crucial resource for the establishment of disaster resilience. Therefore, this study examines and evaluates resilience to changes within DRR process through a social capital perspective. First, it embraces the conceptual framework of disaster resilience through social capital and elucidates six distinct forms of social capital relevant to enhancing disaster resilience. Subsequently, the governance structure of flood emergency management in China is thoroughly examined and a disaster resilience assessment system is established from the standpoint of social capital using a social network analysis methodology. Finally, using the case of the devastating flooding triggered by the 7–20 rainstorm in Henan, China, resilience levels are measured at various stages of the flooding employing 4R theory. The results of this study will aid governmental and non-governmental organizations in enhancing their resilience in the face of natural disasters.

Research on nowcasting prediction technology for flooding scenarios based on data-driven and real-time monitoring.

With the impact of global climate change and the urbanization process, the risk of urban flooding has increased rapidly, especially in developing countries. Real-time monitoring and prediction of flooding extent and drainage system are the foundation of effective urban flood emergency management. Therefore, this paper presents a rapid nowcasting prediction method of urban flooding based on data-driven and real-time monitoring. The proposed method firstly adopts a small number of monitoring points to deduce the urban global real-time water level based on a machine learning algorithm. Then, a data-driven method is developed to achieve dynamic urban flooding nowcasting prediction with real-time monitoring data and high-accuracy precipitation prediction. The results show that the average MAE and RMSE of the urban flooding and conduit system in the deduction method for water level are 0.101 and 0.144, 0.124 and 0.162, respectively, while the flooding depth deduction is more stable compared to the conduit system by probabilistic statistical analysis. Moreover, the urban flooding nowcasting method can accurately predict the flooding depth, and the R2 are as high as 0.973 and 0.962 of testing. The urban flooding nowcasting prediction method provides technical support for emergency flood risk management.

Integrated Decision Support System for Flood Disaster Management with Sustainable Implementation

Emergency management of a flood catastrophe should involve not only an immediate reaction to the flooding but also the pre-flood disaster phase, the flood disaster phase, and the post-flood disaster phase. This is in addition to the immediate response to the flooding. This research investigates whether or not a decision support system (DSS) for flood emergency management is necessary and whether or not it is feasible. Previously, we presented a model for the development of modular disaster management decision support systems. The prior work that we did on the integration of DSS model decomposition-derived modular subroutines is expanded upon in this research. The act of merging many models into a single comprehensive logical model is referred to as “Observed and Forecasted.” After that, it offers a systematic framework for handling crises throughout the whole of the process, and it concludes with an in-depth analysis of the function architecture of each application subsystem. In this work, we create a disaster management metamodel with the goal of eventually building a disaster management (DM) language. It will function as a representational layer of DM data that, over time, will create a system for DM decision support that mixes and matches various DM actions in line with the evolution of the disaster as it develops. A decision support system that will contain an innovative metamodel as a core component will be developed in order to accomplish the aims of integrating, enabling, and accelerating access to DM information. These objectives will be reached via the creation of a decision support system.

Real-time peak flow prediction based on signal matching

Real-time peak flow prediction under heavy precipitation is critically important for flood emergency evacuation planning and management. In the case of emergency evacuation, every second matters as a slightly longer lead time could save more lives and reduce the associated social, economic, and health impacts. Here, we present a model (named SIGMA) based on the principle of signal matching to facilitate real-time peak flow prediction at sub-hourly scales (e.g., minutes to seconds). The SIGMA model divides the target watershed into small zones and the heavy precipitation falling into each zone is collected into a small water tank. As the water tank moves downstream and arrives in the watershed outlet, it will discharge the collected precipitation and generate a small single-pulse streamflow signal. By combining all small signals coming from all zones within the watershed, we will be able to generate a synthesized peak flow signal. The proposed model is applied to simulate the peak flow events observed in a real-world watershed to verify its effectiveness in real-time flood prediction. The results suggest that the presented model can reasonably predict three key aspects of a peak flow event, including the peak flow rate, the arrival time of peak flow, and the duration of the peak flow event. The proposed model is demonstrated to be effective in real-time flood prediction and can be used to support flood emergency evacuation planning and management.

Flood emergency management in a municipality in the Czech Republic: A study of local strategies and leadership

After the fall of Communism in the Czech Republic, the centralised flood management was entrusted to a municipality. The new approach to disaster management in 1997 emphasised on emergency preparedness at the local level. The changing paradigms of emergency management and the changing rules and regulations of insurance companies have increased the responsibility of the local government to reduce the loss of property and save lives in villages. A qualitative method was used to explore a phenomenon in a bounded system and to understand the role and responsibilities of the municipality and the villagers' expectations. In addition, policy papers were examined to help understand the emergency planning of the municipality. The study found that successful emergency leadership is associated with adequate planning, appropriate strategies and effective implementation. The leaders must encourage subordinates and other individuals to participate actively in emergency management. It has been observed that the mayor created a ‘sense of urgency’ after assessing the prevailing institutional situation so that the plans to manage the emergency may be implemented immediately. The municipality also improves its disaster management strategies by identifying the causes of failure in the past, rectifying existing gaps, building confidence among villagers and preventing migration. It is not expected that all the stakeholders, staffs and elected members at the grassroots level engage in emergency management activities equally. However, leadership of a mayor can bind all the stakeholders to achieve a successful emergency management. They may not have adequate emergency management knowledge, but the sharing of knowledge through workshops and training programmes enhance their skills. The effective emergency management at the grassroots level not only requires collaborative strategies and human resource management, but also needs adequate management of funds. Because the local government can not always depend on voluntary participation and contribution, in which the mayor plays an important role.

The rain deluge and flash floods of summer 2022 in the United Arab Emirates: Causes, analysis and perspectives on flood-risk reduction

Across the hyperarid Arabian Peninsula, preparing to face evolving risks of flash-flood hazards against projected scenarios for climate change requires both an understanding of the extreme rainfall events responsible and the implementation of appropriate mitigation/adaptation measures. In July 2022, torrential storms affected northeastern UAE. The damaging impacts prompted a broader consideration of flash-flood risks, especially for rapidly-urbanising arid landscapes. Occurring unexpectedly in mid-summer when rainfall is normally absent, the storms were produced by a combination of climatic and oceanic conditions: ITCZ position, unusual westward extension of a humid air mass from the Indian monsoonal trough, a cool sea-surface temperature anomaly that drove strong onshore moisture advection, and orographic precipitation enhancement by the coastal ranges. A foreign media suggestion that cloud seeding was partly responsible, however, is contested. Although flood emergency management was coordinated and effective, it is argued that various strategies may be strengthened to reduce future disaster risks. These include increased monitoring of mountain wadi systems to augment meteorological forecasts with hydrological information, and improved modelling to predict runoff-generation processes better in ungauged catchments. Greater capture of floodwaters to infiltrate and replenish groundwater aquifers also supports ongoing national efforts to develop sustainable solutions for addressing freshwater scarcity.

Analysis and countermeasures of the “7.20” flood in Zhengzhou

ABSTRACT The “7.20” extreme rainstorm in Zhengzhou caused a serious urban flood. The reasons are as follows: In addition to being affected by global climate change, the rapid urbanization of Zhengzhou in the last 40 years has caused tremendous changes in the city’s water system, underlying surface, urban topography, vertical system, urban underground facilities, all of which will directly or indirectly become factors of urban floods. Then, in response to the increasing frequency of urban floods, it is proposed to develop an overall flood resilience strategy led by the urban stream network; to establish a “large sponge city system of watersheds” and a resilient city’s disaster prevention concept; and to improve the urban flood emergency management system. This is done to adjust measures to local conditions and collaborate with various departments to improve the disaster response capacity of urban floods.

Deep learning enables super-resolution hydrodynamic flooding process modeling under spatiotemporally varying rainstorms

Real-time information on flooding extent, severity, and duration is necessary for effective metropolitan flood emergency management. Existing pluvial flood analysis methods are unable to simulate real-time regional flooding processes under spatiotemporally varying rainstorms. This paper presents a deep learning-enabled super-resolution hydrodynamic flood analysis method to simulate the real-time pluvial flooding process over a large area under spatiotemporally varying rainstorms. Compared with existing flood downscaling techniques, which are limited to flow depth, the proposed method produces high-resolution flow depth and velocity predictions, providing more comprehensive information for flood emergency management. The proposed method adopts a coarse-grid hydrodynamic model to generate a low-resolution flood map time series, which is subsequently converted to high-resolution flood maps by a deep learning model. The deep learning model can be trained using a limited number of assumed rainfall scenarios, which greatly reduces data preparation effort. The proposed method is applied to a complex terrain of 352 km2 in Hong Kong that covers both mountainous and urban areas. Results show that the proposed method simulates the spatiotemporal variations of flood depth and velocity with root mean square errors as low as 0.082 m and 0.088 m/s, respectively, and correlation coefficients of 0.962 and 0.921, respectively. The computation time for a 48-h rainfall event in the study area is less than 30 s, which is 2690 times faster than the direct fine-grid hydrodynamic analysis. The deep learning-enabled super-resolution hydrodynamic flood analysis method provides a promising computational tool for emergency flood risk management.

Automatic detection of actual water depth of urban floods from social media images

Urban flooding disasters are the most frequent natural disasters in cities, which broadly affects people's livelihood. Prompt emergency response is the key to alleviating such impact, however, that would rely on the timely mapping of flooding according to waterlogging severity. Compared with traditional approaches, crowdsourcing images from social media has emerged as a more efficient way to obtain this type of information. Therefore, this paper aims to explore the approach based on computer vision technology to automatically extract water depth from social media images, which can be utilized to construct flooding maps during urban flooding. Firstly, the images related to urban flooding were retrieved from social media and then filtered, leaving only images containing people. Secondly, a specific objective detection model based on YOLO was trained to detect the human body parts which are divided into crus, thigh, shoulder, and head from bottom to top. The experiment shows that the mAP of the trained human body parts detection model in the test dataset is 0.967. Afterward, an algorithm for extracting water depth was proposed based on the ratio of bounding boxes of detected body parts in images to the actual length of human body parts. Next, these models were verified by comparing them with manually estimated depth range and manually measured depth. The experiment shows that the accuracy of water depth range is 0.959 and the mean absolute error of the actual depth detection of water is 10.22 cm in the measurement dataset. Finally, the proposed models were applied to map the 2016 flooding that occurred in Wuhan as an illustrative case. This approach is helpful to broaden the source of flooding severity information and improve the efficiency of flood mapping in urban flood emergency management.

Collation of best practices for preparedness: lessons from disasters in Pakistan and Japan

PurposeThis study aims to compare the humanitarian supply chains and logistics of two countries in earthquake preparedness by modifying and using a previously established preparedness evaluation framework.Design/methodology/approachA European flood emergency management system (FEMS) is a seven-dimensional framework to assess a country’s preparedness for flood emergencies. The FEMS framework was modified to apply to earthquakes. Leveraging a multiple explanatory case study approach with data analysis, the authors reconstructed the events of the earthquakes in Pakistan (2005) and Japan (2011) with an applied grading (1–5). Findings were evaluated within the adopted FEMS framework. From a practitioner’s perspective, the framework is applicable and can accelerate support in the field.FindingsPakistan lacked emergency plans before the 2005 earthquake. In contrast, Japan possessed emergency plans before the disaster, helping minimise casualties. Overall, Japan demonstrated considerably better emergency management effectiveness. However, both countries significantly lacked the distribution of responsibilities among actors.Originality/valuePractical factors in the humanitarian supply chain are well understood. However, synthesising individual factors into a comprehensive framework is difficult, which the study solves by applying and adopting the FEMS framework to earthquakes. The developed framework allows practitioners a structured baseline for prioritising measures in the field. Furthermore, this study exemplifies the usefulness of cross-hazard research within emergency management and preparedness in a real-world scenario.

Effect of urban neighbourhood layout on the flood intrusion rate of residential buildings and associated risk for pedestrians

Urban neighbourhood layouts affect the flood intrusion rate towards residential buildings through various kinds of damaged openings (doors, windows, gates and shafts) and the associated risk for pedestrians within the urban building complex and ultimately the safety of indoor and outdoor pedestrians in urban flooding events. This study adopts computational fluid dynamics (CFD) to quantify the flood intrusion discharge towards residential buildings and evaluate the flood hazard rating of pedestrians within urban areas of divergent configurations in conjunction with a laboratory flume experiment regarding the stability/instability behaviour of a dummy model that represents a human body in a quasi-natural state. Five types of urban building layouts with three different building densities under three various floodwater conditions, including a determinant type A (see the graphic abstract hereafter), a point group type B, an enclosed type C, a hybrid type D and a new hybrid type E, are examined. The results show that the determinant type A and hybrid type D have a similar trend in terms of the flood intrusion discharge distributions towards each row of buildings and discharge variation with respect to urban building spacing, whereas other urban layouts exhibit different characteristics. Strong floodwater leads to an increased flood risk for pedestrians both outdoors and even indoors within various urban configurations: pedestrians are the most prone to toppling instability on the streets, in turn, behind the building and within the interior of the building. For both determinant type A and hybrid type D, the more pedestrians lie behind the building approaching the upstream, the safer the pedestrians become, while for point group type B, enclosed type C and new hybrid type E, the trend is reversed. The experimental results also offer a novel valuable dataset for the validation of other urban flooding numerical models. This study could be referred to by local authorities and stakeholders to take effective measures to improve the safety of pedestrians and mitigate flood risk in the context of urban planning and urban flood emergency management, such as strengthening flood-resilient urban facility designs and devising optimal routines for emergent evacuation of outdoor pedestrians.

Flood Emergency Management in a Village in the Czech Republic

Flood Emergency Management in a Village in the Czech Republic

Estimating the instability criterion of vehicles in urban flooding by an entropic method

Hydrodynamic instability of vehicles in urban flooding always leads to a serious loss of people's life and property all over the world. Thus it is vital to formulate the safety criterion of the vehicle exposed to urban floodwater. In this study, an explicit expression for the incipient velocity of a flooded vehicle concerning water depth is derived based on the Shannon and Tsallis entropy concepts. This entropy-based expression can mimic the decaying trend of the incipient velocity with water depth from shallow water to deep water. Furthermore, a comparison with five experimental datasets regarding the critical instability condition of flooded vehicles shows its validity with a high correlation coefficient and a low relative bias. Sensitivity test results of the entropic model to some parameters, including the friction coefficient, the drag coefficient, densities of the vehicle and surrounding fluid, and length and height of the vehicle, are found to be consistent with our qualitative understandings regarding the physical mechanism of hydrodynamic instability of the flooded vehicle. Taking two types of vehicles popular in China as an example: Audi Q7 passenger car and Ford V348 ambulance, this study illustrates the vulnerability degree zoning of flooded vehicles, which could be referred to by administrators and stakeholders for flood hazard evaluation and flood emergency management. With an increasing floodwater velocity and a surging water level, the safety of the flooded vehicle is threatened, and more protective measures are needed. This study indicates the potential of the entropy-based method as a good addition to existing empirical/semi-empirical models for characterizing the instability threshold of parking vehicles on a road or street in urban flooding.

Automation and Coupling of Models for Coastal Flood Forecasting in South Texas

Forecasting natural disasters such as inundations can be of great help for emergency bodies and first responders. In coastal communities, this risk is often associated with storm surge. To produce flood forecasts for coastal communities, a system must incorporate models capable of simulating such events based on forecasted weather conditions. In this work, a system for forecasting inundations based predominantly on storm surge is explored. An automation and a coupling strategy were implemented to produce forecasted flood maps automatically. The system leverages an ocean circulation model and a channel water flow model to estimate flood events in South Texas specially alongside the Lower Laguna Madre. The system around the models is implemented using Python and the meteorological forcing input is obtained from weather forecasting models maintained by the National Oceanic and Atmospheric Administration. The forecasted weather data retrieval, data processing and automation of the models are successful, and the complete stack of software can be deployed locally or in cloud solutions to accelerate computations. The resulting system performs as expected and successfully produces flood maps automatically providing vital information for flood emergency management in coastal communities.

A Rapid Prediction Model of Urban Flood Inundation in a High-Risk Area Coupling Machine Learning and Numerical Simulation Approaches

Climate change has led to increasing frequency of sudden extreme heavy rainfall events in cities, resulting in great disaster losses. Therefore, in emergency management, we need to be timely in predicting urban floods. Although the existing machine learning models can quickly predict the depth of stagnant water, these models only target single points and require large amounts of measured data, which are currently lacking. Although numerical models can accurately simulate and predict such events, it takes a long time to perform the associated calculations, especially two-dimensional large-scale calculations, which cannot meet the needs of emergency management. Therefore, this article proposes a method of coupling neural networks and numerical models that can simulate and identify areas at high risk from urban floods and quickly predict the depth of water accumulation in these areas. Taking a drainage area in Tianjin Municipality, China, as an example, the results show that the simulation accuracy of this method is high, the Nash coefficient is 0.876, and the calculation time is 20 seconds. This method can quickly and accurately simulate the depth of water accumulation in high-risk areas in cities and provide technical support for urban flood emergency management.

Hospital flood emergency management planning using Markov models and discrete-event simulation

Disasters such as major floods affect all part of the globe. Hospital and healthcare structures are critical resources during such event and do not always benefit of emergency preparedness. When hospitals are impacted by the disaster, it puts a strain on the system and a reorganization of all available hospitals on a given territory is necessary. As part of case study applied to the impact of floods on the Île-De-France region’s health system, we present in this paper a simulation model that evaluates healthcare emergency plan by combining the healthcare process with the flood dynamics. The results can be used to elaborate an optimized strategy for evacuation and transfer operations. We provide a case study including several medical specialties and quantify the impact of several flood scenarios on the healthcare system.

Integrating user‐generated content to track urban flooding hotspots and foster emergency management: A case study in central China

AbstractThis study explores the potential for employing user‐generated content (UGC) during severe flooding to discover and track urban flooding disaster hotspots in a timely manner. A flooding case in central China was selected for this study. Crawlers, natural language processing, and geographic visualization methods were used to extract flood‐related UGC specific to severely flooded areas. Further, the quality of the geo‐tagged content on the web was verified using scientific gauge data (e.g., water level, digital elevation model, and rainfall). Based on our findings, we were able to deduce that UGC on the web is valuable for identifying flooding hotspots. In fact, this approach offers substantial advantages for addressing the emerging needs of data acquisition for flood emergency management. Notably, the values of urban stakeholders that share their observations on the web can be mined rapidly through the integration of various approaches. Overall, the findings of our study can contribute to the efficient mining of web data sources and development of disaster hotspot mapping systems at an urban scale to improve flood management and mitigation. The limitations of UGC and our study's future direction are also discussed.