Emergency Management Activities Research Articles

On the Capabilities of the IREA-CNR Airborne SAR Infrastructure

In this work, the airborne Synthetic Aperture Radar (SAR) infrastructure developed at the Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council of Italy (CNR) is described. This infrastructure allows IREA-CNR to plan and execute airborne SAR campaigns and to process the acquired data with a twofold aim. On one hand, the aim is to develop research activities; on the other hand, the aim is to support the emergency prevention and management activities of the Department of Civil Protection of the Italian Presidency of the Council of Ministers, for which IREA-CNR serves as National Centre of Competence. Such infrastructure consists of a flight segment and a ground segment that include a multi-frequency airborne SAR sensor based on the Frequency-Modulated Continuous Wave (FMCW) technology and operating in the X and L bands, an Information Technology (IT) platform for data storage and processing and an airborne SAR data processing chain. In this work, the technical aspects related to the flight and ground segments of the infrastructure are presented. Moreover, a discussion on the response times and characteristics of the final products that can be achieved with the infrastructure is provided with the aim of showing its capabilities to support the monitoring activities required in a possible emergency scenario. In particular, as a case study, the acquisition and subsequent interferometric processing of airborne SAR data relevant to the Stromboli volcanic area in the Sicily region, southern Italy, are presented

Evaluating the impact of citizen collaboration with government agencies in disaster response operations: An agent-based simulation study

In emergencies, the involvement of informal volunteers as first responders plays a crucial role in alerting professionals and carrying out initial rescue efforts due to their proximity to incidents. The lack of proper training among many of these informal volunteers, however, raises concerns about their potential to inadvertently cause harm. Within the disaster response community, there is a growing need to understand the collaboration dynamics between spontaneous volunteers and government officials in disaster response planning. This paper quantitatively evaluates the value of collaboration between formal government agencies and informal citizen volunteers in disaster response operations. It investigates the potential for informal volunteers' involvement to enhance the effectiveness of disaster response under different collaboration strategies. The study aims to identify best practices for coordinating officials and citizens, develop novel collaborative response models, and assess those models’ performance in emergencies. To achieve this goal, agent-based simulation is employed to model alternative collaboration strategies between the government and informal volunteers across various disaster scenarios. The simulation incorporates verified request locations from emergency phone calls and on-the-ground assessment data, as well as unverified request locations obtained from social media platforms. The objective of this study is to examine various collaboration strategies and assess their impact on the response rates of disaster victims, considering different citizen-related, responder-related, and logistics-related disaster factors. This research provides insights into the current state of emergency management activities and proposes improvements to enhance the effectiveness of disaster response efforts.

Data-Driven Dynamic Bayesian Network Model for Safety Resilience Evaluation of Prefabricated Building Construction

Due to factors such as the availability of assembly equipment, technology, and site management level, prefabricated building construction safety accidents often occur. To ensure the safety of prefabricated buildings and effectively reduce the accident rate, the concept of resilience is introduced into the safety management of prefabricated buildings. Based on the resilience absorption capacity, adaptation capacity, recovery capacity, and optimization capacity, a comprehensive evaluation index system for the safety resilience of prefabricated buildings is established. By combining prior knowledge with structural learning and parameter learning, a dynamic Bayesian network (DBN) model is constructed to dynamically evaluate the safety resilience of prefabricated buildings. Through forward causal reasoning and backward diagnostic reasoning, the dynamic safety resilience value of prefabricated buildings and the chain of maximum failure causes are obtained. Finally, by conducting a sensitivity analysis on the target nodes, the key influencing factors of the safety resilience of prefabricated construction are identified, and improvement suggestions for enhancing resilience are proposed. The results indicate that establishing a resilience safety culture, preventing unsafe behaviors of personnel, safety management, and supervision on the construction site, emergency management actions, and building a risk management information system are crucial factors influencing the safety resilience of prefabricated buildings. The enhancement of absorption capacity has the greatest impact on the safety resilience of prefabricated buildings.

An interesting parameter deduced from confirmed infection number in COVID-19 epidemic

In controlling outbreaks of COVID-19, the number of daily confirmed infected cases is useful in deducing parameters to determine emergency management actions such as locking down the cities. However, complicated mathematical models with ordinary differential equations are involved. Resources including manpower and data accessibility are needed. The parameters deduced are always challenged by stakeholders, particularly those from servicing and tourist industry. A simple parameter is proposed to describe the extent of infection by estimating the transient daily infection number divided by the time. The daily infection number in Hong Kong is used to illustrate the approach. This will not require large-scale long-term observation and mathematical modeling or computation. Keywords: COVID-19; infection number; parameter

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.

An assessment of the performance of emergency management agency in the natural hazards management among farm households in the southeast zone, Nigeria

An assessment of the performance of emergency management agency in mitigating natural hazards among farm households in Southeast Zone, Nigeria was studied. About 240 farm households who were administered questionnaire were chosen from the states’ flood- and erosion-prone regions using multi-stage sampling technique. The results indicate that the EmergencyManagement Based-Performance Index’s average level of national emergency management activities was 57.33. The total average ratings of the National Emergency Management Agency/State Emergency Management Agency performance indicators based on their usefulness as a measure of natural hazard were estimated as 47.8% which showed that the NEMA/SEMA key performance indicators’ degree of effectiveness in hazard management is deemed to be below average of the index. These key performance indicators (KPI) include; distribution of food, provision of seedlings, provision of agro-chemical, training of farmers on postharvest crop preservation, use of weather, rehabilitation of water resources, expansion of irrigation facilities, distribution of fingerlings, provision of household items. The highest weighted score assigned to the distribution of food was 2.89, indicating that it is 57.8% successin mitigating natural disasters, while the average weight score allocated to the distribution of seedlings was 2.62, indicating a 52.4% degree of efficacy. However, the study recommends that the funds allotted to NEMA/SEMA should be monitored to ensure it is utilized in achieving its stated aims and objectives.

The Need for Emergency Management Models

Emergency agencies can use emergency management models to enable them to better prepare for and respond to emergencies. This qualitative study aims to undertake a critical examination of emergency management models by thematic analysis to determine their significance to emergency management. A review and analysis of the existing literature were used in the study. The models were studied to explore their role in emergency management and to identify any significant constraints or challenges which could limit the ability of the emergency management model to carry out appropriate actions. The study found that such models are indispensable because they simplify and improve emergency management. Additionally, they may support planners, managers, and practitioners in reaching proper decisions, making them a valuable and necessary decision-making support tool. The study also showed that each model has an advantage that distinguishes it from the other models. Consequently, a comprehensive emergency management model should be designed to suit all cases and circumstances. The findings also confirmed the doubts raised about the limitations and concerns associated with the models. Concerns included future events’ unpredictability, the models’ prescriptive nature, the event’s cultural context, and the impact on businesses. The findings also indicated that certain planners, managers, and practitioners had a limited understanding of the use of models in emergency management. As such, they appear to have overlooked the use of models while dealing with emergencies. Hence, the study recommends that the models should be employed in all emergency management activities. The study also recommends that the findings are utilized as a basis for further research into the potential use of emergency management models.

Machine learning model for building seismic peak roof drift ratio assessment

The peak roof drift ratio is one of the most important engineering parameters to describe the expected seismic damage in a building. A predictive model of the drift ratio was developed using a machine learning approach (Gaussian process regression model) on a dataset of approximately 11,800 records from 34 monitored buildings in Japan. Four predictors for ground motion and three predictors for building vulnerability are used in the machine-learning modelling. The residual analysis shows a reduction of 50% compared to the state of the art. The Gaussian process regression model is applied in a second analysis on an original dataset of approximately 4,500 records for 127 monitored buildings in Italy. A satisfactory comparison emerges by comparing the drift ratio prediction map with the observed damage pattern produced by satellite imagery for a test site in central Italy after the 2009 earthquake. The drift ratio map plays an important role in the simulation of an earthquake scenario at regional scale, which is needed by Civil Protection for emergency planning and management activities.

Knowledge to action: Resilience planning among state and local transportation agencies in the United States.

The results of a nationwide survey of state and local agencies conducted in 2020 are summarized to understand priorities for improving transportation emergency management (EM) and resilience. Questions on EM activities, policies, and operations were answered by 190 respondents from across the country. In addition to comparing responses across transportation, EM, and other organizations (law enforcement, public works, health, environmental services, etc.), differences are analyzed based on agency size, location, experience, and hazard exposure. Respondents were also asked to prioritize EM activities for additional funding. Capabilities such as Emergency Planning, Traffic Control, Response Teams, and Situational Awareness emerge as priority areas. Approximately 60 percent of transportation agencies report updates of EM plans within the last 5 years compared to 70 percent of EM and 38 percent among other agencies. Four general linear models (GLMs) are developed and tested to determine the attributes of respondents who support improvements in (1) planning, (2) communications, (3) collaboration, and (4) transformation of agencies. An evaluative framework for measuring and managing resilience is developed and tested based on the responses. The analysis reveals diverse EM experiences, capabilities, and priorities, providing insight into future directions for sustaining and improving transportation resilience.

The Benefit of Informed Risk-Based Management of Civil Infrastructures

One of the most interesting applications of Structural Health Monitoring (SHM) is the possibility of providing real-time information on the conditions of civil infrastructures during and following disastrous events, thus supporting decision-makers in prompt emergency operations. The Bayesian decision theory provides a rigorous framework to quantify the benefit of SHM through the Value of Information (VoI) accounting for different sources of uncertainties. This decision theory is based on utility considerations, or, in other words, it is based on risk. Instead, decision-making in emergency management is often based on engineering judgment and heuristic approaches. The goal of this paper is to investigate the impact of different decision scenarios on the VoI. To this aim, a general framework to quantify the benefit of SHM information in emergency management is applied to different decision scenarios concerning bridges under scour and seismic hazards. Results indicate that the considered decision scenario might tremendously affect the results of a VoI analysis. Specifically, the benefit of SHM information could be underestimated when considering non-realistic scenarios, e.g., those based on risk-based decision-making, which are not adopted in practice. Besides, SHM information is particularly valuable when it prevents the selection of suboptimal emergency management actions.

A CAST-Based Analysis of the Metro Accident That Was Triggered by the Zhengzhou Heavy Rainstorm Disaster.

Emergency management research is used to deal with the increasing number of extreme weather threats in urban areas. This paper uses causal analysis based on systems theory (CAST) to review the subway water ingress accident and the government’s emergency management actions in Zhengzhou, Henan Province, during the heavy rainstorm disaster on 20 July 2021. The aims of this article are to establish safety control structures at both the enterprise level and the government level, and to systematically analyze the problems in emergency management in Zhengzhou City. Our analysis found that the construction of disaster prevention facilities restricted emergency management. Therefore, we suggest that enterprises and governments not only pay attention to emergency management, but also to the construction of disaster prevention facilities. This article also points out that the system of chief executive responsibility that is implemented in China is becoming a double-edged sword in emergency management. Our study makes recommendations for enhancing the capacities of emergency management, points out the shortcomings of the existing emergency management structure, and provides knowledge gained for future emergency management research.

Hydrodynamic and Wave Responses During Storm Surges on the Southern Brazilian Coast: A Real-Time Forecast System

Coastal flooding is a global phenomenon that results in severe economic losses, threatens lives, and impacts coastal communities worldwide. While recent developments in real-time flood forecasting systems provide crucial information to support coastal communities during coastal disasters, there remains a challenge to implement such systems in data-poor regions. This study demonstrates an operational real-time coupled surge wave guidance system for the coastal areas of Southern Brazil. This system is based on the recently developed integrated flood (iFLOOD) model, which utilizes the coupled hydrodynamic and phase-averaged ADCIRC–SWAN wave numerical model, driven by astronomical tides and atmospheric forcing from the Global Forecast System (GFS). This numerical modeling framework can simulate water levels and waves with a lead time of 84 h. A version of the coupled ADCIRC–SWAN model calibrated for Brazil, i.e., iFLOOD-Brazil, was operationally implemented (i.e., twice a day) over a period of 4 months (April to September 2020) for normal daily weather validation, as well as during a recent “bomb” cyclone that strongly impacted the southern coast of the country in June 2020. The real-time water levels and waves forecasted by iFLOOD-Brazil showed promising results against observations, with root mean square error (RMSE) values of 0.32 m and 0.68 m, respectively, for normal daily weather. Additionally, the RMSE values were 0.23 m for water levels and 1.55 m for waves during extreme weather, averaged over eight water level and two wave recording stations. In order to improve real-time predictions, a bias correction scheme was introduced and was shown to improve the water level and wave forecasts by removing the known systematic errors resulting from underestimation of astronomical tides and inadequate initial boundary conditions. The bias-corrected forecasts showed significant improvements in forecasted wave heights (0.47 m, 0.35 m) and water levels (0.17 m, 0.28 m) during daily and extreme weather conditions. The real-time iFLOOD-Brazil forecast system is the first step toward developing an accurate prediction model to support effective emergency management actions, storm mitigation, and planning in order to protect these economically valuable and socially vulnerable coastal areas.

Explosion at an aluminum factory caused by the July 2018 Japan floods: Investigation of damages and evacuation activities

An explosion at an aluminum factory occurred due to floods in Soja City, Okayama Prefecture, in July 2018. This study investigates the accident, its overall impact on local residents, and emergency management activities. The results of the study showed that several nearby structures and homes were damaged by the explosion. Concern for more explosions has led to the evacuation of residents of Shimobara area of Soja City. In Shimobara, residents had carried out voluntary disaster prevention activities before the disaster. After the explosion, Shimobara residents were able to evacuate safely in cooperation with the voluntary disaster prevention organization and the local government agency. The study revealed that there was a lack of information on chemical accident hazards shared by the factory with local government authorities and first responders, as well as with local residents. In order to be better prepared for technological accidents, and in particular accidents caused by natural disasters (known as Natechs), mechanisms for information sharing between companies and municipalities should be adopted.

Preparedness and response activities of the US Department of Veterans Affairs (VA) home-based primary care program around the fall 2017 hurricane season

BackgroundLarge-scale natural disasters disproportionally affect both the medically complex and the older old, groups that are responsible for most medical surge after a disaster. To understand how to ameliorate this surge, we examined the activities of the nine US Department of Veterans Affairs (VA) Home Based Primary Care (HBPC) programs impacted during the 2017 Fall Hurricane Season.MethodsConvergent mixed methods design, incorporating independently conducted qualitative and quantitative analyses. Phase One: 34 clinical staff were interviewed from the nine VA HBPC programs impacted by Hurricanes Harvey, Irma, and Maria to examine the experiences of their HBPC programs in response to the Hurricanes. Phase Two: Secondary quantitative data analysis used the VA’s Corporate Data Warehouse (CDW) to examine the electronic health records of patients for these same nine sites.ResultsThe emergency management activities of the HBPC programs emerged as two distinct phases: preparedness, and response and recovery. The early implementation of preparedness procedures, and coordinated post-Hurricane patient tracking, limited disruption in care and prevented significant hospitalizations among this population.ConclusionsIndividuals aged 75 or older, who often present with multiple comorbidities and decreased functional status, typically prefer to age in their homes. Additionally, as in-home medical equipment evolves, more medically vulnerable individuals are able to receive care at home. HBPC programs, and similar programs under Medicare, connect the homebound, medically complex, older old to the greater healthcare community. Engaging with these programs both pre- and post-disasters is central to bolstering community resilience for these at-risk populations.

Multitemporal analysis of ambient noise polarization to characterize site response in the town of Amatrice, shattered by the 2016 central Italy earthquake

SUMMARYThe Mw 6.0 earthquake that hit central Italy on 2016 August 24 caused an abnormally high level of destruction in the town of Amatrice. In order to clarify the role of site response in causing such a disaster, a series of ambient noise recordings acquired in the aftermath of the event are analysed here to identify site resonance properties from the ratios H/V between horizontal and vertical amplitudes of ground motion. Although the noise data acquisition was limited by the emergency management activities, the use of a new analysis technique, which isolates the contribution of Rayleigh waves to the noise wavefield and averages instantaneous estimates of H/V ratios, provided more stable results compared to the standard Nakamura's technique based on mean spectral ratios. The results demonstrated the occurrence of significant resonance phenomena, but without an obvious correlation with the spatial distribution of damage severity. It is apparent that the damage severity was also influenced by some additional local factors related to building vulnerability. Moreover, the time-series analysis revealed seasonal variations in the Rayleigh wave ellipticity curves likely related to the water content changes in the surface deposits and their influence on the Poisson coefficient. Finally, the new method proved capable of recognizing time-varying directions of Rayleigh wave propagation. This capability could be exploited to support other passive seismic methods (e.g. ReMi), whose results’ reliability is limited by the lack of control on wave direction origin.

CIM-Powered Multi-Hazard Simulation Framework Covering both Individual Buildings and Urban Areas

To improve the ability to prepare for and adapt to potential hazards in a city, efforts are being invested in evaluating the performance of the built environment under multiple hazard conditions. An integrated physics-based multi-hazard simulation framework covering both individual buildings and urban areas can help improve analysis efficiency and is significant for urban planning and emergency management activities. Therefore, a city information model-powered multi-hazard simulation framework is proposed considering three types of hazards (i.e., earthquake, fire, and wind hazards). The proposed framework consists of three modules: (1) data transformation, (2) physics-based hazard analysis, and (3) high-fidelity visualization. Three advantages are highlighted: (1) the database with multi-scale models is capable of meeting the various demands of stakeholders, (2) hazard analyses are all based on physics-based models, leading to rational and scientific simulations, and (3) high-fidelity visualization can help non-professional users better understand the disaster scenario. A case study of the Tsinghua University campus is performed. The results indicate the proposed framework is a practical method for multi-hazard simulations of both individual buildings and urban areas and has great potential in helping stakeholders to assess and recognize the risks faced by important buildings or the whole city.

Review of Modeling Methodologies for Managing Water Distribution Security.

Water distribution systems are vulnerable to hazards that threaten water delivery, water quality, and physical and cybernetic infrastructure. Water utilities and managers are responsible for assessing and preparing for these hazards, and researchers have developed a range of computational frameworks to explore and identify strategies for what-if scenarios. This manuscript conducts a review of the literature to report on the state of the art in modeling methodologies that have been developed to support the security of water distribution systems. First, the major activities outlined in the emergency management framework are reviewed; the activities include risk assessment, mitigation, emergency preparedness, response, and recovery. Simulation approaches and prototype software tools are reviewed that have been developed by government agencies and researchers for assessing and mitigating four threat modes, including contamination events, physical destruction, interconnected infrastructure cascading failures, and cybernetic attacks. Modeling tools are mapped to emergency management activities, and an analysis of the research is conducted to group studies based on methodologies that are used and developed to support emergency management activities. Recommendations are made for research needs that will contribute to the enhancement of the security of water distribution systems.

Citizens’ Spatial Footprint on Twitter—Anomaly, Trend and Bias Investigation in Istanbul

Social media (SM) can be an invaluable resource in terms of understanding and managing the effects of catastrophic disasters. In order to use SM platforms for public participatory (PP) mapping of emergency management activities, a bias investigation should be undertaken with regard to the data related to the study area (urban, regional or national, etc.) to determine the spatial data dynamics. Thus, such determinations can be made on how SM can be used and interpreted in terms of PP. In this study, the city of Istanbul was chosen for social media data research area, as it is one of the most crowded cities in the world and expecting a major earthquake. The methodology for the data investigation is: 1. Obtain data and engage sampling, 2. Identify the representation and temporal biases in the data and normalize it in response to representation bias, 3. Identify general anomalies and spatial anomalies, 4. Manipulate the trend of the dataset with the discretization of anomalies and 5. Examine the spatiotemporal bias. Using this bias investigation methodology, citizen footprint dynamics in the city were determined and reference maps (most likely regional anomaly maps, representation maps, time-space bias maps, etc.) were produced. The outcomes of the study can be summarized in four steps. First, highly active users generate the majority of the data and removing this data as a general approach within a pseudo-cleaning process means concealing a large amount of data. Second, data normalization in terms of activity levels, changes the anomaly outcome resulting from diverse representation levels of users. Third, spatiotemporally normalized data present strong spatial anomaly tendency in some parts of the central area. Fourth, trend data is dense in the central area and the spatiotemporal bias assessments show the data density varies in terms of the time of day, day of week and season of the year. The methodology proposed in this study can be used to extract the unbiased daily routines of the social media data of the regions for the normal days and this can be referred for the emergency or unexpected event cases to detect the change or impacts.

Urban Settlements and Road Network Analysis on the Surrounding Area of the Almirante Alvaro Alberto Nuclear Complex, Angra dos Reis, Brazil

Nuclear power plants accidents should be seen as very complex issues, once they encompass environmental and social aspects that must be integrated to sustain appropriate management measures in a potential disaster event. Therefore, emergency plans require sounds spatial information knowledge and the interactions of their environments. The herein study aims to contribute to the emergency plans, especially to the possible evacuation routes identification for a Nuclear Power Plant located in Angra dos Reis, Brazil. The study was conducted within 120 km radius around the power plant. Results indicate that evacuation plans should be elaborated for the urban cores with low roads availability, or even for high population density areas, that in spite of being well served by alternative routes, exhibit increased road traffic and population flow. These results present important information to provide support to local emergency management actions and guidelines, playing a key role to assist on the evacuation planning.

Assessing relevance of tweets for risk communication

ABSTRACTAlthough Twitter is used for emergency management activities, the relevance of tweets during a hazard event is still open to debate. In this study, six different computational (i.e. Natural Language Processing) and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood event. Primarily, tweets containing information about the flooding events and its impacts were analysed. Examination of the relationships between tweet volume and its content with precipitation amount, damage extent, and official reports revealed that relevant tweets provided information about the event and its impacts rather than any other risk information that public expects to receive via alert messages. However, only 14% of the geo-tagged tweets and only 0.06% of the total fire hose tweets were found to be relevant to the event. By providing insight into the quality of social media data and its usefulness to emergency management activities, this study contributes to the literature on quality of big data. Future research in this area would focus on assessing the reliability of relevant tweets for disaster related situational awareness.