Post-disaster Management Research Articles

Drone assisted device to device cooperative communication for critical environments

This paper proposes drone assisted device-to-device cooperative communication (DA-DDCC) for critical situations during post-disaster management. The proposed network utilizes the autonomous mode of D2D communication for setting up the link in the absence of a central node. This network incorporates cooperative communication using drone in D2D session for improving reliability of the overall system. A probability-based statistical channel model for such networks is proposed by taking the statistical independence of links into consideration. Unlike the existing air-to-ground (A2G) channel models that use either Rayleigh or Rician distribution for uplink (UL) and downlink (DL) channel modelling, our approach takes the probability of occurrence of line of sight (LoS) into account while predicting the appropriate channel distribution for UL and DL separately. For performance evaluation of the proposed network, average outage probability and average capacity are derived using the proposed channel model. Monte Carlo simulations are conducted to verify our analysis. Moreover, a multi-cluster DA-DDCC scenario is also being analyzed through simulations from an interference perspective to justify the usefulness of the proposed channel model. Results obtained through this investigation can be utilized in selecting various crucial system parameters judiciously for enhanced performance during post-disaster scenario.

Volumetric Analysis of the Landslide in Abe Barek, Afghanistan Based on Nonlinear Mapping of Stereo Satellite Imagery-Derived DEMs

On 2 May 2014, a large-scale landslide in Abe Barek, Badakhshan, Afghanistan, produced extensive damage to the buildings and killed hundreds of people. Evaluations of the extent and the volume of the displaced materials are vital for post-disaster management activities. In this study, we present the applicability of a nonlinear geometric correction technique for decreasing the undesired registration errors between pre- and post-event digital elevation models (DEMs) generated from high-resolution stereo pair satellite imagery, identifying landslide affected areas, and quantifying the landslide volume from DEMs of difference (DoD) analysis. The nonlinear mapping method consists of shifting vector generation in subareas of the DEMs, consensus operations, and interpolation of the shifting vectors. The quality assessment confirmed that the method outperformed the simple DoD technique by eliminating a large-scale of geometric errors in an unaffected area. We estimated the volume of the landslide as 1.05 × 106 m3 from the DoD corrected by the nonlinear method, and discussed the relationship between the area and volume compared to those of the previous studies.

Autonomous Unmanned Aerial Vehicle for Post-Disaster Management With Cognitive Radio Communication

Unmanned aerial vehicles (UAV) play a significant role in finding victims affected in the post-disaster zone, where a man cannot risk his life under a critical condition of the disaster environment. The proposed design incorporates autonomous vision-based navigation through the disaster environment based on general graph theory with dynamic changes on the length between two or multiple nodes, where a node is a pathway. Camera fixed on it continuously captures the surrounding footage, processing it frame by frame on-site using image processing technique based on a SOC. Identifies victims in the zone and the pathways available for traversal. UAV uses an ultrasonic rangefinder to avoid collision with obstacles. The system alerts the rescue team if any victim detected and transmits the frames using CRN to the off-site console. UAV learns navigation policy that achieves high accuracy in real-time environments; communication using CRN is uninterrupted and useful during such emergencies.

A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings

Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure’s performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building’s behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings.

Urban Informality, Post-Disaster Management, and Challenges to Gender-Responsive Planning in Haiti Since the 2010 Earthquake

Haitian officials, in line with most country leaders around the world, announced a series of health, hygiene and safety precautions following the COVID-19 global pandemic early in 2020. The tiny nation (10,714 square miles) situated on the island of Hispaniola, still recovering from the devastating 2010 earthquake, which claimed the lives of close to two hundred thousand people, seemed prepared to take on the challenges of COVID-19. Businesses and schools immediately closed, face masks and hand sanitizers were distributed by the thousands. But the effects of emergency injunctions that were not geared towards capacity-building, but rather prevention of rapid infectious disease transmission, could prove debilitating for the impoverished nation over the long-term. Primary and secondary school enrollment rates in Haiti are at an all-time low, and projections for the Haitian economy are dismal (-3.5% GDP growth 2020f) (World Bank 2020: 27). As a retrospective study, this paper conducts a critical quantitative and qualitative analysis of humanitarian aid, gender-based violence, and urbanism in Haiti, revealing that gender-responsive planning has a greater role to play in state-led disaster management plans and procedures for achieving long-term equity and sustainable economic growth.

THE DYNAMICS OF MULTI ACTORS ROLES IN POST-SUNDA STRAIT TSUNAMI DISASTER MANAGEMENT: LESSONS FROM THE INFRASTRUCTURE DEVELOPMENT PHASES

Purpose of the study: This study aims to explore the role of government and non-government actors and analyze the dynamics of the multi-actor's role in the phase of infrastructure development after the Sunda Strait tsunami disaster in South Lampung Regency, Indonesia.
 Methodology: The study was conducted in a qualitative approach that analyzes regulative, technical, and implementation aspects. Data is collected from the local government, parliament, community leaders, and private organizations—data collection through in-depth interviews with 20 informants, observation, and documentation. Meanwhile, data analysis was carried out using interactive analysis with the use of data type and data sources triangulation.
 Main Findings: The results of the study identified the involvement of government, community, and private organizations in the post-disaster phase, which had a positive impact on refugees. Dynamics occur at the stage of temporary and permanent housing development, where there is a change in the role of district, provincial and private governments and community institutions.
 Applications of this study: The results of this study can be a reference in managing the role and managing the dynamics that occur in the interaction between actors in disaster management in local governments.
 Novelty/Originality of this study: This research shows the dynamics of the role of actors in disaster management, which fluctuate but are not accommodated institutionally, even though the potential of this group can strengthen disaster management governance and build an active collaborative model in post-disaster management. The findings can be useful in strengthening government networks in disaster management in various regions and countries.

Experiences of an earthquake during pregnancy, antenatal mental health and infants\u2019 birthweight in Bhaktapur District, Nepal, 2015: a population-based cohort study

BackgroundInfant birthweight is an important public health indicator that is a proxy of maternal and baby’s health. Earthquakes can cause acute distress, but can also contribute to chronic stress through long-term disruptions to social, economic and domestic circumstances. The aims of this study were to examine the direct effect of earthquake experiences on the birthweight of infants of women who experienced the 2015 Nepal Earthquakes during pregnancy and whether mental health mediated this relationship.MethodsThis is a prospective, population-based cohort study. Pregnant women in Bhaktapur District, Nepal who had experienced the 2015 earthquakes after conception were recruited. Baseline data were collected in structured individual interviews. Follow up was a short telephone interview two weeks after the baby’s birth. Infant birthweight recorded on the hospital-issued birth certificate. The direct effect of earthquake experiences on birthweight and the indirect effect via antenatal common mental disorders (CMDs) were tested using Sobel tests simultaneously.ResultsIn total 497/498 women contributed baseline data and 469 (94.4%) women also provided the birth weight of their infants. In total 6% of infants had low birth weight (< 2.5 kg). The earthquake experiences did not have a significant direct effect on birthweight (p = 0.116). However, the effect of earthquake experiences had a significant indirect effect on infant birthweight via CMDs. When 15 covariates were controlled, women who had moderate to severe earthquake experiences had an increase of 1.58 scores of antenatal CMD symptoms that resulted in their babies being 20.50 g lighter than those who had low earthquake experiences (p = 0.026).ConclusionsThe findings from this study indicate that the relationship is mediated by the mental health of women during pregnancy. Data demonstrate that the mental health of women who are pregnant should be prioritised in post-disaster management not only because of the burden experienced by women but also because of the risk for the growth and development of their babies.

Post-disaster land use management after mega coastal disasters

Post-disaster land use management after mega coastal disasters

PENTINGNYA PENANGANAN PASCABENCANA YANG BERFOKUS PADA PENDUDUK UNTUK MEWUJUDKAN BUILD BACK BETTER: PEMBELAJARAN DARI BENCANA PALU, SIGI, DAN DONGGALA

Sendai Framework 2015-2030 highlights the importance of build back better after a disaster. Post-disaster management is an important stage to reach this goal. In general, rehabilitation process tends to focus on physical aspects and rarely considering people as the center. This paper aims to examine the rehabilitation process after earthquake and tsunami in Central Sulawesi in 2018 focused on opportunities and challenges to build back better. The temporary shelter (Huntara) is selected as the case study as it is the most urgent need after disaster. Data analysis is based on literature reviews and primary data from rapid assessment in December 2018 in Palu, Sigi, and Donggala. Based on the people-centered concept, results show that most of the temporary shelters are provided as a product and less concern about its function as a process. However, some temporary shelters have considered the process that comes from civil society’s initiatives. They propose Huntara as a transition by proposing a feeling of comfort and secure. To conclude, survivors need to be involved in the rehabilitation process to build back better.

The Structural Performance of Reinforced Concrete Members with Monolithic Non-Structural Walls under Static and Dynamic Loads

The required base shear and drift limit for post-disaster management buildings have increased in the Japanese Building Code following major seismic events. One method to satisfy these requirements for reinforced concrete frame buildings is to cast exterior non-structural concrete wall elements to be monolithic with frame elements, but without anchoring the longitudinal wall reinforcing. This provides additional stiffness and strength while limiting significant damage in the non-structural wall. In this study, the structural performances of such elements were evaluated using static and dynamic experimental tests. The result indicates that non-structural walls that were neither isolated by seismic slits nor anchored to the adjacent walls with longitudinal reinforcements experienced less damage and higher deformability compared with walls having seismic slits. The confinement reinforcing impact was not observed on the strength and drift capacity of the beam member, owing to the large number of transverse reinforcements. However, the confinements limited the damage and nearly prevented concrete crushing. The maximum horizontal load of the specimen could be predicted using cross-sectional analysis, and the authors propose a simple equation to predict it with sufficient accuracy.

Social media in aid of post disaster management

Purpose This paper aims to examine tweet posts regarding Typhoon Washi to contend the usefulness of social media and big data as an aid of post-disaster management. Through topic modelling and content analysis, this study examines the priorities of the victims expressed in Twitter and how the priorities changed over a year. Design/methodology/approach Social media, particularly Twitter, was where the data gathered. Using big data technology, the gathered data were processed and analysed according to the objectives of the study. Topic modelling was used in clustering words from different topics. Clustered words were then used for content analysis in determining the needs of the victims. Word frequency count was also used in determining what words were repeatedly used during the course period. To validate the gathered data online, government documents were requested and concerned government agencies were also interviewed. Finding Findings of this study argue that housing and relief goods have been the top priorities of the victims. Victims are seeking relief goods, especially when they are in evacuation centres. Also, the lack of legal basis hinders government officials from integrating social media information unto policymaking. Research limitation This study only reports Twitter posts containing keywords either, Sendong, SendongPH, Washi or TyphoonWashi. The keywords were determined based on the words that trended after Typhoon Washi struck. Practical implication For social media and big data to be adoptable and efficacious, supporting and facilitating conditions are necessary. Structural, technical and financial support, as well as legal framework, should be in place. Maintaining and sustaining positive attitude towards it should be taken care of. Originality/value Although many studies have been conducted on the usefulness of social media in times of disaster, many of these focused on the use of social media as medium that can efficiently spread information, and little has been done on how the government can use both social media and big data in collecting and analysing the needs of the victims. This study fills those gaps in social big data literature.

Using Social Media to Mine and Analyze Public Opinion Related to COVID-19 in China.

The outbreak of Corona Virus Disease 2019 (COVID-19) is a grave global public health emergency. Nowadays, social media has become the main channel through which the public can obtain information and express their opinions and feelings. This study explored public opinion in the early stages of COVID-19 in China by analyzing Sina-Weibo (a Twitter-like microblogging system in China) texts in terms of space, time, and content. Temporal changes within one-hour intervals and the spatial distribution of COVID-19-related Weibo texts were analyzed. Based on the latent Dirichlet allocation model and the random forest algorithm, a topic extraction and classification model was developed to hierarchically identify seven COVID-19-relevant topics and 13 sub-topics from Weibo texts. The results indicate that the number of Weibo texts varied over time for different topics and sub-topics corresponding with the different developmental stages of the event. The spatial distribution of COVID-19-relevant Weibo was mainly concentrated in Wuhan, Beijing-Tianjin-Hebei, the Yangtze River Delta, the Pearl River Delta, and the Chengdu-Chongqing urban agglomeration. There is a synchronization between frequent daily discussions on Weibo and the trend of the COVID-19 outbreak in the real world. Public response is very sensitive to the epidemic and significant social events, especially in urban agglomerations with convenient transportation and a large population. The timely dissemination and updating of epidemic-related information and the popularization of such information by the government can contribute to stabilizing public sentiments. However, the surge of public demand and the hysteresis of social support demonstrated that the allocation of medical resources was under enormous pressure in the early stage of the epidemic. It is suggested that the government should strengthen the response in terms of public opinion and epidemic prevention and exert control in key epidemic areas, urban agglomerations, and transboundary areas at the province level. In controlling the crisis, accurate response countermeasures should be formulated following public help demands. The findings can help government and emergency agencies to better understand the public opinion and sentiments towards COVID-19, to accelerate emergency responses, and to support post-disaster management.

Investigating the synergy between lean construction practices and post disaster management processes

Lean aims to maximize value while minimizing waste. Lean practices are likely to reduce the number potential hazards and errors. The use of Lean practices in construction is essential to experience less hazards. Benefitting from Lean practices has gained much attention in the last decade. Especially, the destroying effect of hazards and accidents is of utmost importance in terms of seeking for better strategies. Within this context, Lean practices offer a wide variety of advantages and provide means for achieving greater success in projects. This study investigates the use of Lean practices in post disaster management. Since post disaster management includes the activities to help community in rebuilding, Lean tools and techniques might be employed to better handle post disaster management processes. The study also scrutinizes the integration of Lean practices with the post disaster processes and encourages the community to compete against the destroying effect of disasters thanks to using Lean tools and techniques. The main contribution of this study is that it introduces Lean practices to be used in the post disaster management processes, which might potentially remove safety concerns in construction sites up to a great extent.

Strengths and Weaknesses of a Hybrid Post-disaster Management Approach: the Doce River (Brazil) Mine-Tailing Dam Burst.

Mine-tailing dam bursts occur frequently with attendant implications for the environment and human populations. Institutional preparedness for such events plays an important role in their lasting impact. This study analyzes the stakeholder engagement in the new governance framework created to recover the Doce River ecosystem following the 2015 disaster, where 34 million m3 of tailings were released, killing 19 people and causing massive impacts on riverine life. Following the disaster, poorly conceived political and management decisions impeded and continue to impede the progress of ecosystem recovery. The post-event management structure shows a centralized and poorly diverse stakeholder pool. We conclude that poor governance structure, and weak law enforcement, are among the main reasons preventing the Doce River post-disaster watershed recovery. A watershed vulnerability analysis combining dam stability and socioeconomic data, concluded that low ratings of socioeconomic performance substantially increase basin vulnerability. We recommend that the watershed committee should be fully involved in the implementation of the program and take a central role so that the most vulnerable communities (including indigenous people) take ownership of ecosystem recovery.

Aboveground Biomass Allometric Models for Evergreen Broad-Leaved Forest Damaged by a Serious Ice Storm in Southern China

A catastrophic ice storm occurred in the spring of 2008, which severely destroyed nearly 13% of China’s forests; among them, the broad-leaved forest suffered the most extensive damage. In this study, allometric models of the evergreen broad-leaved forests damaged at different recovery stages after the disaster were established to estimate the aboveground biomass of damaged trees. Plant plots were established and surveyed in damaged forests to determine species composition and diameter distribution, and finally a sample scheme was formulated that contained 47 trees from 13 species. The destructive measurements of aboveground biomass of trees selected according to the scheme were conducted in 2008, 2010, 2012 and 2016, respectively. Undamaged trees in the same region were also selected to measure the biomass in 2010. Linear regression of logarithmic transformation of the power function form was performed using Diameter at Breast Height (DBH) as predictor to develop biomass allometric models. The results showed that the ice storm caused tree aboveground biomass loss, which caused different parameters of the tree biomass models at different recovery stages. The models have a high accuracy in predicting trunk and total aboveground biomass, with high determination coefficients (R2, 0.913~0.984, mean 0.957), and have a relatively low accuracy in predicting the biomass of branches and leaves (R2, 0.703~0.892, mean 0.784). The aboveground biomass reduced by 35.0% on average due to the ice storm, and recovered to the same level of undamaged trees in the same diameter 8 years after the disturbance. The branches and leaves recovered very fast, and the biomass of these parts exceeded that of the undamaged trees, reaching the same diameter 2 years after the disaster, indicating an over compensatory growth. The trees with a smaller diameter were mostly composed of middle and late succession species, and recovered faster than other species, indicating that the ice storm may alter the forest structure and accelerate community succession. The biomass allometric models built in this study, combined with forest inventory data, can estimate forest biomass loss and recovery after disturbance, and offer an important sense of the assessment of forest damage and the formulation of forest post-disaster management strategies.

Determination of a Hazard Compensations Based on Land Administration Data

During the summer period, fire hazards represent an especially real threat not only for vegetation and animals, but also for material resources and human lives. In the Republic of Croatia, the most affected areas are those in the coastal region, which are characterised by a very dry environment and strong winds that are conducive to the rapid spread of wildfire. Although the prevention of wildfire is an utmost imperative, many countries are still missing a good and reliable fire management system for both fire monitoring and prevention and post-disaster management. The aim of this paper is to show the utilisation of open source GIS, web-based technologies, and open data in the case of natural disasters, and to define the concept of advanced national system of compensation determination. Such a system could be utilised to identify land parcels for which compensations for damage and losses after fire devastation have to be determined and could be used by compensation agencies, insurance companies, or state administration. The island of Hvar, which has suffered from fire hazards, was used as a case study for a quick and relevant calculation of the compensation based on land administration data. Sentinel satellite images and official digital orthophoto maps (DOMs) of the affected area before and after the fire devastation were obtained and analysed together with the data on cadastral parcels and land owners. Research was conducted by using both sets of imagery data, showing that Sentinel satellite images are more than adequate for fulfilling the task of the determination of hazard compensation.

RDSP: Rapidly Deployable Wireless Ad Hoc System for Post-Disaster Management.

In post-disaster scenarios, such as after floods, earthquakes, and in war zones, the cellular communication infrastructure may be destroyed or seriously disrupted. In such emergency scenarios, it becomes very important for first aid responders to communicate with other rescue teams in order to provide feedback to both the central office and the disaster survivors. To address this issue, rapidly deployable systems are required to re-establish connectivity and assist users and first responders in the region of incident. In this work, we describe the design, implementation, and evaluation of a rapidly deployable system for first response applications in post-disaster situations, named RDSP. The proposed system helps early rescue responders and victims by sharing their location information to remotely located servers by utilizing a novel routing scheme. This novel routing scheme consists of the Dynamic ID Assignment (DIA) algorithm and the Minimum Maximum Neighbor (MMN) algorithm. The DIA algorithm is used by relay devices to dynamically select their IDs on the basis of all the available IDs of networks. Whereas, the MMN algorithm is used by the client and relay devices to dynamically select their next neighbor relays for the transmission of messages. The RDSP contains three devices; the client device sends the victim’s location information to the server, the relay device relays information between client and server device, the server device receives messages from the client device to alert the rescue team. We deployed and evaluated our system in the outdoor environment of the university campus. The experimental results show that the RDSP system reduces the message delivery delay and improves the message delivery ratio with lower communication overhead.

Strengthening Post-Disaster Management Activities by Rating Social Media Corpus

In times of natural disasters such as floods, tsunamis, earthquakes, landslides, etc., people need information so that relief operations such as help can save many lives. The implications of using social media in post-disaster management are explored in the article. The approach has three main parts: (1) extraction, (2) classification, and (3) validation. The results prove that machine learning algorithms are highly reliable in elimination disaster non-related tweets and news posts. The authors strongly believe that their model is more reliable as they are validating the tweets using news posts by providing various ratings according to the trueness.

Evaluation of Post-Earthquake, Tsunami, and Liquefaction Disaster Waste Management in Palu

The Central Sulawesi region is known as the meeting place for three of the world's main tectonics. As a result, the region is prone to natural disasters, especially those caused by the movement of these plates, which has prompted a shift in the Palu-Koro Fault. This also caused the 7.4 magnitude earthquake that devastated the coastal area of Palu Bay on September 28, 2018. Post-disaster waste management is one of the most important operational management systems ever developed to help affected communities and restore conditions to a stable situation after the disaster. In this study, the estimation of disaster waste generation was carried. In addition, an evaluation of disaster waste management was carried out as well as the formulation of disaster waste management mitigation, particularly earthquake, tsunami and liquefaction disasters. The estimated generation of construction disaster waste is 80,894,4 m3 and non-construction waste is 52,305.6 m3. Disaster waste management evaluation indicate that the lowest value in the evaluation aspect is in the aspect of community participation (30%) and the Financing aspect (37.5%). The establishment of a disaster waste management system will focus on the preparation of technical guidelines and Standard Operating Procedures (SOPs) on disaster waste management.

Collaborative data acquisition and processing for post disaster management and surveillance related tasks using UAV-based IoT cloud

Rescue and recovery operations are very critical for post disaster management. For post disaster management and surveillance activities, there is a need of acquiring the information about the ground situation through sensing or observing data and identify the locations, which is a challenging task. These data may be sensed or observed through different types of sensors deployed in the area of interest. This can be achieved through a collaborative way of data acquisition and processing. In this context, this paper introduces the framework for data acquisition based on collaborative processing using unmanned aerial vehicles and internet of things network. The major contribution of this work is that the real time test bed using actual UAVs and IoT devices for data acquisition, clustering of different sensors and devices deployed for it and localising the sensors and different events or situations arising in the region of interest is detailed and demonstrated. The framework is evaluated using real test bed with drones (UAVs) and integrated with cloud platform in IoT-based environment for data storage of the acquired data for further analysis and effective decision making in disaster situations.