Post-disaster Relief Research Articles

How does climate change adaptation affect public budgets? Development of an assessment framework and a demonstration for Austria

Public adaptation to climate change affects government budgets directly on the expenditure side, but also indirectly via changes in the tax base and government consumption patterns. While such indirect effects have been analyzed intensively for mitigation policies, similarly detailed model-based frameworks and studies for adaptation policy are still missing. The objectives of the present paper are (i) to fill this gap by proposing a general modeling framework that allows for a comprehensive analysis of effects of adaptation on federal budgets, both on the expenditure and the revenue side, as well as of macroeconomic effects and (ii) to demonstrate its usefulness by applying this framework to the case of Austria. We find that public adaptation can lead to substantial positive macroeconomic effects on gross domestic product (GDP), welfare, and employment. The results are robust with respect to assumptions about the effectiveness of adaptation. Also, we demonstrate that it is essential for analysis to cover both the expenditure and revenue side, as overall government revenues can increase due to adaptation, offsetting additional direct public expenses for adaptation, thus increasing the budget balance. This is because of less severe climate change impacts and the corresponding lower payments for post-disaster relief and unemployment benefits as well as higher tax revenues. We thus strongly recommend making use of economy-wide modeling frameworks when planning for adaptation, as they shed light on the true costs and benefits of adaptation.

Disaster Governance and Community Resilience: The Law and the Role of SDMAs

Disasters have become more profound in intensity and frequent in occurrence due to climate change. The unpredictable and devastating consequences of rising global temperature has raised the alarm bells for ‘rapid and far-reaching’ transitions in land, energy and urban governance. The recent devastation due to floods in Kerala in August 2018, has brought disaster governance to the mainstream in government accountability. Despite high performance on Human Development Indicators and social infrastructure, the failure of the state government in ensuring adequate preparedness and mitigation through capacity building has pushed back the development of the state by decades. Trust deficit in the face of administrative negligence and executive callousness hindered the translation of scientific information into understandable warnings for first line responders. The havoc was significantly man made as the local administration failed to regulate blatant violations of Coastal Zone regulations even after repeated warnings from Western Ghats Ecology Expert Panel report. The focus of state policy on human development has ignored the crucial aspect of ensuring active citizen participation in the development process. This has resulted in citizens becoming passive recipients of state entitlements, rather than active agents in a democracy. This paper is a critical view on disaster policies in India, which continue to ignore the decentralized institutions as crucial institutions in disaster management. The laudable role of fishermen in rescue and relief in the aftermath of Kerala floods clearly emphasizes that communities can no longer be ignored in the framework of disaster cycle. In a country which witnesses ubiquitous ‘regime of noncompliance’ to building bye laws, coastal zone regulations, land use plans and other safety laws, decentralized disaster management can help in building community resilience and ensuring accountability and transparency of government institutions. The argument gets underscored in a scenario where institutions of Disaster Management continue to focus on post disaster relief and rehabilitation, due to lack of enforcement powers of disaster management institutions

UAV Autonomous Target Search Based on Deep Reinforcement Learning in Complex Disaster Scene

In recent years, artificial intelligence has played an increasingly important role in the field of automated control of drones. After AlphaGo used Intensive Learning to defeat the World Go Championship, intensive learning gained widespread attention. However, most of the existing reinforcement learning is applied in games with only two or three moving directions. This paper proves that deep reinforcement learning can be successfully applied to an ancient puzzle game Nokia Snake after further processing. A game with four directions of movement. Through deep intensive learning and training, the Snake (or self-learning Snake) learns to find the target path autonomously, and the average score on the Snake Game exceeds the average score on human level. This kind of Snake algorithm that can find the target path autonomously has broad prospects in the industrial field, such as: UAV oil and gas field inspection, Use drones to search for and rescue injured people after a complex disaster. As we all know, post-disaster relief requires careful staffing and material dispatch. There are many factors that need to be considered in the artificial planning of disaster relief. Therefore, we want to design a drone that can search and rescue personnel and dispatch materials. Current drones are quite mature in terms of automation control, but current drones require manual control. Therefore, the Snake algorithm proposed here to be able to find the target path autonomously is an attempt and key technology in the design of autonomous search and rescue personnel and material dispatching drones.

Post-disaster map builder: Crowdsensed digital pedestrian map construction of the disaster affected areas through smartphone based DTN

Constructing digital pedestrian maps of the disaster affected areas is a vital requirement for post-disaster relief operation. Digital maps help disaster management agencies in decision making and mobilizing the manpower and resources in the disaster affected areas. However, due to the intermittent connectivity after large scale natural disasters, the existing web based digital mapping systems remain inaccessible in the disaster affected areas. Moreover, the road networks in such areas change drastically because of disaster (for instance waterlogging due to flood, structural collapse or incidental destructions like a landslide). Hence, existing analog (paper based) route maps of such areas become obsolete. In this work, we propose Post-Disaster Map Builder, a crowdsenced digital pedestrian map construction system over smartphone based DTN. The proposed system generates digital pedestrian maps of the disaster affected areas using battery powered mobile handheld devices. Here, trajectory traces collected by the volunteers are periodically shared among the other volunteers of the disaster affected area through smartphone based DTN. Pedestrian maps of the disaster affected areas are gradually constructed in the mobile handheld devices of the volunteers by combining the collected traces over time. The proposed system is thoroughly evaluated through simulation and testbed implementation. The results reveal that our proposed system can automatically construct digital pedestrian maps of disaster affected areas with high accuracy at the cost of marginal delay.

Dynamic Integrated Model for Disaster Management and Socioeconomic Analysis (DIM2SEA)

The DIM2SEA research project aims to increase urban resilience to large-scale disasters. We are engaged in developing a prototype Dynamic Integrated Model for Disaster Management and Socioeconomic Analysis (DIM2SEA) that will give disaster officials, stakeholders, urban engineers and planners an analytic tool for mitigating some of the worst excesses of catastrophic events. This is achieved by harnessing state-of-the-art developments in damage assessment, spatial simulation modeling, and Geographic Information System (GIS). At the heart of DIM2SEA is an agent-based model combined with post-disaster damage assessment and socioeconomic impact models. The large amounts of simulated spatial and temporal data generated by the agent-based models are fused with the socioeconomic profiles of the target population to generate a multidimensional database of inherently “synthetic” big data. Progress in the following areas is reported here: (1) Synthetic population generation from census tract data into agent profiling and spatial allocation, (2) developing scenarios of building damage due to earthquakes and tsunamis, (3) building debris scattering estimation and road network disruption, (4) logistics regarding post-disaster relief distribution, (5) the labor market in post-disaster urban dynamics, and (6) household insurance behavior as a reflection of urban resilience.

A robust model predictive control approach for post-disaster relief distribution

Emergency distribution is an important aspect of disaster response. However, when planning such activities, decision makers should consider not only uncertain and dynamic input data such as supply and demand, but also the real-time adjustment requirements of the existing distribution plans that account for the deviation between the predicted and actual (or observed) values of the input data. Consequently, we present a multi-commodity, multi-period distribution model that considers both relief commodities and injured people to minimize the total weighted unmet demand throughout the planning horizon. Furthermore, we propose a rolling horizon-based framework, based on the robust model predictive control (RMPC) approach, to obtain robust relief distribution plans and adjust them in accordance with updated real-time information. We then use a numerical example based on the Great Wenchuan Earthquake that occurred on May 12, 2008, in Sichuan Province, China, to investigate the application of our proposed model and framework, and we perform a detailed analysis of the influence of the settings of robust optimization parameters.

Post-disaster multi-period road network repair: work scheduling and relief logistics optimization

We develop a multi-period bi-level programming model for the post-disaster road network repair work scheduling and relief logistics problem. A maximum relative satisfaction degree-based steady-state parallel genetic algorithm is designed to solve this model. In order to validate and test the effectiveness of the presented mathematical model and method, we use a network generator to create numerical examples with different scales and characteristics of road network. Our numerical analysis of the solutions shows that the proposed mathematical model and method can effectively assist the decision-makers to deal with the road network repair work scheduling and relief logistics optimization problem during the emergency response phase. This mathematical model and the approach being developed are applied to deal with the case of Wenchuan earthquake in China. The results show that the required CPU time is short enough such that it meets the time limitation in the emergency response phase, and the strategy of road network repair scheduling will allow repair of the damaged roads to be completed before the end of the planning time horizon by 14.93%. Furthermore, the strategy of relief logistics can provide an efficient relief allocation and transportation path.

Exploitation of Social Media for Emergency Relief and Preparedness: Recent Research and Trends

Online Social Media, such as Twitter, Facebook and WhatsApp, are important sources of real-time information related to emergency events, including both natural calamities, man-made disasters, epidemics, and so on. There has been lot of recent work on designing information systems that would be useful for aiding post-disaster relief operations, as well as for pre-disaster preparedness. A special issue on “Exploitation of Social Media for Emergency Relief and Preparedness” was conducted for the journal Information Systems Frontiers. The objective of this special issue was to present a platform for dissemination of the empirical results of various technologies for extracting vital and actionable information from social media content in disaster situations. The papers included in this issue are expected to be the stepping stones for future explorations and technical innovations towards technologies meant for utilizing various online and offline information sources for enhancing pre-disaster preparedness and post-disaster relief operations.

The geo-genetic status of earthquake-related hazards and the role of human and policy dimensions in impact mitigation

ABSTRACTEarthquakes occur in and around active lithospheric-plate margins that are located both in diverging and converging plate-settings. New fault rupture or old active fault reactivation can cause ground shaking, land deformation, land subsidence, landslides, and mass avalanches. The relevant locations of major risk centres around the world are well known. However, the parameters such as trend, pattern, frequency and magnitude are highly variable and very difficult to determine and predict. One result is that many fundamental and universal scientific achievements for mitigating earthquake hazard have often failed in protecting and safe-guarding human life and property. Although naturally occurring events cannot be prevented nevertheless loss of life and property damage could be minimized if an integrated effort and using the wisdom of the relevant professionals are practiced. A paradigm shift of the culture from post-disaster relief and rehabilitation to pre-disaster preparation and practice would reduce the level of destruction from an impending earthquake. Requisite pre-disaster physical planning and appropriate building measures, and pre- and post-disaster risk management should give special attention to public awareness programmes for loss mitigation. However, the fundamental requirement for such achievement is dedicated approach and commitment from the planner, practitioners and end-users of risk mitigation actions.

Integrated post-disaster medical assistance team scheduling and relief supply distribution

Purpose The purpose of this paper is to solve a post-disaster humanitarian logistics problem in which medical assistance teams are dispatched and the relief supplies are distributed among demand points. Design/methodology/approach A mixed integer-programming model and a two-stage hybrid metaheuristic method are developed to solve the problem. Problem instances of various sizes as well as a numerical example based on the 2016 Kyushu Earthquake in Japan are used to test the proposed model and algorithm. Findings Computational results based on comparisons with the state-of-the-art commercial software show that the proposed approach can quickly find near-optimal solutions, which is highly desirable in emergency situations. Research limitations/implications Real data of the parameters of the model are difficult to obtain. Future collaborations with organizations such as Red Cross and Federal Emergency Management Agency can be extremely helpful in collecting data in humanitarian logistics research. Practical implications The proposed model and algorithm can help governments and non-governmental organizations (NGOs) to effectively and efficiently allocate and coordinate different types of humanitarian relief resources, especially when these resources are limited. Originality/value This paper is among the first ones to consider both medical team scheduling (routing) and relief aid distribution as decision variables in the humanitarian logistics field. The contributions include developing a mathematical model and a heuristic algorithm, illustrating the model and algorithm using a numerical example, and providing a decision support tool for governments and NGOs to manage the relief resources in disasters.

DPDRM: A decentralized post-disaster resource management scheme using energy efficient smart phone based DTN

Large-scale natural disasters affect traditional communication infrastructure due to incidental destructions and damages. Delay-tolerant networks (DTN) proves to be a viable option for exchanging information during such scenarios. Different disaster management agencies setup camps and mobilize their resources and manpower in the affected areas after the disaster. Resource management is an integral part of post-disaster relief operation. Each camp manages its own resource inventory. Periodic sharing of resource inventories from different camps is essential for maintaining a near-accurate unified global inventory at each node that enables successful search and retrieval of resource requests (queries). But, due to the intermittent network connectivity, maintaining such a consistent resource inventory and successful search/retrieval of queries becomes challenging. We map this post-disaster resource management as a content sharing problem with strict time constraint and propose an automated, energy efficient and decentralized resource management scheme over DTN. The proposed scheme allows periodic group based sharing of resource inventories and supports search and retrieval of queries within a restricted time period. Here, resource inventories are shared among the group members by employing publish-subscribe mechanism. Moreover the queries are propagated to the group members based on the probability of encounters. This probabilistic propagation controls the unintended replication of query. Finally, the proposed scheme is evaluated through ONE simulator based on a realistic post-disaster scenario of the 2015 Nepal earthquake. The results reveal that our proposed scheme outperforms one of the state-of-the-art content sharing schemes, discover-predict-deliver in terms of response delivery, average energy consumption and average delivery latency.

State, profession, and religion: Reflecting on spirituality and indigenous social work in China in the Yushu Earthquake Relief

ABSTRACTOn April 14, 2010, a massive earthquake measuring 7.1Ms struck the Yushu Tibetan Autonomous Prefecture, Qinghai Province, China. Its scale notwithstanding, it has received much less national and international attention than the also immense Wenchun quake of 2008 in Sichun Province. This field report discusses the contribution of religion and spirituality in postdisaster relief in Yushu. It also calls for critical reflection on the issue of homogenization in the discussion of indigenous social work in China, and perhaps in other multiethnic countries in the world.

Resilience, Female Altruism, and Bodily Autonomy: Disaster-Induced Displacement in Post-Haiyan Philippines

AbstractNatural disasters are increasingly causing displacements globally, and such negative impacts of climate change are expected to increase exponentially. Women and girls in particular distinctly endure long-term or gradual harms while in displacement, such as heightened risks of sexual and gender-based violence, including exposure to sexually transmitted infections and HIV/AIDS, maternal mortality, and forced or unwanted pregnancies. This article examines the Philippines as a case study to unveil the growing gendered security threats embodied by disaster-induced displacements. In the aftermath of the disaster caused by supertyphoon Haiyan, which struck in November 2013, the mantra of developing a “disaster-resilient nation” has gained currency among national and international actors in the country. Building on critical feminist political economy analysis, this article argues that the Haiyan postdisaster relief and reconstruction efforts constitute gendered processes that intimately rely on and mobili...

The 2015 Nepal Earthquake Disaster: Is the Threat of Occurrence of Communicable Disease Epidemic Over?

This opinion paper highlights the state of public health assessment in evacuation centers following the 2015 Nepal earthquake. It also suggests an approach to reinforce risk assessment and surveillance of communicable diseases (CD) in remote Nepalese districts. A short surveillance research was conducted on outbreaks of infectious diseases in Nepal in the post-2015 earthquake in evacuation centers in Kathmandu and Dhading districts. In collaboration with the Nursing Association of Nepal
 (NAN), the researchers have established a monitoring and surveillance system, named ‘EpiNurse’ program, in remote Nepalese districts. Periodic shelter to shelter visits, CD risk assessment and relief needs inventory in local communities are implemented, whereas health events with a potential to cause a CD outbreak are being reported to governmental agencies and health clusters involved in post-disaster relief in Nepal. Several cases of diarrheal diseases were identi fied in Nepalese districts
 after the 2015 earthquake, suggesting the existence of potential risk for the occurrence of new CD epidemics. Onsite risk assessment and monitoring of the effectiveness of actions and interventions implemented, as well as improvement of risk communication between relief agencies should be expanded to less resourced districts to reduce the risk of CD outbreak occurrence.

Engineering blood stem cells for autologous transplants for lysosomal diseases: Correction of mucopolysaccharidosis type I using genome-edited hematopoietic stem and progenitor cells

In this paper, a novel multi-stage possibilistic stochastic programming (MSPSP) approach is developed. At the early stages, there is little information on how to pursue MSPSP the optimal decision, while in the course of time, new series of information become unfolded, which further necessitate the model to be updated. Compared to other competent uncertainty programming approaches (i.e. the classical two-stage stochastic programming (TSSP) and multi-stage stochastic programming (MSSP), and enhanced versions of TSSP and MSSP approaches), the proposed MSPSP method relaxes the subjectively setting the deterministic occurrence probabilities and allows possible perturbation in their values as fuzzy probabilities. It further enables these values to be updated so that the updated values do not conflict with each other and their summation does not exceed one. Also, given the existence of several sources of uncertainty and the deep level of uncertainty in the early stages, the scenario dependent variables at such stages are treated as random fuzzy variables. Finally, it takes into account the effect of unfolded information in an on-going fashion in the course of time. Applicability of the proposed approach is tested on a real case study for the post-disaster relief distribution planning in Tehran. Finally, useful managerial insights are provided through conducting several sensitivity analyses.

Toward healthy housing for the displaced

The population of people living in temporary settlements after disasters is in the millions and the average stay in these settlements exceeds a decade. This paper reviews the literature on the design of post-disaster relief shelters in order to: establish the state of the art, identify trends and describe the academic activity of the past forty years. The analysis demonstrates that the academic engagement in this topic is limited, with fewer than sixty publications in the past four decades. Displacement camps are often situated in countries with extreme climates; however the issue of the thermal performance of shelters and their impact on health is found to be further overlooked. In an attempt to rebalance this situation, thermal surveys were conducted in two refugee camps in Jordan. The study found that the refugees were very unsatisfied with the thermal conditions in their shelters, particularly in summer. Internal surface temperatures of 46°C were recorded in September and indoor CO2 concentration levels of 2700 ppm were measured in winter. In addition, the study reported on the adaptation strategies used by refugees to cope with the heat and cold, and on their views on shelter design considerations and satisfaction.

Through Wall Human Detection Under Small Samples Based on Deep Learning Algorithm

Through-wall human detection has vital and widely used applications for anti-terrorism, anti-explosion, and post-disaster relief. The through-wall human-target recognition using ultra-wideband radar-based technology was established in recent research. With the recent development of deep learning algorithms, classification algorithms have demonstrated a dynamic aptitude to learn important characteristics of the dataset by utilizing only a few sample sets. This paper focuses on studying the detection of a human target’s status behind wall in small sample conditions. In the deep learning network model, the autoencoder algorithm is chosen here to classify and identify human targets behind walls. Through automatic acquiring of the knowledge of inherent characteristics in the data, the autoencoder algorithm can extract the concise data-feature representations. Based on the autoencoder network, we add the denoising encoder and sparsity constraints to extract more efficient feature representations, thereby improving the classification and identification rates. In this paper, we classify and identify the behind-wall human-target states separately under single and multiple sensors under a small-sample condition, and then compare the results with those of other classification algorithms. The results illustrate that the use of multiple sensors is more effective than the use of a single sensor and that the adopted autoencoder algorithm enables more effective detection of human targets behind walls than other algorithms.

A fast heuristic for routing in post-disaster humanitarian relief logistics

Abstract In the last decades, natural disasters have been affecting the human life of millions of people. The impressive scale of these disasters has pointed out the need for an effective management of the relief supply operations. One of the crucial issues in this context is the routing of vehicles carrying critical supplies and help to disaster victims. This problem poses unique logistics challenges, including damaged transportation infrastructure and limited knowledge on the road travel times. In such circumstances, selecting more reliable paths could help the rescue team to provide fast services to those in needs. The classic cost-minimizing routing problems do not properly reflect the relevant issue of the arrival time, which clearly has a serious impact on the survival rate of the affected community. In this paper, we focus specifically on the arrival time objective function in a multi-vehicle routing problem where stochastic travel times are taken into account. The considered problem should be solved promptly in the aftermath of a disaster, hence we propose a fast heuristic that could be applied to solve the problem.

Post-conflict infrastructure rehabilitation requirements

Post-disaster relief and reconstruction are always complex and challenging, requiring an integrated infrastructure and community rehabilitation process. These challenges are even more pronounced in post-conflict situations, where combatants must be reintegrated into normal community life, reconciliation progressed and confidence in institutions restored. When the conflict is prolonged, the transition to normalcy is yet more challenging to a generation or more of the working-age population who have not experienced normal working life prior to the conflict. As integrated systems, the infrastructure needs not only to be rehabilitated within the overall rehabilitation of society but also to provide a vehicle to facilitate the reintegration of the population and the socio-economic transition of the productive capacity of society. Infrastructure rehabilitation is as much about how it is used and owned by the community as the restoration of functionality. This paper explores the nature of post-conflict rehabilitation today, discusses how the existing transition paradigms apply and identifies how the paradigms may better deliver post-conflict infrastructure rehabilitation.

Participatory action research: working beyond disaster toward prevention

Drawing on qualitative analyses of post-disaster effects on vulnerable rural communities in Pakistan, this paper puts forward a participatory action research stance as a next step or approach toward prevention. This approach stands in contrast to the current post-disaster relief model commonly practiced by many non-government organizations (NGOs). The participatory action research perspective was used to examine the qualitative evidence and provide a framework to effectively work with rural vulnerable communities. Qualitative analyses documented community wisdom and ideas toward prevention which, when potentially partnered with resources offered by NGOs, could dramatically reduce their vulnerability to natural disasters.