Natural Disaster Response Research Articles

Budget allocation of food procurement for natural disaster response

This paper studies a variant of the lot sizing problem that arises in the context of disaster management. In this problem, a fixed budget has to be allocated efficiently over multiple time periods to procure large quantities of a staple food that will be stored and later delivered to people affected by disaster strikes whose numbers are unknown in advance. Starting from the deterministic model where perfect information is assumed, different formulations to address the uncertainties are constructed: classical robust optimisation, risk-minimisation stochastic programming, and adjustable robust optimisation. Experiments conducted using data from West Java, Indonesia allow us to discuss the advantages and drawbacks of each method. Our methods constitute a toolbox to support decision makers with making procurement decisions and answering managerial questions such as which annual budget is fair and safe, or when storage peaks are likely to occur.

Signal Novelty Detection as an Intrinsic Reward for Robotics.

In advanced robot control, reinforcement learning is a common technique used to transform sensor data into signals for actuators, based on feedback from the robot's environment. However, the feedback or reward is typically sparse, as it is provided mainly after the task's completion or failure, leading to slow convergence. Additional intrinsic rewards based on the state visitation frequency can provide more feedback. In this study, an Autoencoder deep learning neural network was utilized as novelty detection for intrinsic rewards to guide the search process through a state space. The neural network processed signals from various types of sensors simultaneously. It was tested on simulated robotic agents in a benchmark set of classic control OpenAI Gym test environments (including Mountain Car, Acrobot, CartPole, and LunarLander), achieving more efficient and accurate robot control in three of the four tasks (with only slight degradation in the Lunar Lander task) when purely intrinsic rewards were used compared to standard extrinsic rewards. By incorporating autoencoder-based intrinsic rewards, robots could potentially become more dependable in autonomous operations like space or underwater exploration or during natural disaster response. This is because the system could better adapt to changing environments or unexpected situations.

An outbreak of acute neurological illness associated with drinking water source following a cyclone in Eluru, West Godavari district, Andhra Pradesh, India, December 2020

BackgroundIn December 2020, over 500 residents of Eluru City were hospitalised with seizures and sudden loss of consciousness (LOC) resembling the neurotoxic effects of organochlorine poisoning after a flooding event during the last week of November 2020. We described the epidemiological investigation of outbreak and identified risk factors. MethodsWe performed descriptive analysis followed by 1:1 unmatched case-control study. Cases were identified through house-to-house search and review of medical records at district hospital. A case defined as sudden onset LOC or new-onset seizures in an Eluru resident aged ≥1 year, December 1–15, 2020 and a control as absence of neurological symptoms in a person aged ≥1 year selected randomly from same administrative division of the case. We compared cases and controls for possible risk factors and calculated adjusted odds ratio (aOR) with 95% confidence interval (CI). Biological and environmental samples were tested for contaminants. ResultsWe identified 545 cases (56% males), including one death. Seizures were reported in 491 (90%) cases. Median age was 27 years (interquartile range: 17–37 years) and 480 (88%) cases resided in urban area. Cases were clustered in administrative divisions supplied by municipal water reservoirs. Cases were more likely than controls to use municipal water as primary source of drinking water (aOR = 4.6, 95% CI = 1.6–13.0). High levels (average: 14.6 mg/l) of organochlorine compounds were detected in all municipal water samples (acceptable limit: <0.001 mg/l). ConclusionThis investigation highlights water ingestion as an exposure pathway for environmental contaminants (organochlorines) in the community after largescale flooding. We recommended strengthening safe water surveillance in natural disaster response contingency plans in Eluru.

Big Data, Small Island: Earth Observations for Improving Flood and Landslide Risk Assessment in Jamaica

The Caribbean region is highly vulnerable to multiple hazards. Resultant impacts may be derived from single or multiple cascading risks caused by hydrological-meteorological, seismic, geologic, or anthropological triggers, disturbances, or events. Studies suggest that event records and data related to hazards, risk, damage, and loss are limited in this region. National Disaster Risk Reduction (DRR) planning and response require data of sufficient quantity and quality to generate actionable information, statistical inferences, and insights to guide continual policy improvements for effective DRR, national preparedness, and response in both time and space. To address this knowledge gap, we review the current state of knowledge, data, models, and tools, identifying potential opportunities, capacity needs, and long-term benefits for integrating Earth Observation (EO) understanding, data, models, and tools to further enhance and strengthen the national DRR framework using two common disasters in Jamaica: floods and landslides. This review serves as an analysis of the current state of DRR management and assess future opportunities. Equally, to illustrate and guide other United Nations Disaster Risk Reduction (UNDRR) priority countries in the Pacific region, known as Small Island Developing States (SIDS), to grapple with threats of multiple and compounding hazards in the face of increasing frequency, intensity, and duration of extreme weather events, and climate change impact.

Tsunami Vulnerability Assessment and Multi-Criteria Decision Making Analysis of Eastern Coast of India Using GIS-Based Tools

The marine environment is a vital resource that sustains many people and a variety of ecosystems rich in biodiversity. However, coastal resources and human dwellings are exposed to natural calamities like tsunamis. This article explains a GIS-based multi-criteria study of tsunami exposure along India's eastern coast. Six geographical parameters were employed to determine the research area's vulnerability: elevation, slope, population density, coastal proximity, land use land cover (LULC), and flow accumulation. Additionally, the Analytic Hierarchy Process (A.H.P.) was used to weight the characteristics' criteria. Comparing the vulnerability map to the other parameter maps reveals that highly vulnerable locations are either densely populated or with lower surface elevation or agricultural. GIS-based analyses can assist in various disaster assessments and expedite regional planning for natural disaster prevention and response, like tsunamis. The tsunami susceptibility mapping is expected to aid in the initial stages of tsunami mitigation and management measures along the eastern coast of India in the case of a future tsunami. Moreover, connectivity and safe locations map with primary roads, secondary roads, railway routes, and possible safe locations (high elevation areas) is also mapped to ease the mitigation during an actual tsunami.

Extending scene-to-patch models: Multi-resolution multiple instance learning for Earth observation

Abstract Land cover classification (LCC) and natural disaster response (NDR) are important issues in climate change mitigation and adaptation. Existing approaches that use machine learning with Earth observation (EO) imaging data for LCC and NDR often rely on fully annotated and segmented datasets. Creating these datasets requires a large amount of effort, and a lack of suitable datasets has become an obstacle in scaling the use of machine learning for EO. In this study, we extend our prior work on Scene-to-Patch models: an alternative machine learning approach for EO that utilizes Multiple Instance Learning (MIL). As our approach only requires high-level scene labels, it enables much faster development of new datasets while still providing segmentation through patch-level predictions, ultimately increasing the accessibility of using machine learning for EO. We propose new multi-resolution MIL architectures that outperform single-resolution MIL models and non-MIL baselines on the DeepGlobe LCC and FloodNet NDR datasets. In addition, we conduct a thorough analysis of model performance and interpretability.

Between Policy Goals and Actual Outcome: An Assessment of the National Disaster Management Framework in Nigeria, 2010-2020

In Nigeria, research on disaster management has witnessed tremendous attention in recent years. However, no known study has directly assessed the country’s main disaster management policy framework. Introduced in 2010 to replace the National Disaster Response Plan, the National Disaster Management Framework (NDMF) represents Nigeria’s attempt to depart from a response-driven system to a proactive and comprehensive one within its federal political structure having three levels of government. In assessing the Framework, two sets of criteria were used. The first is in line with its pre-determined goals while the second set was drawn from the ecological context of the Framework. Data for the study were generated from both primary and secondary sources. In-depth interviews were conducted with two senior officials of the National Emergency Management Agency (NEMA); and General Managers of the State Emergency Management Agency of Oyo, Ekiti, Lagos, Osun, and Ogun States. Besides, the NDMF and other secondary materials were read and critically reviewed to generate secondary data. Findings show that despite over a decade since its introduction, the NDMF has not been able to engender the realization of a holistic disaster management system in the country. Rather, it generated rivalry among government agencies assigned to manage disasters. The perceived alienation of many stakeholders especially at the State level in its formulation process; poor funding and lack of political will to execute the provisions of the Framework are notable causes. Knowing the importance of a policy framework in actualizing the goals of disaster management, the paper recommends that stakeholders in the country, especially those at the state and local government levels should be repositioned to play more active roles in the operationalization and implementation of the NDMF.

네트워크 분석기반 주요 기후변화 영향 영역 식별 및 적응 관련 법령 분석1)

Climate change affects numerous aspects of our lives and threatens human society in diverse pathways. Climate crisis comprises both direct impacts in nature such as extreme weather, natural disaster, and sea-level rise and indirect impacts of the cascading events such as health problems, food shortage, and degradation of the human settlement. In order to effectively minimize series of damage from these interlinked climate impacts, we need to intervene at the starting point of climate change impact chains or the center of an impact network. This study investigated the impact chains of climate change based on the text analysis of IPCC assessment reports 1 ~ 6 and identified the five major climate impact areas of water resource management, coastal ecosystem integrity, protecting health from climate impacts, natural disaster response, and food supply. These five areas are of top priority policy in climate change adaptation, and we analyzed the fidelity of the corresponding laws with regard to mitigating vulnerability. The current climate change framework acts address the priority areas and the individual laws of various subject areas are supposed to supplement the framework acts. However, review of the individual laws in the priority areas suggest lack of proper reflection of climate vulnerability analysis results and the need for amendments to better mitigate future vulnerability.

Toward development of comprehensive national disaster response plans: an evaluation of Nigeria's national disaster response plan, 2002

PurposeDevelopment of a disaster response plan at a national level is regarded as the best way to undertake country-level disaster management in a well-coordinated manner, involving all stakeholders in well-built networks. Where developed national disaster response plans (NDRPs) are to be subjected to evaluative assessments from time to time to establish their strengths and weaknesses in disaster response processes. Only about 20 of the world's 195 countries have NDRPs, and only three of them have undergone some form of evaluation. This paper contributes toward filling this gap by evaluating the NDRP of the Federal Republic of Nigeria. Lessons from the implementation of this NDRP will help guide the development of the plans in countries where they do not yet exist.Design/methodology/approachNetwork and qualitative content analysis was employed to subject the Nigerian NDRP to critical evaluation using standards already established in the literature for the development of management networks and their use in disaster response. The experiences of other countries with developed NDRPs were also used to guide an effective evaluation process.FindingsFindings of the study show that the Nigerian NDRP is deficient in many respects, particularly in the areas of weak development of networks, absence of roles-differentiation for various stakeholders, lack of specified achievable and hence monitorable targets, objectives and outcomes and absence of detailed response plans for specific disaster types. The plan needs to therefore be subjected to a detailed multidisciplinary and multi-stakeholder review to get it strengthened.Originality/valueThis is the first attempt to conduct an evaluative assessment of Nigeria's NDRP and the 4th in the world. The study's findings can help not only to strengthen Nigeria's NDRP, but also to develop similar plans in other countries around the world.

An Investigation of Cloudburst-triggered Landslides and Flash Floods in Arakot Region of Uttarkashi District, Uttarakhand

Abstract A cloudburst event occurred at around 04:00hrs (IST), on 18, August 2019, elicited landslides and flash floods, at Tikochi and Makudi village under the Arakot region of Uttarkashi District, Uttarakhand, India. This heavy rain has caused a massive flash flood in Arakot Nala and induced a major landslide downstream. This flash flood has caused the death of 19 people, 8 were missings, and 15 were injured, affecting 38 villages (70 km2 area) and stranding more than 400 people at various places. These flash floods have caused irreparable damage to the livestock, infrastructures, environment, and properties in the Uttarakhand and Himachal states and estimated economic losses of about Rs ~175 crores. During the same period, heavy rainfall also made havoc across north India. Meanwhile, the rescue and evacuation process in the region has been carried out by the Indo-Tibetan Border Police (ITBP), National Disaster Response Force (NDRF), and State Disaster Response Force (SDRF). However, it will take a lot of time to bring the situation in the region to normalcy. The study has suggested that pragmatic check dam policy and logical shifting of the people should be exercised in a safe and stable area before the onset of the monsoon season in the hazard-prone areas. An early warning system (EWS), installation of Doppler radar, and lightning sensors, which would be helpful for policymakers, planners, practitioners, and technologists will aid to minimize the disaster risk and sustainable development within the region. Although an EWS alone can not stop a similar type of disaster but would be very helpful in minimizing the loss of human lives, livestock, biodiversity, infrastructure (civilian and strategic), and land holding in the hilly region.

SIGAP Application Quality Through Webqual Analysis

In this study, the SIGAP application is deemed necessary to be investigated and dissected more deeply, because in the last few years there are still many people who do not know the functions and uses of the SIGAP application as the first natural disaster response application in Sidoarjo Regency. This study uses quantitative methods and uses an approach through statistical data. This study focuses on volunteers who have downloaded the SIGAP application. Researchers set 88 people as a sample to be later developed into statistical data. By using the technique of Non Probability Sampling, data collection in this study using questionnaires and observations. This study uses descriptive statistical analysis techniques. The resulting quality of the total value reached 75.4%. the interaction dimension got an average effect of 75.7%, and the overall dimension got an average value of 75.5%. These four dimensions fall into the Good category. This can be interpreted that the overall quality of the Spry application in the community is good. The Sigap application for disaster information is of good quality which is presented to disaster response volunteers and the community in general. In addition, valid information, which can be accessed by all people and receiving very fast responses or responses from the Sidoarjo Regency BPBD, is a boost to public confidence that the Sigap application is a good disaster application in Sidoarjo Regency.

Disaster Information Verification and Validation Application Using Machine Learning

Social media platforms have made it possible for people and organizations to disseminate information to their peers and target markets. Even while most information is shared with the best of intentions, some people utilize social media to further their own agendas. They might publish untrue or inaccurate information in their posts. Before, during, and after disasters and emergencies, social media is rife with rumors, misinformation, and misleading information. These false rumors and information could also make individuals anxious. How to stop the spread of this incorrect information is one of the main problems that public safety authorities and organizations face. DIVVA is a system that, using some provided input text data, evaluates and validates disaster-related information. The system has two tracks: a validation track and a verification track. Verification will classify the textual inputinto categories related to disasters or not related to disasters. The Validation track, on the other hand, will use the official handles of government disaster relief organizations like the NDRF (National Disaster Response Force) to determine whether the event mentioned in the text data actually happened or not before classifying the disaster- related data as real or fake. Therefore, if many individuals receive erroneous information about a calamity, we can utilize our approach to determine if the information is true or false. Our results show that the Bidirectional LSTM model performs well for the tweet classification (i.e. whether the tweets are related to disaster or not) task with 84% accuracy.

Decision Support System for Natural Disaster Response Process with Twitter Data

Doğal afetlere müdahale sürecinde kısıtlı kaynakların yerinde ve zamanında kullanılması can ve mal kaybını sınırlamada çok önemlidir. Afete zamanında müdahale etmek ve kaynakları sevkini yönetmek amaçlarıyla afet koordinasyon merkezleri oluşturulur. Afet sırasında, bireyler ve kurumlar sosyal medya üzerinden anlık bilgi verici paylaşımlar yapılmaktadır. Paylaşılan bilginin hızlı ve doğru şekilde afet koordinasyon ekiplerine iletilmesi, ekiplerin yönetimine fayda sağlayacaktır. Bu amaçla, bu çalışmada Twitter üzerinden yapılan paylaşımları metin madenciliği ile işleyerek, SMS ve e-posta ile uyarı oluşturan bir karar destek sistemi tanıtılmıştır. Karar destek sistemi tarafından, Twitter’dan elde edilen her tweet için yer, tarih ve saat bilgisiyle birlikte, içerik analizi yapılmış ve risk puanı hesaplanmıştır. Her saat yenilenen risk puanlarıyla, kritik durumdaki lokasyonlar için uyarı oluşturulmuştur. Çalışmada, 2021 Temmuz ve Ağustos aylarında Manavgat, Marmaris ve Milas’ta çıkan orman yangınları sırasında paylaşılan 120,000’den fazla tweet ile karar destek sisteminin uygulaması gösterilmiştir. Uygulamada, Marmaris Hisarönü, Milas Çökertme gibi yangından çok etkilenen yerler için yüksek risk puanının oluşturulduğu görülmüştür.

Early Estimation of Heavy Rain Damage at the Municipal Level Based on Time-Series Analysis of SNS Information

To carry out natural disaster response, restoration, and reconstruction, it is important to efficiently and quickly assess the damage caused by the natural disaster. The existing evidence demonstrates that when a natural disaster occurs, social networking services (SNS) information is amplified significantly, compared to normal times. Specifically, the damage caused by a natural disaster tends to cover a wide area and have a large scale. Additionally, it may vary considerably depending on the municipality. Thus, this study investigates whether the utilization of this amplified SNS information can offer an effective approach for real-time evaluation and monitoring of the damage caused by a natural disaster in municipal units. To this end, focusing on time-series changes in SNS information, we propose a general-purpose analysis method of SNS information for evaluating the damage caused by a natural disaster in real time in municipal units. Using real-world data twitter data, we investigate the case of Kumamoto Prefecture, which experienced heavy rain in July 2020 and July 2021, to verify the proposed analysis method.

Plan the work, work the plan: An introduction to the National Park Service Climate, Science, and Disaster Response Program

The climate crisis poses significant and unprecedent threats to the resources stewarded by the National Park Service (NPS). Some impacts are already apparent, while understanding of other outcomes is still developing. While the rate and magnitude of climate change ultimately depends on worldwide management of greenhouse gas emissions, resource managers today face choices about what actions to take, despite the uncertainty. To support the mission of NPS and its cultural resource preservation goals, the Climate, Science, and Disaster Response (CSDR) Program has been developed to explore climate impacts, provide cultural resource expertise, and expand and accelerate initiatives related to cultural resources and climate change adaptation. Here we introduce the construct of the CSDR program, share the components of the program’s 2022–2025 Action Plan, and highlight initial activities.

Prevalence of Depression and Posttraumatic Stress Disorder in Flint, Michigan, 5 Years After the Onset of the Water Crisis

Environmental disasters, such as the Flint, Michigan, water crisis, are potentially traumatic events (PTEs) that may precipitate long-term psychiatric disorders. The water crisis was associated with acute elevations in mental health problems in the Flint community, but long-term psychiatric sequelae have not yet been evaluated using standardized diagnostic measures. To investigate the prevalence of and factors associated with current presumptive diagnostic-level major depression and posttraumatic stress disorder (PTSD) among Flint residents 5 years after the onset of the crisis. In this cross-sectional study, a household probability sample of 1970 adults living in Flint, Michigan, during the crisis were surveyed about their crisis experiences, their psychological symptoms 5 years later, and their access to and use of mental health services in the intervening years. Analyses were weighted to produce population-representative estimates. Presumptive Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) diagnostic-level past-year major depression and PTSD. Among 1970 respondents, 1061 of 1946 reporting sex (54.5%) were women; 1043 of 1951 reporting race (53.5%) were Black or African American and 829 (42.5%) were White; and 1895 of 1946 reporting ethnicity (97.4%) were non-Hispanic. Overall, 435 (22.1%) met DSM-5 criteria for presumptive past-year depression, 480 (24.4%) for presumptive past-year PTSD, and 276 (14.0%) for both disorders. Residents who believed that their or their family's health was harmed by contaminated water (eg, risk ratio [RR] for depression: 2.23; 95% CI, 1.80-2.76), who had low confidence in public-official information (eg, RR for PTSD, 1.44; 95% CI, 1.16-1.78), who had previous exposure to PTEs (eg, RR for both disorders: 5.06; 95% CI, 2.99-8.58), or who reported low social support (eg, RR for PTSD, 2.58; 95% CI, 1.94-3.43) had significantly higher risk for depression, PTSD, and comorbidity. PTEs involving prior physical or sexual assault were especially potent risk factors (eg, both disorders: RR, 7.30; 95% CI, 4.30-12.42). Only 685 respondents (34.8%) were ever offered mental health services to assist with water-crisis-related psychiatric symptoms; most (543 [79.3%]) who were offered services utilized them. In this cross-sectional study of psychiatric disorder in Flint, Michigan, presumptive depression and PTSD were highly prevalent 5 years after the onset of the water crisis. These findings suggest that public-works environmental disasters have large-scale, long-term psychological sequelae. The Flint community may require expanded mental health services to meet continued psychiatric need. National disaster preparedness and response programs should consider psychiatric outcomes.

Emergency Relief Chain for Natural Disaster Response Based on Government-Enterprise Coordination.

Public health and effective risk response cannot be promoted without a coordinated emergency process during a natural disaster. One primary problem with the emergency relief chain is the homogeneous layout of rescue organizations and reserves. There is a need for government-enterprise coordination to enhance the systemic resilience and demand orientation. Therefore, a bi-level multi-phase emergency plan model involving procurement, prepositioning and allocation is proposed. The tradeoff of efficiency, economy and fairness is offered through the multi-objective cellular genetic algorithm (MOCGA). The flood emergency in Hunan Province, China is used as a case study. The impact of multi-objective and coordination mechanisms on the relief chain is discussed. The results show that there is a significant boundary condition for the coordinated location strategy of emergency facilities and that further government coordination over the transition phase can generate optimal relief benefits. Demand orientation is addressed by the proposed model and MOCGA, with the realization of the process coordination in multiple reserves, optimal layout, and transition allocation. The emergency relief chain based on government-enterprise coordination that adapts to the evolution of disasters can provide positive actions for integrated precaution and health security.

The City of Harare's response to COVID-19: A case for international co-operation and solidarity

This article uses the case of the City of Harare to offer insights into how the coronavirus pandemic shaped paradiplomacy in Zimbabwe. It argues that the City of Harare's international partnerships played a nominal role in helping its response to COVID-19. There is strong evidence that the coronavirus pandemic undermined the significance of international co-operation and solidarity by African subnational governments in pursuit of their development and service delivery mandates. Better leveraging of the City of Harare's international partnerships had the potential to transform many of the challenges it faced in fighting the pandemic. The absence of a robust international dimension in the city's handling of the COVID-19 pandemic was one of the main factors that resulted in most of its responses being ineffective. The article concludes that the City of Harare's experience offers important lessons to, among others, African governments on the need to integrate decentralised responses and city-to-city co-operation into their future national disaster and economic response, recovery and resilience strategies.

Local Officials’ Perceptions of Climate Victim Management Challenges on Bangladesh’s Southeast Coast

In Bangladesh, extreme weather events displace about one million people each year. The national government resettles these climate victims by allocating houses in so-called cluster villages. This paper examines how local disaster management officials manage the resettlement of climate victims in Bangladesh’s coastal areas. For this paper, we conducted a preliminary field work, questionnaire survey, and informal phone interviews. The questionnaire survey was conducted from March to July 2020 among 70 central government civil servants who worked as disaster management officials and played a pivotal role in local decision making for climate victim resettlement. This paper first examines how national disaster response policies were implemented in local areas before, during, and after disasters. Our questionnaire survey results show five management challenges local officials faced in managing displaced people: (1) local officials’ limited onsite experience, (2) varied impacts of natural disasters on islands and the mainland, (3) arbitrary engagement in disaster response actions, (4) lack of evacuation drills, and (5) weak coordination skills among relevant stakeholders. In particular, these challenges were acute among island officials. Our multiple regression analyses show that the respondents’ age and onsite work experience (p &lt; 0.05) significantly affected their perceptions. Overall, these findings suggest a need to drastically improve local disaster governance capacity. This study offers insights into how countries with similar challenges may respond to climate-induced displacement in the future.

재난 이후 지역사회 복원을 위한 공동체 정책에 관한 연구

The severity of recent disasters is differentiated from previous ones in that the patterns of disasters appear widely. Although the concept of disasters is changing, most studies have focused on recovery of physical damage caused by disaster. This study intends to expand the scope of disaster response and suggest the necessity of community restoration through community policy. Recent disasters tend to be complex disasters combined with various factors within society. In order to systematically respond to such disasters, it is necessary to respond by expanding the scope of the time and space of the disaster. The concept and characteristics of community can also be presented from various perspectives. This paper focuses on the fact that communities can be given new roles in disaster situations. Following the conceptual approach to disasters and communities, this study looked at community programs in the Sewol-Ferry disaster. Although the response to the Sewol-Ferry disaster has not been completed, this case has the meaning of drawing up the agenda of community restoration in a disaster situation. In conclusion, this study emphasizes the need for a more expanded approach to national disaster response. In order to achieve the policy goal of community restoration, disaster response needs to expand the scope of its time and space. In addition, the application method of the community program should also examine various approaches.