Disaster Relief Management Research Articles

Developing a Community-Based Disaster Relief Management System

This study aimed to enhance disaster relief operations in Tuguegarao City by proposing a more efficient management system framework. Utilizing interviews and thematic analysis, researchers in 2017 identified problems in emergency management, such as inadequate equipment, budget constraints, and issues with relief distribution. The study highlighted confusion and political bias in relief operations, leading to mistrust in the local government. In response, the researchers developed a community-based Disaster Relief Management System (CBDRMS) framework, inspired by Foucault’s power/knowledge analytics. This framework emphasizes transparency, communication, and good relationships among LGUs, NGOs, and recipients. The proposed online system includes a portal and a Facebook Page for data storage and communication, aiming to empower stakeholders and foster discourse for effective disaster relief management. The researchers believe that this approach can overcome barriers, leading to development and improved disaster response.

The influence of trauma on internal integration: An approach‐avoidance analysis in disaster relief operations

Disaster relief workers face unique factors in their operating environments that can inhibit internal integration. For example, disaster relief often involves exposure to traumatic events affecting relief workers’ commitment to cooperation and the organization. As such, disaster relief organizations dedicate substantial amounts of scarce resources to support workers exposed to trauma. Unfortunately, contradictory views exist in the literature on how trauma exposure affects commitment and integrative behaviors and how supervisor support influences these relationships. Based on the approach‐avoidance coping theory, we test whether trauma exposure has positive or negative effects. We test our hypotheses on data from 300 disaster relief workers collected using a 2 × 3 factorial scenario‐based experiment. We find that trauma exposure evokes avoidance coping behaviors, which decrease individuals’ cooperative disposition, and approach coping behaviors, which motivate organizational commitment. Next, we show that both forms of commitment have a nonlinear convex relationship with internal integration and mediate the relationship between trauma exposure and internal integration. Finally, we find that supervisor support amplifies these relationships. When exposed to trauma, supervisor approach and avoidance orientations provide higher internal integration levels than subjects exposed to no supervisor support. These findings extend the literature on disaster relief management, integration, and support, guiding decision‐making regarding support investments in disaster relief organizations.

A Decision-Making Model to Determine Dynamic Facility Locations for a Disaster Logistic Planning Problem Using Deep Learning

Disaster logistics management is vital in planning and organizing humanitarian assistance distribution. The planning problem faces challenges, such as coordinating the allocation and distribution of essential resources while considering the severity of the disaster, population density, and accessibility. This study proposes an optimized disaster relief management model, including distribution center placement, demand point prediction, prohibited route mapping, and efficient relief goods distribution. A dynamic model predicts the location of post-disaster distribution centers using the K-Means method based on impacted demand points’ positions. Artificial Neural Networks (ANN) aid in predicting assistance requests around formed distribution centers. The forbidden route model maps permitted and prohibited routes while considering constraints to enhance relief supply distribution efficacy. The objective function aims to minimize both cost and time in post-disaster aid distribution. The model deep location routing problem (DLRP) effectively handles mixed nonlinear multi-objective programming, choosing the best forbidden routes. The combination of these models provides a comprehensive framework for optimizing disaster relief management, resulting in more effective and responsive disaster handling. Numerical examples show the model’s effectiveness in solving complex humanitarian logistics problems with lower computation time, which is crucial for quick decision making during disasters.

Distributionally robust location-allocation with demand and facility disruption uncertainties in emergency logistics

Emergency logistics is vital to disaster relief management. In this paper we develop a distributionally robust optimization model (DROM) for optimizing the locations of distribution centres and backup warehouses, and the distribution of disaster relief supplies in emergency logistic networks by minimizing the expected total cost and the total delivery time. Based on limited historical distribution information, the model considers uncertain demand and uncertain facility disruptions, and describes their distributions through ambiguity sets. Following the adaptability and tractability of the ambiguity sets, we show that the model can be equivalently re-formulated as a mixed-integer linear program. To solve the model, we propose an exact algorithm based on Benders decomposition (BD). We also introduce an in-out Benders cut generation strategy to improve the efficiency of the BD algorithm. Finally, we perform extensive numerical studies to test the performance of the BD algorithm, ascertain the benefits of the proposed DROM over the corresponding deterministic and stochastic models, and examine the impacts of the key model parameters to gain managerial insights.

Relief Operations among Selected Typhoon Disasters in the Philippines: A Workplan for Disaster Relief Management

Relief Operations among Selected Typhoon Disasters in the Philippines: A Workplan for Disaster Relief Management

Deducing Flood Development Process Using Social Media: An Event-Based and Multi-Level Modeling Approach

Social media is increasingly being used to obtain timely flood information to assist flood disaster management and situational awareness. However, since data in social media are massive, redundant, and unstructured, it is tricky to intuitively and clearly obtain effective information. To automatically obtain clear flood information and deduce flood development processes from social media, the authors of this paper propose an event-based and multi-level modeling approach including a data model and two methods. Through the hierarchical division of events (division into spatial object, phase, and attribute status), the flood information structure (including time, space, topic, emotion, and disaster condition) is defined. We built an entity construction method and a development process deduction method to achieve the automatic transition from cluttered data to orderly flood development processes. Taking the flooding event of the Yangtze and Huai Rivers in 2020 as an example, we successfully obtained true flood information and development process from social media data, which verified the effectiveness of the model and methods. Meanwhile, spatiotemporal pattern mining was carried out by using entities from different levels. The results showed that the flood was from west to east and the damage level was positively correlated with the number of flood-related social media texts, especially emotional texts. In summary, through the model and methods in this paper, clear flood information and dynamic development processes can be quickly and automatically obtained, and the spatiotemporal patterns of flood entities can be examined. It is beneficial to extract timely flood information and public sentiments towards flood events in order to perform better disaster relief and post-disaster management.

Risk-averse two-stage distributionally robust optimisation for logistics planning in disaster relief management

Relief logistics is vital to disaster relief management. Herein, a risk-averse two-stage distributionally robust programming model is proposed to provide decision support for planning disaster relief logistics. It is distinct from the conventional disaster relief logistics planning problem in that (i) the facility location-inventory model and the multi-commodity network flow formulation are integrated; (ii) the probability distribution information of the supply, demand, and road link capacity is partially known, and (iii) the two-stage distributionally robust optimisation (DRO) method based on the worst-case mean-conditional value-at-risk criterion is developed. For tractability, we reformulate the proposed DRO model as equivalent mixed-integer linear programs for box and polyhedral ambiguity sets, which can be directly solved to optimality using the CPLEX software. To evaluate the validity of the proposed DRO model, we conduct numerical experiments based on a real-world case study addressing hurricane threats in the Gulf of Mexico region of the United States. Furthermore, we compare the performance of the proposed DRO model with that of the conventional two-stage stochastic programming model. Finally, we report the managerial implications and insights of using the risk-averse two-stage DRO approach for disaster relief management.

Two-stage distributionally robust programming based on worst-case mean-CVaR criterion and application to disaster relief management

In this paper, we simultaneously capture several practical features in disaster relief management: integrated facility location, inventory pre-positioning and delivery decisions, relief resource priority, partial probability information of demand and risk-averse criterion. We cast the problem as a two-stage distributionally robust mean-conditional value-at-risk (CVaR) optimization model, for which we derive computationally tractable counterparts under the box and polyhedral ambiguity sets. We further identify the relationship between the proposed distributionally robust model and the traditional two-stage stochastic programming model. We assess the performance of the proposed model by an illustrative small-sized example. From the out-of-sample analysis, we show the superiority of the distributionally robust model compared to the two-stage stochastic programming model in terms of stability. We also implement the proposed model in a realistic large-scale case study of hurricane threats in the southeastern US. We finally achieve the managerial implications and insights of using the distributionally robust optimization method.

A Novel Method for Disaster Relief Management: CMQI System

A Novel Method for Disaster Relief Management: CMQI System

Quality management in humanitarian operations and disaster relief management: a review and future research directions.

Quality management has been widely discussed in the literature, and recent special issues on humanitarian supply chains and relief operations have emphasized the increasing importance of quality management in this key emerging area. In this paper, we provide an extensive literature review in the field of quality management in humanitarian operations and disaster relief management. Our comprehensive review, comprising 61 articles published from 2009 to 2018, leads to the identification of enablers (e.g., transparency, policy framework), challenges (e.g., financial services, identity protection), and theory development approaches, as well as numerous research gaps that must be addressed.

RETRACTED:An assessment of software defined networking approach in surveillance using sparse optimization algorithm

Abstract Unmanned aerial vehicles (UAV) can be used as basic elements of the sensor network or an upgrade of existing network that are built with static wireless sensor nodes. Wireless Networks (WN) are utilized across in surveillance in all domains like natural disasters, agriculture, water, forest, military, buildings, health monitoring, disaster relief & emergency management, area and industrial surveillance, due to its wider applicability. Software Defined Networking (SDN) provide a hopeful resolution in bendy supervision WSNs by allowing the separation of the control logic from the sensor nodes/actuators. The advantage with this SDN-based supervision in structure of WSNs is that it enables centralized control of the entire WSN making it simpler to deploy network-wide management protocols and applications on demand. Synthetic Aperture Radar (SAR) images are difficult to analyze due to the presence of speckle noise. Speckle noise must be filtered out before applying to other image processing applications. Three Layered Feed Forward Back Propagation Neural Network (TLFFBPNN) has been proposed to suppress the speckle noise. GLCM properties have been extracted and Back propagation training algorithm is used to train the neural network.

Administrative Law in the Multi-Level System: Legal Problems of Cross-Border Disaster Management Using the Example of Tyrol

Cross-border disaster relief management is a legally unexplored area. Despite various international agreements, regulations are scattered and marginal. However, cross-border cooperation especially with regard to natural hazard management is of great importance for the Alpine regions and becomes even more significant with regard to climate change. This is especially true for the European Region Tyrol – South Tyrol – Trentino. It is doubtful whether arrangements based on international law can provide the necessary framework adapted to specific situations. Even though a state treaty Tyrol – South Tyrol is conceivable it would be incomplete due to limited competencies, especially on the Tyrolean side. Likewise, little is to be expected at the level of the EGTC European Region Tyrol – South Tyrol – Trentino. On the contrary, it would make sense to codify a concerted approach by the partners Tyrol and South Tyrol in terms of disaster management and the cross-border use in their legislation, given that the regulations fall into their competences.

Three-Stage Mixed Integer Robust Optimization Model Applied to Humanitarian Emergency Logistics by Considering Secondary Disasters

In recent years, natural disasters occur frequently, and secondary disasters induced by major disasters will also cause huge losses. The diversity of secondary disasters makes humanitarian emergency logistics (HEL) more challenging but often overlooked by researchers. In order to solve the comprehensive HEL problem of major and secondary disasters, a three-stage mixed integer linear optimization (TS-MILO) model is proposed. Among them, the uncertainty of the demand for relief supplies is also extremely difficult to deal with. In order to resist the interference of uncertainty, based on robust optimization, the TS-MILO model is further transformed into a three-stage mixed integer robust optimization (TS-MIRO) model, which are respectively BTS-MIRO (Box set), PTS-MIRO (Polyhedral set), and ETS-MIRO (Ellipsoid set). The experimental results show that the TS-MILO model can provide the lowest cost but cannot solve the uncertainty problem. The improved TS-MIRO model will pay a robust price (increase by at least 10.05%), but maintains supply stability even in the worst-case scenario. Specifically, ETS-MIRO model has strong robustness, and its cost increase only accounts for 44.66% of BTS-MIRO model in the worst case. The service level of the three TS-MIRO models increases with the safety parameters, among which the service level in the ETS-MIRO model increases significantly from 88.53% to 96.44%. The research results can provide a strong support for the decision making of disaster relief management department.

Disaster responsive planning: the role of societal forces in implementing software applications

Open source software development is frequently used to serve human society, especially in finding solutions for managing and planning disaster situations such as floods, droughts and tsunami incidents. Careful consideration of the technical accuracy as well as social acceptance is essential to guarantee the sustainability of software development. Understanding the interaction between technology and human behaviour is vital in open source software development in terms of minimizing the negativities after the implementation. The social aspects of software development often being neglected or not being considered well enough in practical filed. The paper discusses the societal factors that can attribute when implementing open source disaster relief management platforms. Humanitarian disaster relief coordination involves some actors with different levels of power and authority. By using theories of multilevel governance and power relations, this paper focuses on understanding the consequences that may arise when implementing common open source platforms, especially in disaster management sector in developing counties. Finally, the article provides recommendations to minimize such obstacles for better functionality and the sustainability of the platform.

A framework to integrate social media and authoritative data for disaster relief detection and distribution optimization

In this paper, we propose an interdisciplinary approach to (natural) disaster relief management. Our framework combines dynamic and static databases, which consist of social media and authoritative data of an afflicted region, respectively, to model rescue demand during a disaster situation. Using Global Particle Swarm Optimization and Mixed-Integer Linear Programming, we then determine the optimal amount and locations of temporal rescue centers. Furthermore, our disaster relief system identifies an efficient distribution of supplies between hospitals and rescue centers and rescue demand points. By leveraging the temporal dimension of the social media data, our framework manages to iteratively optimize the disaster relief distribution.

The Volunteer's Needs and Preparedness in Assisting Natural Disaster Victims


 
 
 This paper is discusses about volunteers’ needs in order to increase the preparedness in assisting natural disasters victims. Disasters are phenomenon that causes damage in properties and lives that limit individuals or societies’ abilities and well-being. Natural disasters such as landslides, tsunami, flood and typhoon happen almost every year. These natural disasters affect certain countries such as Japan, Indonesia, Thailand and Malaysia. The volunteers are the one who manage the humanitarian aids to the victims. The needs of the volunteers that been identified need to be evaluated in order to help to expedite the disaster relief management process. This is because the important aspects that need to be evaluated are planning, operational management and disaster training. Meanwhile, disaster preparedness represent as systematic planning in response to the disaster situation that needs to be done to those involved in disaster relief i.e volunteers. The concern on this aspect will help volunteer to be more active, positive and confident, in both physically and mentally when providing helps. Therefore, this concept paper will discusses volunteers’ needs in various perspectives and opinions in order to help in increasing the preparedness in assisting natural disasters victims, as well as increase the effectiveness throughout the relief process.
 
 

Flood Relief Management for Residents in Temerloh, Pahang, Malaysia

Floods are natural disaster occurred frequently over worldwide including Malaysia. Floods are an overflow or increasing volume of water level which occurred within a specific time and temporary. The objective of this research was to identify the management of flood relief for people who live at Temerloh, Pahang area. Among the objectives includes in this research is to identify the impacts of victims in terms of psychology, next to determine the types of assistance provided to flood victims during and after the disaster and finally to investigate the effectiveness of the assistance provided. A quantitative approach was done in this research by distribution of questionnaires involving 400 respondents. Descriptive analysis and inferential of percentage, frequency and independent sample t-test for study the management of assistance was used in this research. Results showed about 73.3 percent respondent respondents strongly agreed that the phenomenon of flooding lead to emotional stress while others 26.8 percent respondents stated the opposite. In additional, about 42.8 percent respondents agreed that assistance is given inadequate. Besides that, about 52.0 percent respondent disagreed with the statement related disaster relief management is on a satisfactory level. In overall, the management of the assistance provided to the victims was at moderate level

Uncertain weighted dominating set: a prototype application on natural disaster relief management

Indeterminacy is an intrinsic characteristics of real-world data. Where they originate from credible experiments, probability theory is a robust tool to manipulate this type of indeterminacy. However, this is not always the case, and referring to the domain expert belief is an alternative approach. Baoding Liu initiated an axiomatic basis of uncertainty theory to answer this kind of indeterminacy. Dominating set with its different versions has a wide range of applications in many fields, while the practice suffers indeterminacy with no reliable data in most cases. In this paper, we investigate the minimum weighted dominating set with indeterministic weights in two cases. The weights in the first one have probability distribution and in the other one uncertainty distribution which they are based on the belief degree of the domain expert. In both cases, the objective function of model is not defined. To overcome this difficulty, based on probability and uncertainty theory, deterministically two different models are constructed. The first model considers an \(\alpha \)-chance method, and the second exploits the expected value of the uncertain variables. Both models are converted to deterministic ones resulting to the so-called \(\alpha \)-minimum weighted dominating set, and the uncertain minimum weighted dominating set, respectively. A prototype application in earthquake relief management is provided, and the performance of models is experimented in a concrete illustrative example.

Research in Humanitarian Supply Chain Management and a New Framework

With the frequency and magnitude of disasters on the rise, millions of people suffer huge losses every year. Scholars have recently proposed various frameworks in disaster relief management in order to guide the research in this field. Although successful disaster relief requires the entire humanitarian supply chain to respond in harmony, it is surprising that there exists no humanitarian relief framework drawn from the perspective of supply chain management. In this article, we create a new research framework for Humanitarian Supply Chain Management (HSCM) that is complimentary but distinct from commercial supply chain management (CSCM) frameworks. The framework we developed offers a new lens for humanitarian researchers. We also conduct a systematic literature review in this field and identify some opportunities for future research. The results strongly suggest the need for additional empirical research to test the existing concepts and models. Second, there is evidence that research focusing on “upstream” relief chain has been neglected relative to “downstream”. Additionally, due to its rapid advancement, information technology related research opportunities in this field would always be there.

ESOS® – emergency Sanitation Operation System

This paper presents the innovative emergency Sanitation Operation System (eSOS) concept created to improve the entire emergency sanitation chain and provide decent sanitation to people in need. The eSOS kit is described including its components: eSOS smart toilets, an intelligent excreta collection vehicle-tracking system, a decentralized excreta treatment facility, an emergency sanitation coordination center, and an integrated eSOS communication and management system. The paper further deals with costs and the eSOS business model, its challenges, applicability and relevance. The first application, currently taking place in the Philippines will bring valuable insights on the future of the eSOS smart toilet. It is expected that eSOS will bring changes to traditional disaster relief management.