Emergency Relief Operations Research Articles

Integrated planning of emergency supply pre-positioning and victim evacuation

Natural disasters such as earthquakes, floods, and storms cause numerous human casualties and economic losses every year. Emergency supply pre-positioning and victim evacuation, as two important emergency relief operations, tend to be separately considered in the literature. However, there are intrinsic correlations between the two operations in reality, which calls for an integrated planning of emergency supply pre-positioning and victim evacuation. In this study, we propose a stochastic programming model to optimize, in an integrated way, the deployment of emergency facilities, pre-stocking and distribution of emergency supplies and management of self-evacuated victims under two planning goals, namely cost effectiveness and evacuation fairness. We also formulate four comparison models to highlight the benefits of our integrated planning model. With a case study on the development of an emergency preparedness and response plan in Sichuan Province, China, we not only demonstrate the effectiveness and benefits of our approach but also obtain key managerial insights and policy suggestions for better emergency management practice.

Development of geo-environmental factors controlled flash flood hazard map for emergency relief operation in complex hydro-geomorphic environment of tropical river, India.

The occurrences of flash floods in sub-tropical climatic regions like India are ubiquitous phenomena, particularly during the monsoon season. This type of flood occurs within a short period of time and makes it distinctive from all-natural hazards, which causes huge loss of economy and causalities of life. Therefore, its prediction is crucial and one of the challenging tasks for researchers to mitigate this sustainably. Furthermore, identifying flash flood susceptible regions is the foremost responsibility in managing flood events, which helps the local administration take emergency relief operations in flood-prone regions. In September 2021, the flood in the Gandheswari river basin was the most severe compared to the past decade. The occurrences of flash floods in the lower course of the Gandheswari river has been affected riparian habitats rigorously. Thus, in this study, we proposed the bivariate logistic regression (LR) method to delineate this river basin's flash flood hazard (FFH) map. Here, sixteen flood conditioning factors were selected for modeling purposes with the help of a multicollinearity test, and a total of 71 flood points were identified from the historical dataset. The produced result was validated by six distinctive validating techniques, including receiver operating characteristics (ROC) analysis, specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and F-score. These techniques have shown that present modeling has high predictive performance in both training and testing dataset with the values of ROC (training-0.928, validating-0.892), specificity (training-0.911, validating-0.882), sensitivity (training-0.915, validating-0.885), PPV (training-0.912, validating-0.874), NPV (training-0.91, validating-0.875), and F-score (training-0.92, validating-0.89). Therefore, the proposed method in this and the outcome result will help the disaster manager make proper decisions to mitigate the hazardous situation and take sustainable emergency relief operations.

ReliefChain: A blockchain leveraged post disaster relief allocation system over smartphone-based DTN.

One of the major concerns in any emergency relief operation is appropriate allocation of scarce emergency relief materials to the affected community. Due to several reasons ranging from lack of mechanism to accurately assess demand and utility of relief materials to malicious participation of some of the stakeholders, such allocation may become ad-hoc. Thus, it becomes imperative to have an unchallengeable and globally accessible record of relief requirement vis-à-vis allocation for efficient relief management. Emergency response organizations (e.g. UNICEF) have recommended the adoption of blockchain technology to create such immutable records. However, the usage of blockchain is restricted by the availability of end-to-end internet connection which may not be available in a post-disaster scenario. This paper proposes ReliefChain, a blockchain leveraged post disaster relief allocation system over delay tolerant network that works in such environments. We validate relief requirements to mitigate resource diversion, forecasting the exact demand and enumerating precise utilities of relief items. We design smart contracts for creating new transactions to upload relief requirements and allocations in the blockchain network. The proposed system executes these smart contracts to create an immutable and globally accessible record of relief requirement and allocation. Effectiveness of the proposed system is evaluated through extensive simulation in Ethereum platform. Results substantiate the efficiency of the system over a system using baseline methodologies, in terms of design parameters like shelter specific deficit and average resource deficit while not compromising the blockchain performance in terms of processing time and gas consumption even in presence of malicious forwarders.

Social media communication during natural disasters and the impact on the agricultural market

When a natural disaster occurs, it is crucial to establish appropriate channels of communication between government agencies and the public, not only in the interest of efficient emergency responses and relief operations but also to inform the population and contain people’s concerns. In this paper, we study how the social media activity of government agencies involved in disaster management affects the agricultural market during natural disasters and extreme weather conditions. Our results suggest that social media activity, in particular that of the Federal Emergency Management Agency (FEMA), triggers market-related and public concerns about potential shortages and financial losses, which may push up the price of agricultural commodities. We show that higher levels of public concern about global warming and economic policy uncertainty intensify the market reaction, boosting the social media impact.

SUPPLEMENTING SATELLITE IMAGERY WITH SOCIAL MEDIA DATA FOR REMOTE RECONNAISSANCE: A CASE STUDY OF THE 2020 TAAL VOLCANO ERUPTION

Abstract. Social sensing and satellite imagery are named as the top emerging data sources for disaster management. There is a wealth of data, both in quantity and quality that can be extracted from social media platforms such as Twitter, given that the content published by users is generally in real-time and includes a geotag or toponym. To reduce costs, risks, and time, performing reconnaissance using remote sources of information is highly suggested. This study explores how social media data can be used to supplement satellite imagery in post-disaster remote reconnaissance using the January 2020 Taal Volcano Eruption in the Philippines. Tweets about the volcanic eruption were scraped, and ashfall-affected locations mentioned in tweet content were extracted using Named Entity Recognition (NER). To visualize the progression of the tweeted locations, dot density maps and hotspot maps were generated. Additionally, a potential ashfall extent map was generated from processed DIWATA-2 satellite imagery using Support Vector Machine (SVM) classification. An intersection of both dot density map and ashfall extent map was performed for comparative analysis of both data. Validation was carried out by matching the ashfall-affected locations with ground reports from local government offices and news reports. The use of social media data complements satellite image classification in the detection of disaster damage for a quick and cost-efficient remote reconnaissance. This information can be utilized by rescue teams for faster emergency response and relief operations during and after a disaster.

Routing Issues And Challenges for Mobile ad-hoc Networks (MANETs): A Review

Mobile ad-hoc networks also referred to as MANETs belong to a collaborative collection of different cellular devices from a heterogeneous community, the prolific characteristic is the capability to enroll in and give up the community at any time. This results in a creation of a decentralized community of cell nodes that could connect, talk and bypass messages to each different without the requirement of an intermediate router. MANETs are usually used in numerous sets of packages like military operations, emergency or catastrophe relief operations, commercial enterprise conferences, political conferences, mine site operations. In those forms of networks, the maximum crucial trouble is that of locating the first-class direction available between the 2 nodes consistent with the consumer necessities. Despite many fascinating future programs of cellular ad-hoc networks (MANETs), There are nevertheless a few crucial challenges and open issues to be solved. As a consequence, extensively on this paper, we present an outline of MANETs, and their routing protocols. Then we present numerous tough issues and the future work.

Heuristics for the Canadian traveler problem with neutralizations

Canadian Traveler Problem with Neutralizations (CTPN) is a recently introduced challenging graph-theoretic path-planning problem. In CTPN, traversability status of some edges in the underlying graph is dependent on an a priori probability distribution. A traveling agent has two capabilities called disambiguation and neutralization. In the disambiguation case, the true status of an ambiguous edge (traversable or untraversable) is revealed when the agent is at either end of such edges. If the neutralization capability is exploited, the edge immediately becomes traversable. These capabilities are limited and may add a cost of increased path length. The goal of the agent is to find the shortest expected path length by devising an optimal policy that dictates when and where to disambiguate or neutralize. CTPN has important and practical applications within the context of expert and intelligent systems. These include autonomous robot navigation, adaptive transportation systems, naval and land minefield countermeasures, and navigation inside disaster areas for emergency relief operations. There is a recently proposed state-of-the-art exact algorithm that solves CTPN to optimality, called CAON∗ (AO∗ with Caching and Neutralizations). CAON∗ is based on an extension of the well-known AO∗ (AND-OR search) algorithm. Even though CAON∗ has significant improvements compared to its predecessors, it still has exponential run time and space complexity and it has been shown to solve only small instances of CTPN in practice. In this study, we introduce new heuristics for CTPN based on novel strategies that can be used to solve much larger and realistic problem instances. We provide computational experiments on Delaunay graphs to assess and compare the performance of these heuristics and CAON∗, in terms of both run time and solution quality. Our computational experiments indicate that the proposed heuristics run extremely fast (well under a second in all cases) and they result in up to 58% improvement over existing heuristics with respect to expected path lengths with an overall improvement of 32% across our computational experiments.

Development of flood hazard map and emergency relief operation system using hydrodynamic modeling and machine learning algorithm

This study performs flood hazard mapping and evaluates community flood coping strategies. In addition, it proposes a humanitarian aid information system (HAIS) to enhance emergency support for flood victims. First, a flood hazard map was prepared using the hydrodynamic model (HM)–FLO 2D coupled with a machine learning algorithm (MLA)-scaled conjugate gradient neural network (SCG-NN). The performance of the MLA was evaluated using a validation dataset and statistical measures such as the mean square error (MSE: 0.080), root mean square error (RMSE: 0.282), and coefficient of determination (R2 = 0.830). According to the generated flood hazard map, most of the study area was classified as low (47.85%) or moderate (27.47%) hazardous zones, whereas only a small portion was delineated as high (20.64%) or very high (4.04%) hazardous zones. The accuracy of the hazard map (HM-MLA) versus the ground truth was tested statistically and was found to be high. Second, an investigation of local flood management strategies revealed that the current information system is not well prepared for emergencies, including the quantification of emergency relief necessities. Therefore, an HAIS, which specifies hazard information and quantifies emergency aids (food items) for flood victims, can be an effective emergency preparedness tool. We calculated the required emergency aid considering satellite-derived flood data. Finally, we conclude that the proposed HAIS will help humanitarian organizations and government agencies coordinate and perform relief operations effectively in the worst-hit regions across the country.

Planning for resilient transport infrastructure for disaster prone area – Case study of Munnar town

Critical infrastructure networks, including transport, are crucial to the social and economic function of urban areas and the state as a whole but are at increasing risk recently from natural hazards. Minimizing disruption to the transportation networks should form part of a strategy to increase resilience of infrastructure. The recent flood and landslide havocs in Kerala had a devastating effect in the state particularly in hilly regions like Munnar, wherein the landslides will aggravate the devastating effect of rain havoc. Absence of a resilient transportation network was well evident while the evacuation plans were sought for. This highlights the necessity of providing a befitting and robust transport means which would remain useful in all such tough times of emergency requirements for saving lives. This paper presents a GIS mapping of disaster affected locations in the roadway network of the study region. Alternate routes were identified which have the potential to be developed into all-weather bituminous surfaced roads for emergency use in times of disasters. Also, shelter locations are identified for emergency relief operations and were analyzed and mapped in GIS platform with attribute data regarding their accessibility. Hydrological studies were conducted in the study area in order to find the total discharge during the flood, the direction of flow, the flow accumulation, etc.

A healthcare location-allocation model with an application of telemedicine for an earthquake response phase

Researchers have focused on finding ways of reducing natural disaster losses for decades. In recent times, technological advancements have improved the health care system and transformed emergency relief operations. An emerging medical advancement is telemedicine. The application of telemedicine has the potential to reduce the burden on the main trauma centers and hospitals after a significant earthquake occurs. The first 72 hours after an earthquake are critical. A well-planned medical response, although just one component of the disaster management process, plays a vital role in reducing mortality and morbidity. In this study, we propose a new modeling framework to optimize the selection and establishment of temporary health facilities in an area struck by a significant earthquake, and to allocate healthcare professionals, such as doctors, who can provide in-person and virtual care to the victims. The objective of the model is to minimize the total unsatisfied healthcare demand. The framework is particularly useful in low-income countries, where natural disasters quickly deplete medical and social resources. The application of the model is numerically illustrated using data from the 2005 Kashmir Earthquake in Pakistan. The results obtained are presented and discussed in the paper.

A robust neutrosophic fuzzy-based approach to integrate reliable facility location and routing decisions for disaster relief under fairness and aftershocks concerns

Relief distribution and victim evacuation are the crucial emergency relief operations after sudden-onset disasters to alleviate the repercussions of catastrophes in the concerned areas, although aftershocks and unfair distribution of relief items can affect these planning and beget plenty of undesirable reflections. In this paper, a new multi-objective reliable optimization model to organize a humanitarian relief chain is rendered to make a broad range of decisions, including reliable facility location-allocation, fair distribution of relief items, assignment of victims, and routing of trucks. For this purpose, the first objective function is to minimize the total logistics costs, the second one is to minimize the total time of relief operations, and the third one minimizes the variation between upper and lower bounds of transportation cost of distribution centers to regulate the workload of them. What is more, due to the fact that the uncertain essence of catastrophes, such as demand, the capacity of facilities, miscellaneous costs and transportation times, a novel uncertainty approach, including robust optimization and the neutrosophic set, is proposed to surmount these obstacles. Ultimately, a real case study is examined to illustrate the validity of the proposed model and solution method. The obtained results reveal that via increasing the capacity of the emergency centers by 30%, the total cost of the humanitarian relief network is reduced by 18%, and the operating time is reduced by 9%. What is more, if the probability of disruption in one of the distribution centers reaches zero, the logistics costs will be reduced to approximately 20%, and also the distance between the maximum and minimum distance traveled will be reduced by 30%, and the workload between distribution centers will be more balanced.

A three-stage stochastic model for emergency relief planning considering secondary disasters

ABSTRACT In the real world, secondary disasters occur frequently after primary disasters, and their diverse and uncertain nature along with the destruction may make the emergency relief operations more challenging. A scenario-based three-stage stochastic programming model is proposed considering the correlation between primary and secondary disasters under uncertain conditions. In order to enhance the computational tractability of the model, an accelerated Benders decomposition algorithm is formulated. In addition, to tackle large-scale cases, an approximation method employing the worst-case scenario in the third stage is established to improve the computational tractability. A computational study is performed to highlight the significance of the model and the efficiency of the proposed solution strategy. The results indicate that, by considering secondary disasters, demand satisfaction can be considerably improved compared with considering only primary disasters.

Uncertain demand estimation with optimization of time and cost using Facebook disaster map in emergency relief operation

When a disaster disrupts a society within a moment without a little warning, the living people of those areas face the deprivation due to demolition. Facebook, one of the most popular social media plays a vital role in response to the victims. The authority of Facebook has launched safety check feature to know about the requirement after the disruption of disaster and based on the information, a intensity factor is defined for requirement of relief products in this research work. Estimating the amount of relief products through intensity measure, our research work has introduced a mathematical model for initiation of humanitarian logistic operation plan. The research has focused on two objective functions through the mathematical model which are minimization of total cost and response time. The model with two objective functions is converted to a equivalent compromise model with neutrosophic compromise programming approach. Deterministic and non-deterministic both algorithms are implemented in the solution process of the compromise mathematical model. In deterministic approach, mathematical model is varified with different methods to obtain compromise results. For non-deterministic approach, a genetic algorithm is proposed to solve the model. The model is experienced with a numerical example and hereby statistical investigation is performed considering different dimension varying the parameters of the model.

Deployment of an aerial platform system for rapid restoration of communications links after a disaster: a machine learning approach

Having reliable telecommunication systems in the immediate aftermath of a catastrophic event makes a huge difference in the combined effort by local authorities, local fire and police departments, and rescue teams to save lives. This paper proposes a physical model that links base stations that are still operational with aerial platforms and then uses a machine learning framework to evolve ground-to-air propagation model for such an ad hoc network. Such a physical model is quick and easy to deploy and the underlying air-to-ground (ATG) propagation models are both resilient and scalable and may use a wide range of link budget, grade of service (GoS), and quality of service (QoS) parameters to optimise their performance and in turn the effectiveness of the physical model. The prediction results of a simulated deployment of such a physical model and the evolved propagation model in an ad hoc network offers much promise in restoring communication links during emergency relief operations.

The Value of Near Real-Time Earth Observations for Improved Flood Disaster Response

Information is a critical resource in disaster response scenarios. Data regarding the geographic extent, severity, and socioeconomic impacts of a disaster event can help guide emergency responders and relief operations, particularly when delivered within hours of data acquisition. Information from remote observations provides a valuable tool for assessing conditions ‘on the ground’ more quickly and efficiently. Here, we evaluate the social value of a near real-time flood impact system using a disaster response case study, and quantify the Value of Information (VOI) of satellite-based observations for rapid response using a hypothetical flooding disaster in Bangkok, Thailand. MODIS imagery from NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) system is used to produce operational estimates of inundation depths and economic damages. These rapid Earth observations are coupled with a decision-analytical model to inform decisions on emergency vehicle routing. Emergency response times from vehicles routed using flood damage data are compared with baseline routes without the benefit of advance information on road conditions. Our results illustrate how the application of near real-time Earth observations can improve the response time and reduce potential encounters with flood hazards when compared with baseline routing strategies. Results indicate a potential significant economic benefit (i.e. millions of dollars) from applying near real-time Earth observations for improved flood disaster response and management.

Canadian Traveler Problem with Neutralizations

The Canadian Traveler Problem (CTP) and the Obstacle Neutralization Problem (ONP) are two well-studied graph-theoretic path planning problems in the literature and both problems have been shown to be computationally intractable. In CTP, certain edges in a graph are blocked by a known probability and their status is revealed only when the traversing agent is at either end of these edges using the agent’s limited disambiguation capability. The goal is to minimize the expected length of the traversal between a starting and a termination vertex by devising a policy that dictates in real-time which edge to disambiguate. In ONP, an agent needs to safely and swiftly navigate from a given source location to a destination through an arrangement of obstacles in the plane. The agent has a limited neutralization capability and uses it to safely pass through an obstacle at a cost of increased traversal length. The agent’s goal is to find the sequence of obstacles to be neutralized en route which minimizes the overall traversal length subject to the agent’s limited neutralization capability. Both of these problems have important and practical applications within the context of expert and intelligent systems. These include: autonomous robot navigation, adaptive transportation systems, naval and land minefield countermeasures, and navigation inside disaster areas for emergency relief operations. In this study, we consider a new path planning problem in the simultaneous presence of disambiguation and neutralization capabilities. This appears to be the first of its kind in the literature despite the close and inherent relationship between CTP and ONP. We call this problem the Canadian Traveler Problem with Neutralizations (CTPN). We present a Markov decision process formulation of CTPN and propose an optimal algorithm. This is based on an extension of the well-known AO* search algorithm. We provide computational experiments on Delaunay graphs to assess the relative performance of this algorithm in comparison to the well-known value iteration and AO* algorithms. We then investigate the relative utility and importance of the disambiguation and neutralization capabilities in order to assist decision-makers with financial constraints as well as navigation performance decisions.

A cyberGIS-enabled multi-criteria spatial decision support system: A case study on flood emergency management

ABSTRACTWith the increased frequency of natural hazards and disasters and consequent losses, it is imperative to develop efficient and timely strategies for emergency response and relief operations. In this paper, we propose a cyberGIS-enabled multi-criteria spatial decision support system for supporting rapid decision making during emergency management. It combines a high-performance computing environment (cyberGIS-Jupyter) and multi-criteria decision analysis models (Weighted Sum Model (WSM) and Technique for Order Preference by Similarity to Ideal Solution Model (TOPSIS)) with various types of social vulnerability indicators to solve decision problems that contain conflicting evaluation criteria in a flood emergency situation. Social media data (e.g. Twitter data) was used as an additional tool to support the decision-making process. Our case study involves two decision goals generated based on a past flood event in the city of Austin, Texas, U.S.A. As our result shows, WSM produces more diverse values and higher output category estimations than the TOPSIS model. Finally, the model was validated using an innovative questionnaire. This cyberGIS- enabled spatial decision support system allows collaborative problem solving and efficient knowledge transformation between decision makers, where different emergency responders can formulate their decision objectives, select relevant evaluation criteria, and perform interactive weighting and sensitivity analyses.

Bi-objective multi-resource scheduling problem for emergency relief operations

Resource scheduling for emergency relief operations is complex as it has many constraints. However, an effective allocation and sequencing of resources are crucial for the minimization of the completion times in emergency relief operations. Despite the importance of such decisions, only a few mathematical models of emergency relief operations have been studied. This article presents a bi-objective mixed integer programming (MIP) that helps to minimize both the total weighted time of completion of the demand points and the makespan of the total emergency relief operation. A two-phase method is developed to solve the bi-objective MIP problem. Additionally, a case study of hospital network in the Melbourne metropolitan area is used to evaluate the model. The results indicate that the model can successfully support the decisions required in the optimal resource scheduling of emergency relief operations.

Cross-sector humanitarian–business partnerships in managing humanitarian logistics: an empirical verification

Cross-Sector humanitarian–business partnerships in managing humanitarian logistics comprise a key discussion topic in literature, yet empirical validations of these partnerships are lacking to date. This paper aims to develop a typological framework for humanitarian–business partnerships in managing humanitarian logistics and to empirically verify this typology with a data-set using content analysis. The results show that the amounts of partnerships developed between the business sector and the humanitarian sector in managing humanitarian logistics is still limited and these partnerships are not widely publicised. The research furthermore shows that financial contributions comprise the most common type of resource delivered by the business sector when it comes to partnerships with the humanitarian sector focused on managing humanitarian logistics. It is also interesting to note that the majority of partnerships in our sample are dyadic in nature, managed by a single humanitarian organisation and a single business corporation. Furthermore, we find that most of the partnerships target support for natural disaster emergency relief operations and mainly focus on disaster response.

A Decadal Historical Satellite Data and Rainfall Trend Analysis (2001–2016) for Flood Hazard Mapping in Sri Lanka

Critical information on a flood-affected area is needed in a short time frame to initiate rapid response operations and develop long-term flood management strategies. This study combined rainfall trend analysis using Asian Precipitation—Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) gridded rainfall data with flood maps derived from Synthetic Aperture Radar (SAR) and multispectral satellite to arrive at holistic spatio-temporal patterns of floods in Sri Lanka. Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data were used to map flood extents for emergency relief operations while eight-day Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data for the time period from 2001 to 2016 were used to map long term flood-affected areas. The inundation maps produced for rapid response were published within three hours upon the availability of satellite imagery in web platforms, with the aim of supporting a wide range of stakeholders in emergency response and flood relief operations. The aggregated time series of flood extents mapped using MODIS data were used to develop a flood occurrence map (2001–2016) for Sri Lanka. Flood hotpots identified using both optical and synthetic aperture average of 325 km2 for the years 2006–2015 and exceptional flooding in 2016 with inundation extent of approximately 1400 km2. The time series rainfall data explains increasing trend in the extreme rainfall indices with similar observation derived from satellite imagery. The results demonstrate the feasibility of using multi-sensor flood mapping approaches, which will aid Disaster Management Center (DMC) and other multi-lateral agencies involved in managing rapid response operations and preparing mitigation measures.