Post-earthquake Relief Research Articles

Collaborative Task Allocation and Optimization Solution for Unmanned Aerial Vehicles in Search and Rescue

Earthquakes pose significant risks to national stability, endangering lives and causing substantial economic damage. This study tackles the urgent need for efficient post-earthquake relief in search and rescue (SAR) scenarios by proposing a multi-UAV cooperative rescue task allocation model. With consideration the unique requirements of post-earthquake rescue missions, the model aims to minimize the number of UAVs deployed, reduce rescue costs, and shorten the duration of rescue operations. We propose an innovative hybrid algorithm combining particle swarm optimization (PSO) and grey wolf optimizer (GWO), called the PSOGWO algorithm, to achieve the objectives of the model. This algorithm is enhanced by various strategies, including interval transformation, nonlinear convergence factor, individual update strategy, and dynamic weighting rules. A practical case study illustrates the use of our model and algorithm in reality and validates its effectiveness by comparing it to PSO and GWO. Moreover, a sensitivity analysis on UAV capacity highlights its impact on the overall rescue time and cost. The research results contribute to the advancement of vehicle-routing problem (VRP) models and algorithms for post-earthquake relief in SAR. Furthermore, it provides optimized relief distribution strategies for rescue decision-makers, thereby improving the efficiency and effectiveness of SAR operations.

Implementation of an Electronic Health Record System for Use Inhumanitarian Emergencies, Disaster Response, and Conflict Zones

Abstract Objective Despite the global expansion of electronic medical record (EMR) systems and their increased integration with artificial intelligence (AI), their utilization in disaster settings remains limited, and few studies have evaluated their implementation. We aimed to evaluate Fast Electronic Medical Record (fEMR), a novel, mobile EMR designed for resource-limited settings, based on user feedback. Methods We examined usage data through October 2022 to categorize the nature of its use for disaster response and determine the number of patients served. We conducted interviews with stakeholders and gathered input from clinicians who had experience using fEMR. Results Over eight years, fEMR was employed 60 times in 11 countries across four continents by 14 organizations (universities, non-profits, and disaster response teams). This involved 37,500+ patient encounters in diverse settings including migrant camps at the US-Mexico and Poland-Ukraine borders, mobile health clinics in Kenya and Guatemala, and post-earthquake relief in Haiti. User feedback highlighted adaptability, but suggested hardware and workflow improvements. Conclusion EMR systems have the potential to enhance healthcare delivery in humanitarian responses, offer valuable data for planning and preparedness, and support measurement of effectiveness. As a simple, versatile EMR system, fEMR has been deployed to numerous disaster response and low-income settings.

Determination of most affected areas by earthquakes based on mobile signaling data: a case study of the 2022 Mw 6.6 Luding earthquake, China

Rapid determination of the most-affected areas after an earthquake can help with emergency relief efforts, which is also an essential part of postearthquake relief work. Mobile signaling data contains rich user information, which helps determine the most-affected areas. This study investigated the 2022 Mw 6.6 Luding earthquake in Sichuan, China; the relationship between mobile signaling data and the seismically most-affected area was explored using comparative, regression, and spatial interpolation analysis. The results revealed that mobile signaling data changed abnormally following the earthquake and differed across high- and low-intensity zones. There was a significant correlation between mobile signaling data and intensity. Additionally, the most-affected areas estimated using mobile signaling data were comparable to the actual seismic damage. Therefore, mobile signaling data can provide a reference for determining the most-affected areas and assist in emergency relief efforts after an earthquake.

Optimal Solution Design for Initial Post-earthquake Casualty Dispatch Considering Road Disruption Scenarios: Based on Network Optimization Model

The optimization of casualty dispatch is a key problem faced in the initial post-earthquake relief process. In view of the urgency and uncertainty of casualty rescue in the early post-earthquake period, this paper investigates the decision optimization problem of casualty dispatching in the early post-earthquake period. Based on the fact that current research on emergency disaster relief in the early post-earthquake period rarely considers the bi-objective optimization problem of transport time and system cost, this paper quantifies the risk of road disruption based on the earthquake risk level and designs a location-routing optimization model with the goal of minimizing the cost of the casualty dispatch system and minimizing the casualty transport time, which provides a solution for casualty dispatch in the initial post-earthquake period. Finally, the example of Sichuan Ya’an Lushan earthquake is used to search for the least costly and most efficient emergency rescue solution. Under this solution, even if the road network in some of the affected sites is severely damaged, timely treatment of casualties and optimal transportation system cost can still be ensured. Moreover, it can adjust measures to local conditions, and decision-makers can make a trend between shortening casualty dispatching time and reducing total system cost based on casualty injuries and road network conditions in the affected sites. Based on the casualty situation and the road network condition in the affected area, we make a trade-off between shortening the casualty dispatch time and reducing the total cost of the system to achieve the optimal casualty dispatch benefit.

Robust optimization for casualty scheduling considering injury deterioration and point-edge mixed failures in early stage of post-earthquake relief.

Scientifically organizing emergency rescue activities to reduce mortality in the early stage of earthquakes. A robust casualty scheduling problem to reduce the total expected death probability of the casualties is studied by considering scenarios of disrupted medical points and routes. The problem is described as a 0-1 mixed integer nonlinear programming model. An improved particle swarm optimization (PSO) algorithm is introduced to solve the model. A case study of the Lushan earthquake in China is conducted to verify the feasibility and effectiveness of the model and algorithm. The results show that the proposed PSO algorithm is superior to the compared genetic algorithm, immune optimization algorithm, and differential evolution algorithm. The optimization results are still robust and reliable even if some medical points fail and routes are disrupted in affected areas when considering point-edge mixed failure scenarios. Decision makers can balance casualty treatment and system reliability based on the degree of risk preference considering the uncertainty of casualties, to achieve the optimal casualty scheduling effect.

Implications on Nepal’s Foreign Affairs of the Sino-Indian Rapprochement over Lipulekh

When China and India get closer, will Nepal be uncomfortable with losing its strategicspace? The available literature on Sino-Indian conflict and competition in the Himalayanregion do not divulge the impacts of Sino-Indian cooperation on Nepal. By examining theSino-Indian rapprochement over Nepal’s Lipulekh, this study elaborates the implications ofIndia’s and China’s neighborhood policies on Nepal’s foreign policy. In 2015, while Nepalwas concentrating on post-earthquake relief, rehabilitation, and reconstruction, China andIndia—the two Asian geopolitical rivals, decided to use Nepal’s Lipulekh as a trading corridor,without Nepal’s consent. The Sino-Indian agreement over Nepal’s Lipulekh not only stirredcontroversy in Nepal but also raised questions over the intention of Nepal’s two immediateneighbors. Although 2015 was not the first time that Nepal’s neighbors agreed upon the Lipulekhroute, the case of the Sino-Indian rapprochement in Lipulekh is studied and methodologicallyanalyzed under the framework of exploratory research design. By situating Nepal’s case in theconceptual framework of small states, first, this study surveys Nepal’s foreign policy behaviorin the context of Nepal’s Gulliverian neighborhood through deductive reasoning. Second, theimpacts of the multi-faceted Sino-Indian interactions on Nepal’s foreign policy are identifiedthrough inductive reasoning. Finally, the implications of the neighborhood policies of Chinaand India toward Nepal are discussed against the backdrop of the Sino-Indian rapprochementover Nepal’s Lipulekh.

Casualty scheduling optimisation with facility disruptions under grey information in early stage of post-earthquake relief

PurposeThe purpose of this paper is to propose a new casualty scheduling optimisation problem and to effectively treat casualties in the early stage of post-earthquake relief.Design/methodology/approachDifferent from previous studies, some new characteristics of this stage are considered, such as the grey uncertainty information of casualty numbers, the injury deterioration and the facility disruption scenarios. Considering these new characteristics, we propose a novel casualty scheduling optimisation model based on grey chance-constrained programming (GCCP). The model is formulated as a 0–1 mixed-integer nonlinear programming (MINP) model. An improved particle swarm optimisation (PSO) algorithm embedded in a grey simulation technique is proposed to solve the model.FindingsA case study of the Lushan earthquake in China is given to verify the effectiveness of the model and algorithm. The results show that (1) considering the facility disruption in advance can improve the system reliability, (2) the grey simulation technology is more suitable for dealing with the grey uncertain information with a wider fluctuation than the equal-weight whitening method and (3) the authors' proposed PSO is superior to the genetic algorithm and immune algorithm.Research limitations/implicationsThe casualty scheduling problem in the emergency recovery stage of post-earthquake relief could be integrated with our study to further enhance the research value of this paper.Practical implicationsConsidering the facility disruption in advance is beneficial to treat more patients. Considering the facility disruption in the design stage of the emergency logistics network can improve the reliability of the system.Originality/value(1) The authors propose a new casualty scheduling optimisation problem based on GCCP in the early stage of post-earthquake relief. The proposed problem considers many new characteristics in this stage. To the best of the authors' knowledge, the authors are the first to use the GCCP to study the casualty scheduling problem under the grey information. (2) A MINP model is established to formulate the proposed problem. (3) An improved integer-encoded particle swarm optimisation (PSO) algorithm embedded grey simulation technique is designed in this paper.

Assessment and determination of earthquake casualty gathering area based on building damage state and spatial characteristics analysis

Timely determination of the spatial distribution of post-earthquake relief forces and appropriate levels of response is a daunting task. This paper proposes a method based on building damage state and spatial characteristics analysis to assess the hotspot distribution of earthquake casualties and determine the dangerous gathering area. The method comprises three steps: (1) use the basic information of the buildings in the study area to calculate the building damage state and build the relationship with the casualties; (2) mesh the research area and establish the mapping relationship with the casualties caused by the building damage; (3) use grid data for spatial autocorrelation analysis (Moran's I) and hotspot analysis (Getis-Ord Gi*) to comprehensively assess the casualty cluster area. The feasibility of the proposed method was validated using the seismic damages of Longtoushan Town in the 2014 Ludian earthquake of China. The damage state of buildings and the number of casualties evaluated by this method coincide quite well with the actual earthquake results, and the casualty concentration area is determined. This method quantifies the degree of spatial risk aggregation of casualties and provides scientific guidance for disaster prevention planning and rescue.

Quantifying the Impact of a Tsunami on Data-Driven Earthquake Relief Zone Planning in Los Angeles County via Multivariate Spatial Optimization

Post-earthquake relief zone planning is a multidisciplinary optimization problem, which required delineating zones that seek to minimize the loss of life and property. In this study, we offer an end-to-end workflow to define relief zone suitability and equitable relief service zones for Los Angeles (LA) County. In particular, we address the impact of a tsunami in the study due to LA’s high spatial complexities in terms of clustering of population along the coastline, and a complicated inland fault system. We design data-driven earthquake relief zones with a wide variety of inputs, including geological features, population, and public safety. Data-driven zones were generated by solving the p-median problem with the Teitz–Bart algorithm without any a priori knowledge of optimal relief zones. We define the metrics to determine the optimal number of relief zones as a part of the proposed workflow. Finally, we measure the impacts of a tsunami in LA County by comparing data-driven relief zone maps for a case with a tsunami and a case without a tsunami. Our results show that the impact of the tsunami on the relief zones can extend up to 160 km inland from the study area.

The joint location-transportation model based on grey bi-level programming for early post-earthquake relief

<p style='text-indent:20px;'>The initial period after the earthquake is the prime time for disaster relief. During this period, it is of great value to rationally locate the transfer facilities of relief materials and effectively arrange the transportation of relief materials. Considering the characteristics of the two-level emergency logistics system including uncertain demand, uncertain transportation time, multiple varieties of relief materials, shortage of supply, multi-transportation modes and different urgencies of relief material demand, the integrated issue with the concern of transfer facility location and relief material transportation is studied. Then, this problem is formulated as a grey mixed integer bi-level nonlinear programming in which the upper-level aims at the shortest relief material transportation time and the lower-level aims at the maximum fairness of relief material distribution. According to the characteristics of the model, a hybrid genetic algorithm is designed to solve the proposed model. Finally, a numerical simulation is carried out on the background of 5.12 Wenchuan Earthquake. In addition, the validation of the proposed model and algorithm is verified.</p>

Optimization Dubins Path of Multiple UAVs for Post-Earthquake Rapid-Assessment

In the last decade, with the wide application of UAVs in post-earthquake relief operations, the images and videos of affected areas obtained by UAVs immediately after a seismic event have become an important source of information for post-earthquake rapid assessment, which is crucial for initiating effective emergency response operations. In this study, we first consider the kinematic constraints of UAV and the Dubins curve is introduced to fit the shortest flyable path for each UAV that meets the maximum curvature constraint. Second, based on the actual requirements of post-earthquake rapid assessment, heterogeneous UAVs, multi-depot launching, and targets allowed access to multiple times, the paper proposes a multi-UAV rapid-assessment routing problem (MURARP). The MURARP is modeled as the multi-depot revisit-allowed Dubins TOP with variable profit (MD-RDTOP-VP) which is a variant of the team orienteering problem (TOP). Third, a hybrid genetic simulated annealing (HGSA) algorithm is developed to solve the problem. The result of numerical experiments shows that the HGSA algorithm can quickly plan flyable paths for heterogeneous UAVs to maximize the expected profit. Finally, a case study based on real data of the 2017 Jiuzhaigou earthquake in China shows how the method can be applied in a post-earthquake scenario.

A multi-objective approach for locating temporary shelters under damage uncertainty

Every year, natural disasters such as earthquakes, hurricanes, landslides, etc. kill thousands of people and destroy habitats and assets worth millions-of-dollars. Choice of temporary shelter areas and subsequent relocation of homeless people play a crucial role in post-earthquake relief operations. This paper proposes a multi-objective location-relocation model based on goal programming approach considering uncertainties of damage to infrastructure due to earthquakes. The model considers multiple objectives of risk, number of sites, unmet demand and qualitative suitability of locations and generates solutions under different scenarios of damage. A numerical illustration is also presented to demonstrate the applicability of proposed approach in solving the decision problem.

A multi-objective approach for locating temporary shelters under damage uncertainty

Every year, natural disasters such as earthquakes, hurricanes, landslides, etc. kill thousands of people and destroy habitats and assets worth millions-of-dollars. Choice of temporary shelter areas and subsequent relocation of homeless people play a crucial role in post-earthquake relief operations. This paper proposes a multi-objective location-relocation model based on goal programming approach considering uncertainties of damage to infrastructure due to earthquakes. The model considers multiple objectives of risk, number of sites, unmet demand and qualitative suitability of locations and generates solutions under different scenarios of damage. A numerical illustration is also presented to demonstrate the applicability of proposed approach in solving the decision problem.

Natural disaster and political trust: A natural experiment study of the impact of the Wenchuan earthquake

How do natural disasters impact political trust in contemporary China and what is the causal mechanism? Existing literature indicates that the severity of disaster, government relief effort and information dissemination are three key factors influencing people’s political trust in the context of acute natural disasters. This study uses the Wenchuan earthquake as a natural experiment and focuses specifically on the survey data collected right before and after the earthquake. It finds that primarily due to the ‘rally round the flag’ effect and extensive media coverage, public trust in government officials at all levels rose significantly after the earthquake. During the crisis, state-run media played a vital intermediate role. The more a citizen was exposed to the official media coverage, the more likely his/her political trust was to increase. However, the division of work in disaster relief among different levels of government resulted in differences in the level of increase in trust. As local-level governments are often directly responsible for the rescue and post-earthquake relief, they gained the highest increase in political trust, while state-level officials gained the least. The short-run upsurge in political trust receded as time went by. Government mobilization and media coverage are core contributing factors to the increase in political trust during the post-disaster period. Nevertheless, the key to consolidating political legitimacy lies in long-term efforts to build good and effective governance.

Use of Variable Fuzzy Clustering to Quantify the Vulnerability of a Power Grid to Earthquake Damage

The power grid is a critical component of city infrastructure. If it is damaged by an earthquake, there can be a huge impact on the safety and well-being of society and individuals. Identifying nodes in the grid that are highly vulnerable to earthquake damage is significant for effective pre-earthquake damage prevention, emergency response, and post-earthquake relief. Three indicators, the probability of node disconnection, the node hierarchical level, and the node critical threshold, were chosen, and their combined ability to represent node vulnerability to damage from an earthquake event was analyzed. A variable fuzzy clustering model was used to classify and order the nodes in the grid. The 20-node power grid of a city was used as an example to show how highly vulnerable nodes were identified, and how the reasons for the high vulnerability of these nodes were drawn out of the analysis. Countermeasures were given to reduce network vulnerability. The variable fuzzy clustering method used in this paper offers a new perspective on network vulnerability, and it quantifies the vulnerability of grid nodes more comprehensively than existing methods of assessing grid vulnerability. This research is significant as a baseline reference for future studies of grid vulnerability.

An automatic method for road centerline extraction from post-earthquake aerial images

Road vector database plays an important role in post-earthquake relief, rescue and reconstruction. However, due to data privacy policy, it is difficult for general users to obtain high-precision and complete vector data of road network. The OpenStreetMap (OSM) project provides an open-source, global free road dataset, but there are inevitable geo-localization/projection errors, which will lead to large errors in hazard survey analysis. In this paper, we proposed a road centerline correction method using post-earthquake aerial images. Under the constraint of the vector road map (OpenStreetMap), we rectified the centerline by the context feature and spectral gradient feature of post-event images automatically. The experiment implemented on 0.5 m/pixel post-event aerial images of Haiti, 2010, showed that the completeness and extraction quality of proposed method were over 90% and 80% without any manual intervention.

Estimation of Fundamental Time Periods of Elevated Reservoirs

Elevated water reservoirs commonly known as overhead water tanks are important structures and need to remain functional after major seismic events. Failure of these structures adds to the misery of the affected population and adversely affects the post-earthquake relief operations. Therefore these structures are designed for higher seismic loads. Fundamental time period, which is defined as first mode time period of vibration of a structure, has great influence on expected seismic load on the structure. Precise estimation of fundamental time period of a structure, thus, is very important for its earthquake resistant design. Ambient vibration testing have been carried out across the world to find fundamental time period of structures like buildings, bridges etc. and may become useful tool for precise estimation of fundamental time period of elevated water tanks also. The work summarizes the results of experimental study on ambient vibration testing of reinforced concrete elevated water tanks. During this study, a total of five reinforced concrete elevated water tanks are considered for determining their fundamental time periods. The tanks are situated in Gwalior in State of Madhya Pradesh, India. The tanks considered for testing are also modeled as per prevailing design practices to estimate their fundamental time period analytically. By comparing the real values obtained by ambient vibration testing and the analytical values, it is found that the analytical method prescribed by IS code, in general, results in good estimation of fundamental time period of elevated water tanks. However, the water depth to plan size ratio, flexibility of braces and stiffness of staircase also influences the value and should also be considered for analytical estimation.

Bi-objective covering tour location routing problem with replenishment at intermediate depots: Formulation and meta-heuristics

The Covering tour Location Routing Problem with Replenishment at intermediate depots (CLRPR) is an extension of location routing problem with service time restriction, replenishment at intermediate depots, and customer mobility in a predefined walking distance. Among the different applications of the problem, this study concentrates on the post-earthquake relief distribution system. This paper represents a new bi-objective integer linear programming model that minimizes the total weighted waiting time and the total amount of lost demands. The mathematical model is coded in GAMS software and solved optimally by Cplex solver with epsilon-constraint method. In order to cope with the NP-hardness feature of the problem, the NSGAII multi-objective algorithm with two distinct improvements are proposed as heuristic solution procedures. The results of 36 randomly generated test problems were analyzed in terms of quality, quantity, diversity and spread of Pareto front solutions.

Mapping surface motion parameters and liquefaction susceptibility in Tribhuvan International Airport, Nepal

ABSTRACTLocal site effects and liquefaction are major causes of building and lifeline damage in Kathmandu Valley as depicted by several historical earthquakes including recent 2015 Gorkha earthquake (Mw 7.8). Critical facilities like airports play important role in post-earthquake relief, response and recovery efforts. Tribhuvan International Airport (TIA) is the only international airport in Nepal that is located in alluvial soil deposit regarded as Gokarna formation. To assess the site effects and liquefaction occurrence in TIA, this study performs one-dimensional ground response analysis as well as liquefaction susceptibility analysis using shallow borehole logs. Surface motion parameters estimated from one-dimensional ground response analysis are mapped for 0.3g and 0.5g peak ground acceleration inputs. In addition to this, qualitative liquefaction susceptibility map is prepared considering the liquefaction potential index estimated for each borehole log.

Reusable sanitary towels: promoting menstrual hygiene in post-earthquake Nepal

It is a normal human tendency to ‘run for your life’ when an earthquake occurs. Adolescent girls and women of reproductive age leave their homes with only the clothes they...