Adaptive Emergency Evacuation Centre Management for Dynamic Relocation of Flood Victims using Firefly Algorithm

Situations during bad flood demands a systematic management of evacuation centres. Flood evacuation centres which are gazeted as temporary locations to evacuate flood victims have chances from being drowned by flood. Problem occurs to relocate flood victims when the evacuations centres are flooded. Currently, there is no study done on proposing a decision support aid to reallocate victims and resources of the evacuation centre when the situation getting worsens. Therefore, this article proposes a decision aid model to be utilized in realizing an adaptive emergency evacuation centre management system. This study undergoes two main phases; development of algorithm and models, and development of a web-based and mobile app. The proposed model operates using Firefly multi-objective optimization algorithm that creates an optimal schedule for the relocation of victims and resources for an evacuation centre. The proposed decision aid model and the adaptive system can be applied in supporting the National Security Council’s respond mechanisms for handling disaster management level II (State level) especially in providing better management of the flood evacuating centres

Adaptive emergency evacuation centre management for dynamic relocation of flood victims using firefly algorithm

Situations during bad flood demands a systematic management of evacuation centres. Flood evacuation centres which are gazeted as temporary locations to evacuate flood victims have chances from being drowned by flood. Problem occurs to relocate flood victims when the evacuations centres are flooded. Currently, there is no study done on proposing a decision support aid to reallocate victims and resources of the evacuation centre when the situation getting worsens. Therefore, this article proposes a decision aid model to be utilized in realizing an adaptive emergency evacuation centre management system. This study undergoes two main phases; development of algorithm and models, and development of a web-based and mobile app. The proposed model operates using Firefly multi-objective optimization algorithm that creates an optimal schedule for the relocation of victims and resources for an evacuation centre. The proposed decision aid model and the adaptive system can be applied in supporting the National Security Council’s respond mechanisms for handling disaster management level II (State level) especially in providing better management of the flood evacuating centres

Application of Integer Programming for Mine Evacuation Modeling with Multiple Transportation Modes

The safe evacuation of miners during an emergency within the shortest possible time is very important for the success of a mine evacuation program. Despite developments in the field of mine evacuation, little research has been done on the use of mine vehicles during evacuation. Current research into mine evacuation has emphasized on miner evacuation by foot. Mathematical formulations such as Minimum Cost Network Flow (MCNF) models, Ant Colony algorithms, and shortest path algorithms including Dijkstra's algorithm and Floyd-Warshall algorithm have been used to achieve this. These models, which concentrate on determining the shortest escape routes during evacuation, have been found to be computationally expensive with expanding problem sizes and parameter ranges or they may not offer the best possible solutions.An ideal evacuation route for each miner must be determined considering the available mine vehicles, locations of miners, safe havens such as refuge chambers, and fresh-air bases. This research sought to minimize the total evacuation cost as a function of the evacuation time required during an emergency while simultaneously helping to reduce the risk of exposure of the miners to harmful conditions during the evacuation by leveraging the use of available mine vehicles. A case study on the Turquoise Ridge Underground Mine (Nevada Gold Mines) was conducted to validate the Integer Programming (IP) model. Statistical analysis of the IP model in comparison with a benchmark MCNF model proved that leveraging the use of mine vehicles during an emergency can further reduce the total evacuation time. A cost-savings analysis was made for the IP model, and it was found that the time saved during evacuation, by utilizing the IP model, increased linearly, with an increase in the number of miners present at the time of evacuation

Risk management in Norwegian avalanche rescue operations. Managing uncertainty, complexity, overcommitment and the long-term monitoring of accident risk

PhD thesis in Risk management and societal safetyIntroduction: Avalanche incidents commonly take place in adverse environmental conditions, and the expected survival time of avalanche victims is short. These situations require an immediate rescue response, which may pose a serious challenge to the safety of both rescuers and avalanche victims. Historically, the Norwegian rescue service has experienced few serious accidents, but undesirable incidents where rescuers are dangerously exposed in avalanche runout zones seem more frequent. Risk management in the avalanche rescue service is multifaceted, influenced by its multi-organizational structure. Individuals acting in this socio-technical rescue system are easily caught between two imperatives: saving lives and staying alive. The aim of risk management is to maintain equilibrium in rescue commitment. This project analysed whether the Norwegian avalanche rescue system is correctly balanced to withstand the extra load of common risk influencing factors in rescue operations. Aim: The fundamental aim of this thesis was to contribute to new knowledge on factors that are important for risk management and performance in the Norwegian avalanche rescue service. Methods: Mixed methods research was applied to answer the specific research questions. This implied multiple research activities in a combination of quantitative and qualitative methodologies. Study number 1 was a retrospective study to characterize Norwegian avalanche incidents and rescue response (Paper I). A comprehensive study comprising avalanche rescue statistics, cross-case analysis, factor analysis and risk modelling was conducted to gain insight into avalanche rescue performance (Paper II). In a phenomenological study to explore the concept of overcommitment, nine air ambulance crews from five bases took part in focus group interviews (Papers III and IV). Lastly, a systemic safety analysis was conducted in two separate seminars, supported by the insight of six experts in Norwegian avalanche rescue operations (Paper V). The thesis itself is a cross-paper synthesis of results. Results: The studies returned results which contribute to justified beliefs about patient and rescuer safety in Norwegian avalanche rescue operations. Conclusion: A synthesis of results from the various studies indicates that the Norwegian rescue service is vulnerable to common risk sources in rescue operations, affecting the safety of both rescuers and patients. The avalanche rescue system could benefit from a focus on the integrity of already established safety barriers. This implies an interorganizational effort to identify and reach common goals and system requirements. This thesis may serve as input to discussions on risk acceptance levels in the rescue service, the applicability and validity of control algorithms in rescue management and how to adjust the degree of commitment in various rescue missions

Analytic prioritization of indoor routes for search and rescue operations in hazardous environments

Applications to prioritize indoor routes for emergency situations in a complex built facility have been restricted to building simulations and network approaches. These types of applications often failed to account for the complexity and trade-offs needed to select the optimal indoor path during an emergency situation. In this article, we propose a step change for finding the optimal routes for Search And Rescue (SAR) teams in a building, where a multi-epicentre extreme event is occurring. We have developed an algorithm that is based on a novel approach integrating the Analytic Hierarchy Process (AHP), statistical characteristics, the propagation of hazard, Duckham-Kulik’s adapted algorithm, Dijkstra's classical algorithm, and the binary search with three criteria: hazard proximity, distance/travel time, and route complexity. The sub-criteria for the route complexity are validated in the context of SAR using a real-life building (Doha World Trade Centre). The important feature of the algorithm is its ability to generate an optimal route depending on user’s needs. The findings revealed that the generated optimal routes are indeed the ‘best’ trade-off amongst distance/travel time, hazard proximity and route complexity. The test results also demonstrated the robustness of the algorithm with respect to different parameters, and its insensitivity to different scenarios of uncontrolled evacuation

Disaster recovery heuristic

A need exists to develop a software simulation that demonstrates the most effective methods of evacuation for disaster scenarios. In a real-world situation this heuristic coupled with real-time data gathered by sensors could serve to provide an efficient rescue plan. Data gathered about the terrain in the immediate aftermath of the situation is invaluable in deciding a plan of action. With this type of information many different routes can be planned so that recovery or rescue can be made as optimal as possible. But of course in any rescue mission speed also is of the utmost importance. This is why we must explore heuristics that make the processing of the collected data faster. The result of this processing must be dependable and must significantly enhance the success of the rescue mission. This work proposes such a heuristic. The results obtained from this heuristic is compared with the results obtained from a process that best mimics an ad-hoc retrieval. Keeping in mind that human ingenuity can never be replaced, in this thesis we create a heuristic that will render a reliable plan of action yielding more predictable results in a disaster recovery situation. Here optimum retrieval means an act of recovery or restoration from any terrain in the most efficient way. Such a process of recovery is very useful when faced with a disaster scenario such as a hurricane or a manmade calamity on a large scale

Contribution des services dirigés par l’ontologie pour l’interopérabilité de la gestion opérationnelle multi-acteurs des situations des crises

La gestion opérationnelle de situations de crise nécessite, selon l’importance et l’étendue de la crise, la mobilisation rapide et la coordination des différents services de secours. Malheureusement, cette coordination interservices est un exercice très délicat du fait de la diversité des acteurs intervenant sur le terrain et de l’hétérogénéité des différentes organisations. Aujourd’hui, il y a un manque de coordination, l’information n’est que très peu partagée entre les acteurs opérationnels et la communication n’est pas formalisée. Ces inconvénients conduisent au dysfonctionnement des réponses aux situations de crise. Afin de mieux répondre aux situations de crise, nous proposons POLARISC, une plateforme interopérable de coordination interservices pour la gestion opérationnelle de catastrophes visualisant en temps réel le théâtre des opérations. L’objectif de POLARISC est d’aider à la décision quel que soit le niveau de commandement. Pour atteindre ces objectifs, le premier enjeu de cette thèse est de garantir une interopérabilité sémantique entre les différents acteurs métiers pour assurer l’échange et le partage des informations. À cet égard, l’idée est de formaliser sémantiquement les connaissances des acteurs métiers de la gestion opérationnelle à l’aide des ontologies. En effet, nous proposons une approche fédérée qui représente les données, les services, les processus et les métiers de chaque acteur. Nous avons modélisé les connaissances des acteurs de secours en développant une ontologie modulaire (POLARISCO) comportant un module ontologique pour chaque acteur de secours et intégré ces derniers pour proposer un vocabulaire partagé. L’utilisation des ontologies de haut niveaux et des ontologies intermédiaires, respectivement « Basic Formel Ontology » et « Common Core Ontologies », facilitent l’intégration de ces modules et de leurs mappings. Le deuxième enjeu est d’exploiter ces ontologies afin de diminuer l’ambigüité et d’éviter la mal interprétation des informations échangées. Par conséquent, nous proposons un service de messagerie appelé PROMES transformant sémantiquement le message envoyé par un acteur émetteur selon le module ontologique de l’acteur destinataire. En effet, PROMES se base sur l’ontologie POLARISCO et sert à enrichir sémantiquement le message pour éviter tout type d’ambiguïté. Le fonctionnement de PROMES est basé principalement sur deux algorithmes ; un algorithme de transformation textuelle, et par la suite, un algorithme de transformation sémantique. Ainsi, nous avons instancié l’ontologie POLARISCO avec des données réelles de la réponse aux attaques terroristes de Paris en 2015 afin d’évaluer l’ontologie et le service de messagerie. Le troisième et dernier enjeu est de proposer un service d’aide à la décision multicritère qui permet de proposer des stratégies d’évacuation des victimes après le lancement du plan blanc. L’objectif est de trouver les structures hospitalières les plus adaptées à l’état de la victime. Le choix de l’hôpital le plus approprié dépend de la durée du transport, et surtout de la disponibilité des ressources matérielles et humaines, de façon à prendre en charge les victimes le plus rapide que possible. Notre étude comprend deux étapes : la première étape consiste à développer un module ontologique qui associe à chaque pathologie les ressources indispensables pour une meilleure prise en charge des victimes selon leurs états. La deuxième étape consiste à développer un algorithme qui permet de vérifier la disponibilité des ressources nécessaires, calculer le temps d’attente pour que la victime soit prise en charge dans chaque hôpital et par la suite choisir l’hôpital le plus appropri

Resilience of critical structures, infrastructure, and communities

In recent years, the concept of resilience has been introduced to the field of engineering as it relates to disaster mitigation and management. However, the built environment is only one element that supports community functionality. Maintaining community functionality during and after a disaster, defined as resilience, is influenced by multiple components. This report summarizes the research activities of the first two years of an ongoing collaboration between the Politecnico di Torino and the University of California, Berkeley, in the field of disaster resilience. Chapter 1 focuses on the economic dimension of disaster resilience with an application to the San Francisco Bay Area; Chapter 2 analyzes the option of using base-isolation systems to improve the resilience of hospitals and school buildings; Chapter 3 investigates the possibility to adopt discrete event simulation models and a meta-model to measure the resilience of the emergency department of a hospital; Chapter 4 applies the meta-model developed in Chapter 3 to the hospital network in the San Francisco Bay Area, showing the potential of the model for design purposes Chapter 5 uses a questionnaire combined with factorial analysis to evaluate the resilience of a hospital; Chapter 6 applies the concept of agent-based models to analyze the performance of socio-technical networks during an emergency. Two applications are shown: a museum and a train station; Chapter 7 defines restoration fragility functions as tools to measure uncertainties in the restoration process; and Chapter 8 focuses on modeling infrastructure interdependencies using temporal networks at different spatial scales