Дисертації з теми "Disaster modelling"

Questa ricerca intende contribuire alla discussione sulla riduzione del rischio disastri (DRR), esplorando come le comunità locali dovrebbero adattarsi ai pericoli che le circondano. La prima parte riporta la teoria della panarchia alle dinamiche del rischio. Il modello teorico che ne deriva, la Panarchia Sociale-Ecologica, descrive le condizioni di rischio e permette di riconoscere i nuclei del DRR: la resilienza ai disastri e la sostenibilità ambientale. Il modello fornisce le basi per lo sviluppo di una Valutazione Combinata di Resilienza e Sostenibilità, concentrata sul rischio inondazione alla scala comunale. La seconda parte svolge un’analisi quantitativa attraverso indicatori, che identificano e caratterizzano i livelli di resilienza e sostenibilità. La terza parte impiega strumenti qualitativi (questionari) per raccogliere le percezioni delle comunità locali sui rischi presenti nei loro Comuni. L’analisi è stata applicata a due casi studio, la Regione Marche (Italia) e l’Hokkaidō (Giappone). I risultati mostrano il ruolo delle inondazioni nel determinare la resilienza locale, e degli impatti antropici per la sostenibilità. Le criticità maggiori sono concentrate nelle aree montane/collinari. Allo stesso tempo, aspetti di welfare e sicurezza sociale risultano fondamentali per formare la resilienza, così come la presenza di vegetazione lo è per la sostenibilità. Inoltre, emerge una sostanziale differenza fra misurazione e percezione di resilienza e sostenibilità, generalmente in senso peggiorativo. In generale, ulteriori sforzi dovrebbero essere diretti alle aree interne, benché la regione intera gioverebbe del consolidamento della resilienza locale. Inoltre, le comunità sembrano molto sensibili ai temi ambientali, per cui potrebbero appoggiare sforzi per aumentare la sostenibilità. Infine, questi studi possono contribuire alle strategie DRR, per promuovere l’adattamento trasformativo delle comunità locali, reso urgente dall’esasperazione degli eventi estremi.
This research intends to contribute to the discussion on disaster risk reduction (DRR), investigating the question of how local communities should adjust to the surrounding threats. The first part adapted the panarchy heuristics to risk dynamics. The drawn theoretical model, the Social-Ecological Panarchy, could describe the conditions of risk and allow to recognise the two cores of DRR: disaster resilience and environmental sustainability. The model supported the development of a Combined Assessment of Resilience and Sustainability, focused on flood risk at the Municipal scale. The second part of the research performed a quantitative analysis through numerical indicators, that identified and characterised the levels of resilience and sustainability. The third part of the research employed qualitative tools (questionnaires) to gather the thoughts of local communities on the risks affecting their Municipalities. The analysis was applied to two case studies, Marche Region (Italy) and Hokkaidō (Japan). Results evidenced the role of flood events in determining the resilience capacities of local communities, and of the anthropic impacts for defining their sustainability. Most critical issues lied in the mountainous/hill areas. At the same time, social welfare and protection appeared pivotal in building local resilience, while the presence of vegetation shaped sustainability. Besides, a substantial mismatch emerged between assessed and perceived conditions of resilience and sustainability, generally in negative terms. Overall, it appeared that further efforts should be tailored to the innermost areas, though the overall region might benefit from consolidated resilience. At the same time, local populations seemed highly responsive to environmental issues, possibly endorsing the enhancement of sustainability. Eventually, these insights might inform risk reduction strategies, to foster a transformative adaptation of local communities, urged by exacerbating disruptive threats.

Application of agent-based modelling technology (ABM) to disaster management has to date been limited in nature. Existing research has concentrated on extending the model structures and agent architectures of complex algorithms to test robustness and extensibility of this simulation approach. Less attention has been brought to bear on testing the current state-of-the-art in ABM for modelling real-life systems. This thesis aims to take first steps in remedying this gap. It focuses on identifying the practical and conceptual issues which preclude wider utilisation of ABM in disaster management. It identifies that insufficient attention is put on incorporating real-life information and domain knowledge into model definitions. This research first proposes a methodology by which some of these issues may be overcome, and consequently tests and evaluates it through implementation of InSiM (Incident Simulation Model), which depicts reaction of pedestrians to a CBRN (chemical, biological, radiological or nuclear) explosion in a city centre. A number of steps are conducted to obtain real-life information related to human response to CBRN incidents. This information is then used for design and parameterisation of InSiM which is implemented in three configurations. In order to identify the effects use of real-life data have on the simulation results each configuration incorporates the information at different level of complexity. The effects are assessed by comparison of the generated dispersion patterns of agents along the city centre. However, use of conventional statistical goodness-of-fit tests for assessing the degree of the difference was challenged by inhomogeneous nature of the data. Hence, alternative approaches are also adopted so that results can be qualitatively assessed. Nevertheless, the evaluation reveals significant differences at global and local level. This research highlights that incorporation of real-life information and domain knowledge into ABM is not without problems. Each time a problem was addressed, additional issues began to emerge. Most of these challenges were related to generalisation of the complex real-life systems that the model represents. Therefore, further investigations are needed at every methodological step before ABM can fully realise its potential to support disaster management.

A large number of natural disasters affects hundreds of thousands of people each year in their housing around the world. Therefore, there is a call to find more appropriate strategies for housing reconstruction following a disaster. This study aims to reduce the construction time and cost of housing affected by such disasters. The academic literature on the 3 Dimensional Lightweight Panels construction system (3D-LPs), Building Information Modelling system (BIM) and experiences gained in post-disaster housing reconstruction strengthens the argument that here is an opportunity to contribute to solve the housing reconstruction problem. The study points out that the combination of these systems and community participation presents an option to produce both affordable and sustainable housing in the shortest time on a large scale by the affected people after overcoming the emergence phase of a disaster. A holistic philosophy was used to study the housing reconstruction problem as a whole to understand all parts of the problem and three research questions were set up to explore the possible solution to this problem. The research strategy to address the problem was based on a survey of worldwide experts, interviewing a forum of lightweight concrete panel manufacturers and the modelling of a basic housing prototype in BIM. Research question (1) How can displaced people use their own labour to save money and time? and research question (2) How does the 3D-LPs construction system contribute to housing recovery after natural disasters? Research questions (1) and (2) were answered by 17 open-ended questions conducted with 22 housing experts from 11 countries and 7 semi-structured interviews composed of 14 questions with 7 manufacturers of construction materials respectively which collected rich qualitative data (15,419 words) that were analysed in Nvivo 10 through pattern matching and validated by triangulation techniques to give reliability to the study. The housing prototype modelling was used to answer the research question (3) Can the BIM model show the cost-benefit in building housing with the 3D-LPs construction system and displaced people's own labour?The main findings of this study are that a housing prototype built with 3D-LPs is 36.82% cheaper in comparison to houses built with bricks and reinforcement elements and could be built by unskilled people in 90 days. The study provides novel in-depth knowledge of how unskilled people from communities affected should participate in housing reconstruction and how new construction systems can be implemented after disasters, which contributes to the body of knowledge. In addition, the study provides guidelines to implement a system directed at unskilled people and also Non-Governmental Organizations (NGOs) in a novel way, to help to solve the housing reconstruction problem and engage the displaced people in the housing reconstruction.

Landslides cause enormous economic damage and fatalities worldwide. The 2011 Mega disaster in the Rio de Janeiro mountainous region is considered the worst landslide disaster in Brazilian history. Traditionally, risk topic has been analyzed from a purely engineering-based perspective, which has proved to have an ineffective response to face the challenges posed by physical and social factors, especially in low-income countries. This thesis introduces a conceptual framework for an integrated risk assessment and undertakes the proposal in a practical way in the Nova Friburgo municipality, as a case study. In the first part of this research, an assessment of the physical component of risk was addressed. Three scenarios of landslide susceptibility were performed using a 10m-resolution DEM, geotechnical data and a landslide inventory. Findings suggest that the scenario with a wide range of cohesion parameter values was able to predict almost 70% of the inventoried landslides and about 50% of the territory with landslide-prone areas. In the second part of this thesis, a deep analysis of human component of risk is performed. A social vulnerability assessment- using the SoVI methodand data collection disaggregated by age, sex and race/ethnicity of the 2011 landslide-related fatalities were conducted. Results reveal differential social vulnerability among census tracts. Most of them were classified as moderately vulnerable. Although highly social vulnerable areas were not widely distributed in the territory, they are important because of their location and implications for the municipality economic matrix. Regarding the 2011 landslide-related fatalities, 434 casualties were registered. Spatial analysis indicates that the highest mortality was located at the northwest and central municipality zones. Landslide disaster affected males and females differently. In most age groups, landslides have killed more men and boys than women and girls. Fifty percent of those who lost their lives were the youngest and the elderly. The black population had a slightly higher mortality rate than either the brown (Pardos) and white ones. Data did not reveal a discernible trend in the association between social vulnerability and casualties. It seems that the landslide quantity and magnitude was so great that all of Nova Friburgo inhabitants were equally reached, beyond the inequalities expressed by their social vulnerability. In the third part of this inquiry, to predict landslide risk probability, the social vulnerability and the landslide susceptibility predictors were combined using the Generalized Additive Model (GAM). Findings suggest that in instable terrains, is enough a moderate social vulnerability level to increase the probability of landslide risk. Results also highlight model capacity to uncover hidden patterns in the dataset, capturing a nonlinear effect of social vulnerability predictor and a linear effect of terrain stability predictor. In conclusion, the proposed conceptual framework is generic and flexible, so can be applied to other areas, analysis scales and natural hazard types although some adaptation would be necessary depending on available data. Furthermore, the integrated approach performed in this thesis highlights that it is feasible and necessary linking data from different science domains to better understand disaster risk complexity, reducing risk and curbing losses of both human lives and economic assets through knowledge-based actions. It should be noted that this thesis research complies with guidelines given at the first priority area for action of the Sendai Framework for Disaster Risk Reduction 2015-2030, which outlines the importance of Understanding disaster risk. Finally, resulting knowledge provides Nova Friburgo community, organizations and governments with a basis to comprehend the risk related to a specific natural hazard: the landslides which can be leveraged to achieve an effective preparation and response to future disasters and also to promote disaster-resilient societies.
Os deslizamentos de terra causam enormes prejuízos econômicos e mortes em todo o mundo. O Mega desastre 2011 na região montanhosa do Rio de Janeiro é considerado o pior desastre na história do Brasil. Tradicionalmente, o tópico de risco foi analisado a partir de uma perspectiva puramente baseada na engenharia que provou ter uma resposta ineficaz para enfrentar os desafios impostos por fatores físicos e sociais, especialmente em países de baixa renda. Esta tese apresenta um marco conceitual para uma avaliação integrada do risco e realiza a proposta de maneira prática no município de Nova Friburgo, como um estudo de caso. Na primeira parte desta pesquisa, uma avaliação do componente físico do risco foi abordada. Três cenários de suscetibilidade a escorregamentos foram realizados usando um DEM de 10m de resolução espacial, dados geotécnicos e um inventário de deslizamentos. Os resultados sugerem que o cenário que utilizou uma ampla gama de valores de coesão foi capaz de prever quase 70% dos deslizamentos de terra inventariados e cerca de 50% do território com áreas propensas a deslizamentos. Na segunda parte desta tese, se analisou o componente humano de risco. Uma avaliação de vulnerabilidade social - usando o método SoVI - e a coleta de dados desagregados por idade, sexo e raça/etnia dos óbitos provocados pelos deslizamentos de 2011 foram realizados. Os resultados revelam uma vulnerabilidade social diferenciada entre os setores censitários. A maioria deles foi classificada como moderadamente vulnerável. Embora as áreas altamente vulneráveis não sejam amplamente distribuídas no território, elas são importantes devido à sua localização e implicações para a matriz econômica do município. Em relação aos óbitos por deslizamentos de terra, foram registradas434 vítimas. A análise espacial indica que a maior mortalidade se localizou nas zonas do noroeste e centro do município. O desastre provocado pelos deslizamentos de terra afetou aos homens e mulheres de maneira diferente. Na maioria das faixas etárias, morreram mais homens e meninos do que mulheres e meninas. Cinquenta por cento daqueles que perderam suas vidas eram os mais jovens e os idosos. A população negra teve uma taxa de mortalidade ligeiramente maior do que os Pardos e brancos. Os dados não revelaram uma tendência discernível na associação entre vulnerabilidade social e óbitos. Parece que a magnitude dos deslizamentos foi tão grande que todos os habitantes de Nova Friburgo foram igualmente atingidos, além das desigualdades expressas por sua vulnerabilidade social. Na terceira parte desta investigação, determinou-se a probabilidade de risco de deslizamento, para isso a vulnerabilidade social e a susceptibilidade aos deslizamentos de terra foram combinados usando o Modelo Aditivo Generalizado (GAM). Os resultados sugerem que, em terrenos instáveis, basta um nível moderado de vulnerabilidade social para aumentar a probabilidade de risco de deslizamento. Os resultados também destacam a capacidade do modelo de descobrir padrões oculto no conjunto de dados, capturando um efeito não linear da variável vulnerabilidade social e um efeito linear da variável estabilidade do terreno. Em conclusão, o marco conceptual proposto é genérico e flexível pelo que pode ser aplicado a outras áreas, escalas de análise e tipos de perigos naturais, embora seja necessária alguma adaptação, dependendo dos dados disponíveis. Além disso, a abordagem integrada desta tese destaca que é viável e necessário vincular dados de diferentes domínios científicos para melhor compreender o risco de desastres, reduzir riscos e reduzir perdas de vidas humanas e ativos econômicos por meio de ações baseadas em conhecimento. Deve-se notar que esta pesquisa está em conformidade com as diretrizes dadas na primeira área prioritária para a ação do Marco de Sendai para a Redução de Riscos de Desastres 2015-2030, que descreve a importância de "Compreender o risco de desastres". Finalmente, o conhecimento resultante desta pesquisa fornece à comunidade, às organizações e ao governo de Nova Friburgo uma base para compreender o risco relacionado a um perigo natural específico: "os deslizamentos" que podem ser aproveitados para obter uma preparação melhor e respostas eficazes a desastres futuros e também para promover sociedades resilientes aos desastres.

Economic and social stability across the world is tightly dependent on a sufficient and reliable flow of goods and services, based on the balance between supply and demand. The basis for the balance between supply and demand is a set of physical assets, processes and organisations that are collectively known as critical infrastructure systems (CIs). CIs are strongly interdependent in two complex ways: namely, physical and informational. Identification of CI interdependencies and interconnections is essential for the study of such supply and demand systems. Importantly, CI interdependencies and interconnections are also essential for the development of models that help in the study of the impact of extreme events or disasters, or prediction of economic loss from damage caused by cascading impacts on the supply and demand chain system. Based on the stated problem, the aims of this research can be summarized as an estimation of economic loss. This estimation includes individual industry loss and total economic loss, taking interconnected industries into consideration. The main aims are to develop and test a new model and to develop a methodology based on this model for the assessment and management of risk from extreme events on critical infrastructure systems.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Griffith School of Environment
Science, Environment, Engineering and Technology
Full Text

La modélisation des catastrophes et la localisation des installations sont des aspects essentiels de la gestion des catastrophes qui contribuent à améliorer l’efficacité et l’efficience des chaînes d’approvisionnement des secours. Cependant, les incertitudes inhérentes aux catastrophes et aux chaînes d’approvisionnement des secours peuvent avoir un impact significatif sur l’efficacité de ces modèles. Pour relever ce défi, la thèse propose l'utilisation de modèles basés sur la quantification de l'incertitude et de modèles basés sur Markov caché pour la modélisation des catastrophes dans le contexte de la chaîne d'approvisionnement des secours marocains. La thèse commence par conceptualiser la chaîne d'approvisionnement de secours marocaine, décrivant de manière exhaustive sa conception, ses activités et les différents acteurs impliqués dans le processus humanitaire, puis une analyse détaillée a été menée pour mettre en évidence les forces et les faiblesses de la chaîne d'approvisionnement de secours marocaine. Cela impliquait un examen approfondi des sources d’incertitude au sein du processus humanitaire, afin de bien comprendre les défis rencontrés au sein de la chaîne d’approvisionnement humanitaire marocaine et d’identifier les exigences spécifiques. À la suite de ce travail de base conceptuel, les modèles proposés sont ensuite appliqués à une étude de cas dédiée à la chaîne d’approvisionnement humanitaire marocaine. Cette application pratique vise à valider l'efficacité des modèles basés sur la quantification de l'incertitude et des modèles basés sur Markov caché dans un scénario réel, fournissant des informations précieuses sur leur applicabilité, leur utilité et leur impact potentiel sur la dynamique complexe du domaine humanitaire. Les résultats démontrent que le modèle basé sur la quantification de l'incertitude et le modèle basé sur Markov caché peuvent améliorer considérablement la robustesse et l'efficacité du réseau de la chaîne d'approvisionnement en termes de prévision des catastrophes. Le modèle basé sur la quantification de l'incertitude permet de prédire l'impact humain potentiel des catastrophes et des régions les plus sensibles, ce qui peut aider à évaluer la robustesse du réseau de la chaîne d'approvisionnement dans différents scénarios, en tenant compte de diverses sources d'incertitude, telles que la demande et incertitudes sur les données documentées. D'autre part, le modèle caché basé sur Markov est utilisé pour prédire le comportement d'une catastrophe lors de la prochaine occurrence, sur la base de données et de tendances historiques. Ce modèle fournit des informations importantes sur le potentiel des HMM dans la gestion des catastrophes et la logistique humanitaire et souligne l'importance de ces modèles dans la protection des impacts des catastrophes, des populations vulnérables et dans l'atténuation des effets des catastrophes naturelles à l'avenir. La thèse vise également à identifier les emplacements optimaux des installations et à développer un plan de réponse efficace aux catastrophes pouvant atténuer l'impact des catastrophes. Ces stations auront pour fonction la réception, le contrôle, le soutien et la distribution de l'aide en cas de catastrophes naturelles (tremblements de terre, inondations, sécheresse, glissements de terrain...) ou catastrophes d'origine humaine (accidents technologiques, attentats terroristes, accidents de la route), à travers l'intégration des différents acteurs de la chaîne d'approvisionnement de l'aide marocaine (Ministère de l'Intérieur, Ministère de l'Aménagement des Territoires marocains, protection civile, militaire...) et en considérant diverses sources d'incertitude, telles que la demande, les délais de transport et les ruptures d'approvisionnement. Les emplacements optimaux des installations identifiés par les modèles offrent une meilleure couverture des zones touchées, améliorant ainsi la rapidité et l'efficacité du plan d'intervention en cas de catastrophe
Disaster modelling and facility location are critical aspects of disaster management that help to improve the effectiveness and efficiency of relief supply chains. However, the inherent uncertainties associated with disasters and relief supply chains can significantly impact the effectiveness of such models. To address this challenge, the thesis proposes the use of uncertainty quantification-based models and hidden Markov based models for disaster modelling in the context of the Moroccan relief supply chain. The thesis initiates by conceptualizing the Moroccan relief supply chain, comprehensively outlining its design, activities, and the various actors involved in the humanitarian process, then, a detailed analysis was conducted to highlight the strengths and weaknesses of the Moroccan relief supply chain. This involved a deep examination of uncertainty sources within the humanitarian process, to make a good understanding of challenges faced within the Moroccan relief supply chain and identify the specific requirements. Following this conceptual groundwork, the proposed models are then applied to a dedicated case study of the Moroccan relief supply chain. This practical application aims to validate the effectiveness of the uncertainty quantification-based models and hidden Markov-based models in a real-world scenario, providing valuable insights into their applicability, utility, and potential impact on the complex dynamics of the humanitarian field. The results demonstrate that the uncertainty quantification-based model and the hidden Markov based model can significantly improve the robustness and efficiency of the supply chain network in term of disaster prediction. The uncertainty quantification-based model enables to make prediction of the potential human impact of disasters and the most sensitive regions which can help in the evaluation of the robustness of the supply chain network under different scenarios, considering various sources of uncertainty, such as demand and uncertainties on documented data. On the other hand, the hidden Markov based model is used to predict the disaster behaviour in next occurrence, based on historical data and trends. This model provides important insights into the potential of HMMs in disaster management and humanitarian logistics and emphasize the importance of these models in protecting disasters impact, vulnerable populations and mitigating the effects of natural disasters in the future. The thesis aims also to identify the optimal facility locations and develop an efficient disaster response plan that can mitigate the impact of disasters, this stations will have for function the reception, control, support and the distribution of help in case of natural disasters (earthquakes, floods, torrential floods, locust invasions, drought, landslides ...) or man-made disasters (technological accidents, terrorist attacks, road accidents, pollution ...), through the integration of different actors in the Moroccan relief supply chain (Ministry of the Interior, Ministry of Planning of Moroccan Territories, the civil protection, military, NGOs ...) and by considering various sources of uncertainty, such as demand, transportation time, and supply disruptions. The optimal facility locations identified by the models provide a better coverage of the affected areas, thereby improving the speed and effectiveness of the disaster response plan. The thesis highlights the significance of incorporating uncertainty analysis in disaster modelling and provides insights into the relief supply chain management in Morocco. The findings of this thesis can be useful for policymakers and practitioners in disaster management to improve the effectiveness and efficiency of relief supply chains

Cette thèse est consacrée à l'optimisation de la logistique sanitaire pour soutenir les plans de gestion de crise afin de réduire les effets des catastrophes naturelles et/ou humaines en termes de santé publique. Les catastrophes naturelles peuvent être prédites, mais elles sont difficiles à éviter. Par conséquence, la prise en charge des victimes potentielles et le dimensionnement des moyens logistiques de secours y afférent sont d'une importance cruciale. Une approche analytique en trois étapes est proposée afin d'étudier le dimensionnement des ressources et l'organisation des plans de gestion de crise (plan blanc) d’origine naturelle. La première étape propose un modèle de référence pour concevoir un plan de gestion de crise. La deuxième étape considère un modèle d’optimisation linéaire pour prédire le nombre de ressources nécessaires. La dernière étape présente un modèle dynamique pour simuler l'ensemble de l’organisation de manière fine. L'évacuation d’un hôpital dans le cadre d’un plan blanc étendu à plusieurs hôpitaux pour faire face à un tsunami fluvial a été considérée comme scénario pour évaluer la justesse de notre approche. Les catastrophes d'origine humaine et la propagation des maladies peuvent donner lieu à des désastres de grande ampleur qui mobilisent de nombreuses ressources humaines et matérielles. Par exemple, un modèle de réponse logistique face à une attaque bioterroriste avec un agent non-contagieux ainsi qu’un modèle pour la réponse logistique aux épidémies ont été proposés. Elles traduisent un problème de gestion d’inventaire multi-échelons et multi-périodes. Ces deux modèles dynamiques de flux requièrent une optimisation linéaire et une optimisation non-linéaire respectivement. Ils intègrent les caractéristiques de la gestion de ces catastrophes: la propagation de l’infection ou de la maladie, les réponses médicales appropriées et le déploiement de la logistique associée. Avec ces deux modèles, le nombre de patients aux différents stades de la maladie et le nombre des ressources médicales nécessaires pour chaque période peuvent être calculés. Les facteurs qui influent sur le nombre de décès et l’efficience des différentes politiques d'intervention médicale, peuvent également être évalués. Les deux modèles peuvent ainsi aider les décideurs à prévoir les conséquences de la situation en cas de catastrophe ainsi que de connaître les informations d'une réponse pertinente, ceci à un niveau stratégique. Une réponse logistique à une attaque bioterroriste anonyme à l'anthrax dans un centre commercial et une réponse logistique à une pandémie H5N1 sont pris comme scénarios pour tester l'efficacité des modèles correspondants
This thesis is devoted to optimize the health care logistics which can support emergency management plans to reduce the impacts of natural and/or man-made disasters. After the review of relevant papers, two main gaps have been found in the current studies. One is that most of the researches are not based on real cases. The other is that some main characteristics of disasters are neglected when disasters are studied. Therefore, based on real case scenarios, the thesis studies different disasters (natural and/or man-made disasters) separately according to the characteristics of disasters. Natural disasters may be predicted but are difficult to avoid. Therefore, the evacuation of potential victims and the dimensioning of relief resources are crucially important. A three-step approach is proposed to study the resource dimensioning and the organization of emergency management plan (French White Plan) facing natural disasters. In our three-step approach, the first step builds a framework model to get the insights of emergency management plan clearly. The second step establishes a global model (a linear model) to predict the quantity of required resources for evacuation. The third step proposes a detailed simulation model to reflect the real world more precisely. The hospital evacuation under the guidance of a French Extended White Plan in case of a flood has been taken as a real case scenario to test the correctness of our approach. The man-made disasters and the outbreak of diseases can be large-scale disasters which require a high demand of resources. In this thesis, a model for logistics response to bioterrorist attack with a non-contagious agent and another model for the logistics response to epidemics have been proposed. Multi-period and multi-echelon inventory management problems have been studied. The two models (a linear model and a non linear model respectively) combine the main characteristics of disasters: the propagation of the disease, the relevant medical interventions and the logistics deployment together. The number of patients in different disease stages and the required medical resources for each period can be estimated. The factors affecting the number of deaths and the different medical intervention policies can also be evaluated with the two models. With the help of the models, the decision makers can get an idea of the disaster situation and the relevant medical responses from a strategy level. A logistics response to an anonymous bioterrorist attack with anthrax to a shopping center and the logistics response to the outbreak of H5N1 are taken as real case scenarios to test the effectiveness of the models respectively

The potential for a gastroenteritis outbreak in a post-earthquake environment may increase because of compromised infrastructure services, contaminated liquefaction (lateral spreading and surface ejecta), and the presence of gastroenteritis agents in the drinking water network. A population in a post-earthquake environment might be seriously affected by gastroenteritis because it has a short incubation period (about 10 hours). The potential for a gastroenteritis outbreak in a post-earthquake environment may increase because of compromised infrastructure services, contaminated liquefaction (lateral spreading and surface ejecta), and the presence of gastroenteritis agents in the drinking water network. A population in a post-earthquake environment might be seriously affected by gastroenteritis because it has a short incubation period (about 10 hours). The aim of this multidisciplinary research was to retrospectively analyse the gastroenteritis prevalence following the February 22, 2011 earthquake in Christchurch. The first focus was to assess whether earthquake-induced infrastructure damage, liquefaction, and gastroenteritis agents spatially explained the recorded gastroenteritis cases over the period of 35 days following the February 22, 2011 earthquake in Christchurch. The gastroenteritis agents considered in this study were Escherichia coli found in the drinking water supply (MPN/100mL) and Non-Compliant Free Associated Chlorine (FAC-NC) (less than <0.02mg/L). The second focus was the protocols that averted a gastroenteritis outbreak at three Emergency Centres (ECs): Burnside High School Emergency Centre (BEC); Cowles Stadium Emergency Centre (CEC); and Linwood High School Emergency Centre (LEC). Using a mixed-method approach, gastroenteritis point prevalence and the considered factors were quantitatively analysed. The qualitative analysis involved interviewing 30 EC staff members. The data was evaluated by adopting the Grounded Theory (GT) approach. Spatial analysis of considered factors showed that highly damaged CAUs were statistically clustered as demonstrated by Moran’s I statistic and hot spot analysis. Further modelling showed that gastroenteritis point prevalence clustering could not be fully explained by infrastructure damage alone, and other factors influenced the recorded gastroenteritis point prevalence. However, the results of this research suggest that there was a tenuous, indirect relationship between recorded gastroenteritis point prevalence and the considered factors: earthquake-induced infrastructure damage, liquefaction and FAC-NC. Two ECs were opened as part of the post-earthquake response in areas with severe infrastructure damage and liquefaction (BEC and CEC). The third EC (CEC) provided important lessons that were learnt from the previous September 4, 2010 earthquake, and implemented after the February 22, 2011 earthquake. Two types of interwoven themes identified: direct and indirect. The direct themes were preventive protocols and indirect themes included type of EC building (school or a sports stadium), and EC staff. The main limitations of the research were Modifiable Areal Units (MAUP), data detection, and memory loss. This research provides a practical method that can be adapted to assess gastroenteritis risk in a post-earthquake environment. Thus, this mixed method approach can be used in other disaster contexts to study gastroenteritis prevalence, and can serve as an appendage to the existing framework for assessing infectious diseases. Furthermore, the lessons learnt from qualitative analysis can inform the current infectious disease management plans, designed for a post-disaster response in New Zealand and internationally Using a mixed-method approach, gastroenteritis point prevalence and the considered factors were quantitatively analysed. A damage profile was created by amalgamating different types of damage for the considered factors for each Census Area Unit (CAU) in Christchurch. The damage profile enabled the application of a variety of statistical methods which included Moran’s I , Hot Spot (HS) analysis, Spearman’s Rho, and Besag–York–Mollié Model using a range of software. The qualitative analysis involved interviewing 30 EC staff members. The data was evaluated by adopting the Grounded Theory (GT) approach. Spatial analysis of considered factors showed that highly damaged CAUs were statistically clustered as demonstrated by Moran’s I statistic and hot spot analysis. Further modelling showed that gastroenteritis point prevalence clustering could not be fully explained by infrastructure damage alone, and other factors influenced the recorded gastroenteritis point prevalence. However, the results of this research suggest that there was a tenuous, indirect relationship between recorded gastroenteritis point prevalence and the considered factors: earthquake-induced infrastructure damage, liquefaction and FAC-NC. Two ECs were opened as part of the post-earthquake response in areas with severe infrastructure damage and liquefaction (BEC and CEC). The third EC (CEC) provided important lessons that were learnt from the previous September 4, 2010 earthquake, and implemented after the February 22, 2011 earthquake. The ECs were selected to represent the Christchurch area, and were situated where potential for gastroenteritis was high. BEC represented the western side of Christchurch; whilst, CEC and LEC represented the eastern side, where the potential for gastroenteritis was high according to the outputs of the quantitative spatial modelling. Qualitative analysis from the interviews at the ECs revealed that evacuees were arriving at the ECs with gastroenteritis-like symptoms. Participants believed that those symptoms did not originate at the ECs. Two types of interwoven themes identified: direct and indirect. The direct themes were preventive protocols that included prolific use of hand sanitisers; surveillance; and the services offered. Indirect themes included the EC layout, type of EC building (school or a sports stadium), and EC staff. Indirect themes governed the quality and sustainability of the direct themes implemented, which in turn averted gastroenteritis outbreaks at the ECs. The main limitations of the research were Modifiable Areal Units (MAUP), data detection, and memory loss. It was concluded that gastroenteritis point prevalence following the February 22, 2011 earthquake could not be solely explained by earthquake-induced infrastructure damage, liquefaction, and gastroenteritis causative agents alone. However, this research provides a practical method that can be adapted to assess gastroenteritis risk in a post-earthquake environment. Creating a damage profile for each CAU and using spatial data analysis can isolate vulnerable areas, and qualitative data analysis provides localised information. Thus, this mixed method approach can be used in other disaster contexts to study gastroenteritis prevalence, and can serve as an appendage to the existing framework for assessing infectious diseases. Furthermore, the lessons learnt from qualitative analysis can inform the current infectious disease management plans, designed for a post-disaster response in New Zealand and internationally.

Rock and debris avalanches result from sudden rock slope failure; they occur in a variety of materials and landscapes, and often have a catastrophic and lasting impact on the society, infrastructure, and landscape of the area. In order to fully understand these events, the factors leading to failure and those influencing the course of the event must be investigated. In recent years, increased attention has been given to numerous aspects of rock/debris avalanche emplacement: among these is the influence of runout path material on the behaviour of snow and ice avalanches, pyroclastic currents, debris flows, volcanic debris avalanches and non-volcanic rock avalanches. The fact that substrates are involved in rock avalanche emplacement has been known since Buss and Heim remarked on it in 1881, but few detailed studies on the effects of this involvement on avalanche emplacement exist. One popular hypothesis which has emerged is that the long runout of large rock avalanches can be explained by the basal friction reduction due to overrunning or failure of saturated substrate material. However, the present study shows that this is not the case. From analysis of nearly 400 rock and debris avalanche deposit descriptions it is evident that: (1) avalanches inevitably interact with their runout path material; (2) all large (> 10⁶ m³) rock and debris avalanche events have runout distances that exceed simple frictional model predictions regardless of type or degree of substrate interaction; (3) substrates only add complexities to the ‘long-runout’ avalanche events similar to topographic interference. The complexities resulting from substrate interaction include, for example, characteristic deposit surface features such as longitudinal ridges and flowbands, compressional faults and raised margins from rapid deceleration behind e.g. bulldozed substrates; shearing in a basal mixed zone and consequent changes in basal avalanche mechanical properties; volcanic edifice failure on weak underlying sediments with a change in volcano shape; transformation into more mobile debris flows through the entrainment of large quantities of water or water-bearing materials; and many others.

In the United Kingdom, there is a system for the co-ordination of the emergency services in response to disasters - The Emergency Management Combined Response System (EMCRS). It is a complex three tier command and control system. It was set up in response to a need for better co­ordination between agencies, when they respond to disasters. This research has developed models of the EMCRS that support diagnosis of co-ordination problems between agencies. Data for the modelling was acquired by means of training exercises. The co-ordination pro blems were identified through behaviour conflicts between the agencies. For example, the Fire Service behaviours of setting up a cordon around the disaster site conflict with the Ambulance Service behaviours of accessing the site for treatment of casualties. In the course of EMCRS model development, the scope of an existing framework was extended to accommodate EMCRS characteristics, which are general to: (i) systems with more than one level of operation and interactions between the levels; (ii) systems that do not have stable membership; and (iii) systems where there are trade-offs between different parts of the system that affect performance. For example, the framework extension for (ii) is to include time lines and a symbol that denotes additional structures. The EMCRS models constitute substantive Human Computer Interaction design knowledge, that is, knowledge that is both explicit and supports design. Such knowledge supports design practice directly, as the diagnosis of design problems, and indirectly, as the prescription of design solutions. An initial method for coordination design problem diagnosis by means of EMCRS models is also proposed. The strengths and weaknesses of the research are identified and discussed. Future work would be to apply the extended framework to data from an actual disaster to valid ate the EMCRS models.

A discapito della propria rilevanza economica, culturale e sociale, il turismo rappresenta un fenomeno particolarmente esposto e fragile rispetto alle questioni della sicurezza: gli shock esterni costituiscono un deterrente all'attrazione dei turisti e un ostacolo per lo sviluppo sostenibile della destinazione. In particolare, di fronte alla crescente intensità e frequenza con cui i disastri naturali colpiscono le destinazioni in tutto il mondo, esacerbati da processi quali l'urbanizzazione, i cambiamenti climatici e lo sviluppo economico, il crisis e disaster management emerge come un'assoluta necessità nel contesto della gestione turistica. La presente trattazione si propone quindi di indagare la natura complessa di tali fenomeni e di fornire un quadro dei modelli e delle strategie di prevenzione e mitigazione emersi in letteratura. Con l'obiettivo di verificare l'applicabilità dei frameworks presentati, si adotterà una prospettiva empirica rispetto al caso-studio dei terremoti avvenuti nel Centro Italia nel 2016-2017. Focalizzandosi sulla regione Umbria, si studieranno gli impatti del sisma e le misure adottate in tutte le fasi del disastro, osservando se, e in quale misura, queste abbiano interessato il settore turistico. Tramite un approccio comparativo sarà quindi possibile implementare i modelli teorici, da un lato, e testare la validità delle strategie effettivamente applicate, dall'altro, proponendo eventuali modifiche o misure di innovazione.

In the last 50 years, flooding has figured as the most frequent and widespread natural disaster globally. Extreme precipitation events stemming from climate change could alter the hydro-geological regime resulting in increased flood risk. Near real-time precipitation monitoring at local scale is essential for flood risk mitigation in urban and suburban areas, due to their high vulnerability. Presently, most of the rainfall data is obtained from ground‐based measurements or remote sensing that provide limited information in terms of temporal or spatial resolution. Other problems may be due to the high costs. Furthermore, rain gauges are unevenly spread and usually placed away from urban centers. In this context, a big potential is represented by the use of innovative techniques to develop low-cost monitoring systems. Despite the diversity of purposes, methods and epistemological fields, the literature on the visual effects of the rain supports the idea of camera-based rain sensors but tends to be device-specific. The present thesis aims to investigate the use of easily available photographing devices as rain detectors-gauges to develop a dense network of low-cost rainfall sensors to support the traditional methods with an expeditious solution embeddable into smart devices. As opposed to existing works, the study focuses on maximizing the number of image sources (like smartphones, general-purpose surveillance cameras, dashboard cameras, webcams, digital cameras, etc.). This encompasses cases where it is not possible to adjust the camera parameters or obtain shots in timelines or videos. Using a Deep Learning approach, the rainfall characterization can be achieved through the analysis of the perceptual aspects that determine whether and how a photograph represents a rainy condition. The first scenario of interest for the supervised learning was a binary classification; the binary output (presence or absence of rain) allows the detection of the presence of precipitation: the cameras act as rain detectors. Similarly, the second scenario of interest was a multi-class classification; the multi-class output described a range of quasi-instantaneous rainfall intensity: the cameras act as rain estimators. Using Transfer Learning with Convolutional Neural Networks, the developed models were compiled, trained, validated, and tested. The preparation of the classifiers included the preparation of a suitable dataset encompassing unconstrained verisimilar settings: open data, several data owned by National Research Institute for Earth Science and Disaster Prevention - NIED (dashboard cameras in Japan coupled with high precision multi-parameter radar data), and experimental activities conducted in the NIED Large Scale Rainfall Simulator. The outcomes were applied to a real-world scenario, with the experimentation through a pre-existent surveillance camera using 5G connectivity provided by Telecom Italia S.p.A. in the city of Matera (Italy). Analysis unfolded on several levels providing an overview of generic issues relating to the urban flood risk paradigm and specific territorial questions inherent with the case study. These include the context aspects, the important role of rainfall from driving the millennial urban evolution to determining present criticality, and components of a Web prototype for flood risk communication at local scale. The results and the model deployment raise the possibility that low‐cost technologies and local capacities can help to retrieve rainfall information for flood early warning systems based on the identification of a significant meteorological state. The binary model reached accuracy and F1 score values of 85.28% and 0.86 for the test, and 83.35% and 0.82 for the deployment. The multi-class model reached test average accuracy and macro-averaged F1 score values of 77.71% and 0.73 for the 6-way classifier, and 78.05% and 0.81 for the 5-class. The best performances were obtained in heavy rainfall and no-rain conditions, whereas the mispredictions are related to less severe precipitation. The proposed method has limited operational requirements, can be easily and quickly implemented in real use cases, exploiting pre-existent devices with a parsimonious use of economic and computational resources. The classification can be performed on single photographs taken in disparate conditions by commonly used acquisition devices, i.e. by static or moving cameras without adjusted parameters. This approach is especially useful in urban areas where measurement methods such as rain gauges encounter installation difficulties or operational limitations or in contexts where there is no availability of remote sensing data. The system does not suit scenes that are also misleading for human visual perception. The approximations inherent in the output are acknowledged. Additional data may be gathered to address gaps that are apparent and improve the accuracy of the precipitation intensity prediction. Future research might explore the integration with further experiments and crowdsourced data, to promote communication, participation, and dialogue among stakeholders and to increase public awareness, emergency response, and civic engagement through the smart community idea.
Negli ultimi 50 anni, le alluvioni si sono confermate come il disastro naturale più frequente e diffuso a livello globale. Tra gli impatti degli eventi meteorologici estremi, conseguenti ai cambiamenti climatici, rientrano le alterazioni del regime idrogeologico con conseguente incremento del rischio alluvionale. Il monitoraggio delle precipitazioni in tempo quasi reale su scala locale è essenziale per la mitigazione del rischio di alluvione in ambito urbano e periurbano, aree connotate da un'elevata vulnerabilità. Attualmente, la maggior parte dei dati sulle precipitazioni è ottenuta da misurazioni a terra o telerilevamento che forniscono informazioni limitate in termini di risoluzione temporale o spaziale. Ulteriori problemi possono derivare dagli elevati costi. Inoltre i pluviometri sono distribuiti in modo non uniforme e spesso posizionati piuttosto lontano dai centri urbani, comportando criticità e discontinuità nel monitoraggio. In questo contesto, un grande potenziale è rappresentato dall'utilizzo di tecniche innovative per sviluppare sistemi inediti di monitoraggio a basso costo. Nonostante la diversità di scopi, metodi e campi epistemologici, la letteratura sugli effetti visivi della pioggia supporta l'idea di sensori di pioggia basati su telecamera, ma tende ad essere specifica per dispositivo scelto. La presente tesi punta a indagare l'uso di dispositivi fotografici facilmente reperibili come rilevatori-misuratori di pioggia, per sviluppare una fitta rete di sensori a basso costo a supporto dei metodi tradizionali con una soluzione rapida incorporabile in dispositivi intelligenti. A differenza dei lavori esistenti, lo studio si concentra sulla massimizzazione del numero di fonti di immagini (smartphone, telecamere di sorveglianza generiche, telecamere da cruscotto, webcam, telecamere digitali, ecc.). Ciò comprende casi in cui non sia possibile regolare i parametri fotografici o ottenere scatti in timeline o video. Utilizzando un approccio di Deep Learning, la caratterizzazione delle precipitazioni può essere ottenuta attraverso l'analisi degli aspetti percettivi che determinano se e come una fotografia rappresenti una condizione di pioggia. Il primo scenario di interesse per l'apprendimento supervisionato è una classificazione binaria; l'output binario (presenza o assenza di pioggia) consente la rilevazione della presenza di precipitazione: gli apparecchi fotografici fungono da rivelatori di pioggia. Analogamente, il secondo scenario di interesse è una classificazione multi-classe; l'output multi-classe descrive un intervallo di intensità delle precipitazioni quasi istantanee: le fotocamere fungono da misuratori di pioggia. Utilizzando tecniche di Transfer Learning con reti neurali convoluzionali, i modelli sviluppati sono stati compilati, addestrati, convalidati e testati. La preparazione dei classificatori ha incluso la preparazione di un set di dati adeguato con impostazioni verosimili e non vincolate: dati aperti, diversi dati di proprietà del National Research Institute for Earth Science and Disaster Prevention - NIED (telecamere dashboard in Giappone accoppiate con dati radar multiparametrici ad alta precisione) e attività sperimentali condotte nel simulatore di pioggia su larga scala del NIED. I risultati sono stati applicati a uno scenario reale, con la sperimentazione attraverso una telecamera di sorveglianza preesistente che utilizza la connettività 5G fornita da Telecom Italia S.p.A. nella città di Matera (Italia). L'analisi si è svolta su più livelli, fornendo una panoramica sulle questioni relative al paradigma del rischio di alluvione in ambito urbano e questioni territoriali specifiche inerenti al caso di studio. Queste ultime includono diversi aspetti del contesto, l'importante ruolo delle piogge dal guidare l'evoluzione millenaria della morfologia urbana alla determinazione delle criticità attuali, oltre ad alcune componenti di un prototipo Web per la comunicazione del rischio alluvionale su scala locale. I risultati ottenuti e l'implementazione del modello corroborano la possibilità che le tecnologie a basso costo e le capacità locali possano aiutare a caratterizzare la forzante pluviometrica a supporto dei sistemi di allerta precoce basati sull'identificazione di uno stato meteorologico significativo. Il modello binario ha raggiunto un'accuratezza e un F1-score di 85,28% e 0,86 per il set di test e di 83,35% e 0,82 per l'implementazione nel caso di studio. Il modello multi-classe ha raggiunto un'accuratezza media e F1-score medio (macro-average) di 77,71% e 0,73 per il classificatore a 6 vie e 78,05% e 0,81 per quello a 5 classi. Le prestazioni migliori sono state ottenute nelle classi relative a forti precipitazioni e assenza di pioggia, mentre le previsioni errate sono legate a precipitazioni meno estreme. Il metodo proposto richiede requisiti operativi limitati, può essere implementato facilmente e rapidamente in casi d'uso reali, sfruttando dispositivi preesistenti con un uso parsimonioso di risorse economiche e computazionali. La classificazione può essere eseguita su singole fotografie scattate in condizioni disparate da dispositivi di acquisizione di uso comune, ovvero da telecamere statiche o in movimento senza regolazione dei parametri. Questo approccio potrebbe essere particolarmente utile nelle aree urbane in cui i metodi di misurazione come i pluviometri incontrano difficoltà di installazione o limitazioni operative o in contesti in cui non sono disponibili dati di telerilevamento o radar. Il sistema non si adatta a scene che sono fuorvianti anche per la percezione visiva umana. I limiti attuali risiedono nelle approssimazioni intrinseche negli output. Per colmare le lacune evidenti e migliorare l'accuratezza della previsione dell'intensità di precipitazione, sarebbe possibile un'ulteriore raccolta di dati. Sviluppi futuri potrebbero riguardare l'integrazione con ulteriori esperimenti in campo e dati da crowdsourcing, per promuovere comunicazione, partecipazione e dialogo aumentando la resilienza attraverso consapevolezza pubblica e impegno civico in una concezione di comunità smart.

Disasters especially from natural phenomena are inevitable. The affected areas recover from the aftermath of a natural disaster with the support from various agents participating in humanitarian operations. There are several domains of the operation, and distributing relief aids is one. For distribution, satisfying the demand for relief aid is important since the condition of the environment is unfavorable to affected people and resources needed for the victim’s life are scarce. However, it becomes problematic when the logistic agents believed to be work properly fail to deliver the emergency goods because of the capacity loss induced from the environment after disasters. This study was proposed to address the problem of logistic agents’ unexpected incapacity which hinders scheduled distribution. The decrease in a logistic agent’s supply capability delays
achieving the goal of supplying required relief goods to the affected people which further endangers them. Regarding the stated problem, this study explored the importance of
setting the profile of logistic agents that can survive for certain duration of times. Therefore, this research defines the “survivability” and the profile of logistic agents for surviving the last mile distribution through agent based modeling and simulation. Through simulations, this study uncovered that the logistic exercise could gain survivability with the certain number and organization of logistic agents. Proper formation of organization establish the logistics’ survivability, but excessive size can threaten the survivability.

Disaster impacts around the world are increasing with 2011 and 2017 the largest on record in terms of total losses from disasters in recorded history (USD 444billion and USD 341billion, respectively). The reasons for the increase in losses are multiple. Climate change is increasing the likelihood and intensity of several natural hazard types, and as the world’s population and economy grow, and humans increasingly develop in areas exposed to natural hazard (e.g. along rivers, and coastal areas), the values exposed are also rapidly increasing. These multiple factors contribute to the complex nature of disaster risk, which is considered to be the combination of natural hazard intensity and extent, exposure (assets, people, other values), and vulnerabilities of the exposed values to the characteristics of the hazards. This can be considered the risk triangle – hazard, exposure and vulnerability – and each of these factors change into the future impacted by a range of drivers; population and economic change, technology, urbanisation rates, political actions etc. To reduce the impacts of disasters, risk management and reduction activities are designed and implemented, and are typically underpinned by risk assessments. Risk assessments use qualitative and/or quantitative approaches to attempt to characterise the likelihood and impact of disaster types for a region or organisation. Currently, risk assessments do not capture future changes across all dimensions of risk in a manner that provides insight into the strategic threats and opportunities of emergent disaster risks. Therefore, there is a need for approaches to consider realistic degrees of complexity within the disaster risk system and account for the uncertainty in emergent risk. By capturing this within disaster risk assessments, treatment options can be developed and tested that strategically manage these risks over time. This research has developed these approaches and provides three key contributions through the use of foresight, primarily scenarios within disaster risk assessment processes, to support effective policy and investment decision making to reduce future impacts. The thesis is organised around three publications, all contributing to the development of a generic framework which integrates foresight into disaster risk management and specific approaches to develop and use scenarios for strategic risk assessment and management of emergent disaster risk. The first paper (Chapter 2) proposes and demonstrates this generic framework for the incorporation of the principles of foresight into risk assessment and management processes. The second paper (Chapter 3) focuses on the design of scenarios to support policy making for disaster risk reduction through several improvements to the methodological approach for constructing relevant and challenging scenarios using an “outcomes of interest” framing. The third paper (Chapter 4) outlines and applies an approach for the use of exploratory scenarios within quantitative disaster risk assessment through the development of alternative pathways of disaster risk using scenarios and integrated risk models.
Thesis (Ph.D.) -- University of Adelaide, School of Civil, Environmental and Mining Engineering, 2019

Yes
Social media usage in evacuations and emergency management represents a rapidly expanding field of study. Our paper thus provides quantitative insight into a serious practical problem. Within this context a behavioural approach is key. We discuss when facilitators should consider model-based interventions amid further implications for disaster communication and emergency management. We model the behaviour of individual people by deriving optimal contrarian strategies. We formulate a Bayesian algorithm which enables the optimal evacuation to be conducted sequentially under worsening conditions.
Supported by EPSRC (IDEAS Factory - Game theory and adaptive networks for smart evacuations, EP/I005765/1)

Submitted in fulfillment of the requirements of the degree of Master of Technology: Industrial Engineering, Durban University of Technology, Durban, South Africa, 2014.
The SADC region has seen both man-made and natural disasters killing over 90 thousand people and affecting millions in the past 33 years. Most of these deaths were as a result of lack of infrastructure and preparedness. Looking at the challenges for providing relief to victims/evacuees throughout the entire disaster and post-disaster periods in the region, the emphasis of this thesis is on last mile transportation of resources, victims, emergency supplies, aiming to optimize the effectiveness (quick­I response) and efficiency (low-cost) of logistics activities including humanitarian supply chain. A survey was used for data collection. Statistical analysis helped determine the impact of disaster relief chains and lead to the development of a mathematical model that shall equip the region with mechanisms for response and recovery operations. An EXCEL optimization tool was used to find the optimal way of transporting relief in the region in case of a disaster.
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Understanding human mobility patterns is important for severe weather warning since these patterns can help identify where people are in time and in space when flash floods, tornados, high winds and hurricanes are occurring or are predicted to occur. A GIS (Geographic Information Science) data model was proposed to describe the spatial-temporal human activity. Based on this model, a metric was designed to represent the spatial-temporal activity intensity of human mobility, and an index was generated to quantitatively describe the change in human activities. By analyzing high-resolution human mobility data, the paper verified that human daily mobility patterns could be clearly described with the proposed methods. This research was part of a National Science Foundation grant on next generation severe weather warning systems. Data was collected from a specialized mobile app for severe weather warning, called CASA Alerts, which is being used to analyze different aspects of human behavior in response to severe weather warnings. The data set for this research uses GPS location data from more than 300 APP users during a 14 month period (location was reported at 2 minutes interval, or at based on a 100m change in location). A targeted weather warning strategy was proposed as a result of this research, and future research questions were discussed.