Computational Techniques to Identify Rare Events in Spatio-temporal Data

University of Minnesota Ph.D. dissertation.May 2018. Major: Computer Science. Advisor: Vipin Kumar. 1 computer file (PDF); xi, 96 pages.Recent attention on the potential impacts of land cover changes to the environment as well as long-term climate change has increased the focus on automated tools for global-scale land surface monitoring. Advancements in remote sensing and data collection technologies have produced large earth science data sets that can now be used to build such tools. However, new data mining methods are needed to address the unique characteristics of earth science data and problems. In this dissertation, we explore two of these interesting problems, which are (1) build predictive models to identify rare classes when high quality annotated training samples are not available, and (2) classification enhancement of existing imperfect classification maps using physics-guided constraints. We study the problem of identifying land cover changes such as forest fires as a supervised binary classification task with the following characteristics: (i) instead of true labels only imperfect labels are available for training samples. These imperfect labels can be quite poor approximation of the true labels and thus may have little utility in practice. (ii) the imperfect labels are available for all instances (not just the training samples). (iii) the target class is a very small fraction of the total number of samples (traditionally referred to as the rare class problem). In our approach, we focus on leveraging imperfect labels and show how they, in conjunction with attributes associated with instances, open up exciting opportunities for performing rare class prediction. We applied this approach to identify burned areas using data from earth observing satellites, and have produced a database, which is more reliable and comprehensive (three times more burned area in tropical forests) compared to the state-of-art NASA product. We explore approaches to reduce errors in remote sensing based classification products, which are common due to poor data quality (eg., instrument failure, atmospheric interference) as well as limitations of the classification models. We present classification enhancement approaches, which aim to improve the input (imperfect) classification by using some implicit physics-based constraints related to the phenomena under consideration. Specifically, our approach can be applied in domains where (i) physical properties can be used to correct the imperfections in the initial classification products, and (ii) if clean labels are available, they can be used to construct the physical properties

Logística humanitária: modelagem de processos para a fase de aquisição na resposta a desastres naturais

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Produção, Florianópolis, 2014As organizações humanitárias trabalham em ambientes voláteis, envolvendo uma variedade de atores com diferentes habilidades e conhecimentos. O serviço de emergência às vítimas de desastres naturais exige uma rápida tomada de decisão. É difícil distribuir tarefas para quem é menos experiente e há poucas ferramentas para auxiliar os tomadores de decisão nestes ambientes de alta pressão. O objetivo desta tese é apresentar um modelo matemático e computacional para apoiar as operações humanitárias em eventos de desastres naturais, com destaque para a função de aquisição de suprimentos durante a fase de resposta em caso de desastres naturais. Esta abordagem evidência a integração de ferramentas para os fluxos de trabalhos e processos utilizando um modelo de referência de tarefas existente para a Logística Humanitária (LH) na notação BPMN. Essa ferramenta incorpora a clusterização de tipos de desastres de acordo com as regiões geográficas com base em um algoritmo K-means, a fim de predeterminar artigos de socorro. Com esta estrutura de demanda, determinados fornecedores são selecionados com a possibilidade de entregar os itens em curto prazo, utilizando um modelo de fluxo em redes adaptado e considerando-se, ainda, o menor custo. A fim de facilitar a tomada de decisão em situações de emergência, utilizou-se técnicas de simulação e otimização, de modo a estudar o impacto da aplicação das atividades sugeridas, na gestão dos processos em uma cadeia de suprimentos humanitária. Com o intuito de exemplificar uma aplicação do modelo, estudou-se o cenário de um desastre ocorrido no estado de Santa Catarina. Na aplicação verificou-se que decisões podem ser tomadas para gerir de forma eficiente o fluxo de materiais ao longo da cadeia de suprimentos em operações humanitárias, minimizando o tempo de resposta para a fase emergencial em eventos de desastres naturais.Abstract: Humanitarian organizations working in volatile environments, involve a variety of actors with different abilities and knowledge. The emergency services to victims of natural disasters requires a quick decision. It's difficult to allocate tasks to those who are less experienced and there are few tools to support decision makers in these high-pressure environments.The aim of this thesis is to present a mathematical and computational model to support humanitarian operations in natural disaster events, with emphasis on the procurement function of supplies during the phase of response in case of disasters. This approach highlights the integration of tools for workflows and processes using an existing reference tasks model for Humanitarian Logistics (HL) in the BPMN notation.This tool incorporates clustering of types of disasters according to the geographic regions based on a K-means algorithm with goal to predetermine relief items. With this demand structure, certain suppliers are selected with the possibility to deliver the items in a short time, using a networks flow model adapted and considering, also, the lowest cost.To facilitate decision making in emergency situations, we used techniques of simulation and optimization with purpose to study the impact of the application of the suggested tasks, in the business processes management, in a humanitarian supply chain. With the intention to exemplify an application of the proposed model, we study the scene of a disaster in the state of Santa Catarina. In the application example shows that decisions can be taken to manage efficiently the flow of materials throughout the supply chain in humanitarian operations, minimizing the response time for the emergency phase in natural disaster events

Monitoring Global Forest Cover Using Data Mining

Forests are a critical component of the planet's ecosystem. Unfortunately, there has been significant degradation in forest cover over recent decades as a result of logging, conversion to crop,plantation, and pasture land, or disasters (natural or man made) such as forest fires, floods, and hurricanes. As a result, significant attention is being given to the sustainable use of forests. A key to effective forest management is quantifiable knowledge about changes in forest cover. This requires identification and characterization of changes and the discovery of the relationship between these changes and natural and anthropogenic variables. In this paper, we present our preliminary efforts and achievements in addressing some of these tasks along with the challenges and opportunities that need to be addressed in the future. At a higher level, our goal is to provide an overview of the exciting opportunities and challenges in developing and applying data mining approaches to provide critical information for forest and land use management