DATA-DRIVEN ANALYTICAL MODELS FOR IDENTIFICATION AND PREDICTION OF OPPORTUNITIES AND THREATS

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dc.contributor.advisorAyyub, Bilalen_US
dc.contributor.authorMishra, Saurabhen_US
dc.contributor.departmentReliability Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2018-09-19T05:32:18Z
dc.date.available2018-09-19T05:32:18Z
dc.date.issued2018en_US
dc.description.abstractDuring the lifecycle of mega engineering projects such as: energy facilities, infrastructure projects, or data centers, executives in charge should take into account the potential opportunities and threats that could affect the execution of such projects. These opportunities and threats can arise from different domains; including for example: geopolitical, economic or financial, and can have an impact on different entities, such as, countries, cities or companies. The goal of this research is to provide a new approach to identify and predict opportunities and threats using large and diverse data sets, and ensemble Long-Short Term Memory (LSTM) neural network models to inform domain specific foresights. In addition to predicting the opportunities and threats, this research proposes new techniques to help decision-makers for deduction and reasoning purposes. The proposed models and results provide structured output to inform the executive decision-making process concerning large engineering projects (LEPs). This research proposes new techniques that not only provide reliable timeseries predictions but uncertainty quantification to help make more informed decisions. The proposed ensemble framework consists of the following components: first, processed domain knowledge is used to extract a set of entity-domain features; second, structured learning based on Dynamic Time Warping (DTW), to learn similarity between sequences and Hierarchical Clustering Analysis (HCA), is used to determine which features are relevant for a given prediction problem; and finally, an automated decision based on the input and structured learning from the DTW-HCA is used to build a training data-set which is fed into a deep LSTM neural network for time-series predictions. A set of deeper ensemble programs are proposed such as Monte Carlo Simulations and Time Label Assignment to offer a controlled setting for assessing the impact of external shocks and a temporal alert system, respectively. The developed model can be used to inform decision makers about the set of opportunities and threats that their entities and assets face as a result of being engaged in an LEP accounting for epistemic uncertainty.en_US
dc.identifierhttps://doi.org/10.13016/M24T6F67W
dc.identifier.urihttp://hdl.handle.net/1903/21416
dc.language.isoenen_US
dc.subject.pqcontrolledArtificial intelligenceen_US
dc.subject.pqcontrolledEconomicsen_US
dc.subject.pqcontrolledEngineeringen_US
dc.subject.pquncontrolleddeep neural networksen_US
dc.subject.pquncontrolledeconomic forecastingen_US
dc.subject.pquncontrolledepistemic uncertaintyen_US
dc.subject.pquncontrolledexecutive decision makingen_US
dc.subject.pquncontrolledgeopolitical forecastingen_US
dc.subject.pquncontrolledmegaproject managementen_US
dc.titleDATA-DRIVEN ANALYTICAL MODELS FOR IDENTIFICATION AND PREDICTION OF OPPORTUNITIES AND THREATSen_US
dc.typeDissertationen_US

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