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- Creators: Atkinson, Robert
Description
Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning of the relevant patterns This dissertation proposes TS representations and methods for supervised TS analysis. The approaches combine new representations that handle translations and dilations of patterns with bag-of-features strategies and tree-based ensemble learning. This provides flexibility in handling time-warped patterns in a computationally efficient way. The ensemble learners provide a classification framework that can handle high-dimensional feature spaces, multiple classes and interaction between features. The proposed representations are useful for classification and interpretation of the TS data of varying complexity. The first contribution handles the problem of time warping with a feature-based approach. An interval selection and local feature extraction strategy is proposed to learn a bag-of-features representation. This is distinctly different from common similarity-based time warping. This allows for additional features (such as pattern location) to be easily integrated into the models. The learners have the capability to account for the temporal information through the recursive partitioning method. The second contribution focuses on the comprehensibility of the models. A new representation is integrated with local feature importance measures from tree-based ensembles, to diagnose and interpret time intervals that are important to the model. Multivariate time series (MTS) are especially challenging because the input consists of a collection of TS and both features within TS and interactions between TS can be important to models. Another contribution uses a different representation to produce computationally efficient strategies that learn a symbolic representation for MTS. Relationships between the multiple TS, nominal and missing values are handled with tree-based learners. Applications such as speech recognition, medical diagnosis and gesture recognition are used to illustrate the methods. Experimental results show that the TS representations and methods provide better results than competitive methods on a comprehensive collection of benchmark datasets. Moreover, the proposed approaches naturally provide solutions to similarity analysis, predictive pattern discovery and feature selection.
ContributorsBaydogan, Mustafa Gokce (Author) / Runger, George C. (Thesis advisor) / Atkinson, Robert (Committee member) / Gel, Esma (Committee member) / Pan, Rong (Committee member) / Arizona State University (Publisher)
Created2012
Description
Aquifers host the largest accessible freshwater resource in the world. However, groundwater reserves are declining in many places. Often coincident with drought, high extraction rates and inadequate replenishment result in groundwater overdraft and permanent land subsidence. Land subsidence is the cause of aquifer storage capacity reduction, altered topographic gradients which can exacerbate floods, and differential displacement that can lead to earth fissures and infrastructure damage. Improving understanding of the sources and mechanisms driving aquifer deformation is important for resource management planning and hazard mitigation.
Poroelastic theory describes the coupling of differential stress, strain, and pore pressure, which are modulated by material properties. To model these relationships, displacement time series are estimated via satellite interferometry and hydraulic head levels from observation wells provide an in-situ dataset. In combination, the deconstruction and isolation of selected time-frequency components allow for estimating aquifer parameters, including the elastic and inelastic storage coefficients, compaction time constants, and vertical hydraulic conductivity. Together these parameters describe the storage response of an aquifer system to changes in hydraulic head and surface elevation. Understanding aquifer parameters is useful for the ongoing management of groundwater resources.
Case studies in Phoenix and Tucson, Arizona, focus on land subsidence from groundwater withdrawal as well as distinct responses to artificial recharge efforts. In Christchurch, New Zealand, possible changes to aquifer properties due to earthquakes are investigated. In Houston, Texas, flood severity during Hurricane Harvey is linked to subsidence, which modifies base flood elevations and topographic gradients.
Poroelastic theory describes the coupling of differential stress, strain, and pore pressure, which are modulated by material properties. To model these relationships, displacement time series are estimated via satellite interferometry and hydraulic head levels from observation wells provide an in-situ dataset. In combination, the deconstruction and isolation of selected time-frequency components allow for estimating aquifer parameters, including the elastic and inelastic storage coefficients, compaction time constants, and vertical hydraulic conductivity. Together these parameters describe the storage response of an aquifer system to changes in hydraulic head and surface elevation. Understanding aquifer parameters is useful for the ongoing management of groundwater resources.
Case studies in Phoenix and Tucson, Arizona, focus on land subsidence from groundwater withdrawal as well as distinct responses to artificial recharge efforts. In Christchurch, New Zealand, possible changes to aquifer properties due to earthquakes are investigated. In Houston, Texas, flood severity during Hurricane Harvey is linked to subsidence, which modifies base flood elevations and topographic gradients.
ContributorsMiller, Megan Marie (Author) / Shirzaei, Manoochehr (Thesis advisor) / Reynolds, Stephen (Committee member) / Tyburczy, James (Committee member) / Semken, Steven (Committee member) / Werth, Susanna (Committee member) / Arizona State University (Publisher)
Created2018