1,721,052 research outputs found
Use of Machine Learning Methods to Reduce Predictive Error of Groundwater Models
No full textQuantitative analyses of groundwater flow and transport typically rely on a physically-based model, which is inherently subject to error. Errors in model structure, parameter and data lead to both random and systematic error even in the output of a calibrated model. We develop complementary data-driven models (DDMs) to reduce the predictive error of physically-based groundwater models. Two machine learning techniques, the instance-based weighting and support vector regression, are used to build the DDMs. This approach is illustrated using two real-world case studies of the Republican River Compact Administration model and the Spokane Valley-Rathdrum Prairie model. The two groundwater models have different hydrogeologic settings, parameterization, and calibration methods. In the first case study, cluster analysis is introduced for data preprocessing to make the DDMs more robust and computationally efficient. The DDMs reduce the root-mean-square error (RMSE) of the temporal, spatial, and spatiotemporal prediction of piezometric head of the groundwater model by 82%, 60%, and 48%, respectively. In the second case study, the DDMs reduce the RMSE of the temporal prediction of piezometric head of the groundwater model by 77%. It is further demonstrated that the effectiveness of the DDMs depends on the existence and extent of the structure in the error of the physically-based model.close0
Caprock characteristics and uncertainties in geological carbon sequestration in the Illinois basin
Get PDFGeological carbon sequestration in deep saline aquifers has emerged as a promising mitigation strategy for reducing greenhouse gas emissions to the atmosphere. Success of commercial-scale GCS requires containment of injected CO2 and the sealing ability of the overlying caprock has been identified as an important factor related to the long-term storage of CO2. Caprock research is recent and the behavior of caprocks as seals in GCS is not well understood. Geological uncertainty assessment in GCS research is often limited to reservoir properties. This research presents a current review of the dominant physical characteristics of caprocks and their relation to CO2 containment. This work is limited to argillaceous sediments, such as shales and mudrocks, as caprocks in GCS. The ability to retard vertical fluid flow is a complex issue as the CO2 plume will be in contact with the caprock due to buoyancy and involve hydrodynamic, geomechanical, and geochemical processes.
Physical processes which govern leakage through a caprock are often coupled, yet the effective geologic parameters are uncertain. To address caprock geologic parameter uncertainty in GCS modeling, a simplified caprock-reservoir simulation model based on the Eau Claire Formation and the Mt. Simon Formation at the Illinois Basin – Decatur Project (IBDP), a large-scale carbon capture and storage project, is developed. The extended Morris OAT method (Morris, 1991; Campolongo et al., 2007) is used to assess the sensitivity of the pressure response due to brine injection at locations in the caprock and reservoir with respect to four caprock parameters: horizontal permeability, anisotropic permeability ratio, porosity, and rock compressibility. All caprock parameters exhibited nonlinear and non-negligible effects. Horizontal permeability caprock is the dominating factor which aids in the dissipation of pressure in the caprock and reservoir at all times after injection. Caprock compressibility has a positive effect on pressure perturbation in the system at 10 years after injection ends. A higher caprock compressibility allows for greater pressure absorption in the caprock pore space; therefore decreasing the pressure in the underlying reservoir. These results indicate the parameters tested are all deserving of additional research; however, caprock compressibility and permeability are the dominant factors which influence pressure perturbation in the caprock and reservoir.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-12-01The student, Brynne Storsved, accepted the attached license on 2016-12-08 at 11:33.The student, Brynne Storsved, submitted this Thesis for approval on 2016-12-08 at 11:38.This Thesis was approved for publication on 2016-12-09 at 13:38.DSpace SAF Submission Ingestion Package generated from Vireo submission #10483 on 2017-02-28 at 14:43:24Made available in DSpace on 2017-03-01T17:02:08Z (GMT). No. of bitstreams: 2
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Previous issue date: 2016-12-09Embargo set by: Seth Robbins for item 98739
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Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 98739
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Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 98739
Lift date: 2019-03-01T17:05:02Z
Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 98739
Lift date: 2019-03-01T17:06:55Z
Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemLimited Restriction Lifted for Item 98739 on 2019-03-02T10:15:18Z
Impact of nonideal transport upon the effectiveness of pump-and-treat groundwater remediation: A computational investigation
No full textThe impact of various nonidealities affecting pump and treat remediation was analyzed using numerical simulations. In particular, the effect of the following important non-idealities was investigated: spatial variability of hydraulic conductivity (K-field heterogeneity), spatial variability of sorption parameters, rate-limited desorption, and the combined effects of the latter two with K-field heterogeneity. Heterogeneous K-fields are modelled as spatially correlated lognormal random fields in this thesis. A hypothetical problem scenario was used to illustrate the effects of most of these nonidealities. The effect of K-field heterogeneity was also examined for a plume formed by naturally leaching conditions.Monte Carlo simulations were used to analyze the impact of uncertainty of K-field heterogeneity on the uncertainty of cleanup times. For the highest K-field variability of \sigma\sb{\rm Y} = 2.0, the uncertainty in the 95% cleanup time estimated by coefficient of variation was approximately 0.2. Some analytical results derived for travel time moments, radial velocity variances, and effective hydraulic conductivity were compared with the numerical results.Efficient codes were developed to for the solution of three-dimensional groundwater flow and solute transport problems on supercomputers. The codes were developed for Cray Y-MP/C90 which is a shared memory vector/parallel computer and the connection machine CM-5 which is a distributed memory massively parallel computer. For the groundwater flow problem, a finite-element/finite-difference code coupled with a conjugate gradient matrix solver was developed to work efficiently on both machines. For the solute transport problem, a finite-element/finite-difference code coupled with a GMRES matrix solver and a particle tracking code were developed for both machines.Made available in DSpace on 2011-05-07T13:47:49Z (GMT). No. of bitstreams: 2
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Previous issue date: 1995Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding ([email protected]) on 2011-05-07T14:59:16Z
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Modeling transport of agricultural chemicals in a dual porosity system resulting from macropores
No full textA conceptual model, based upon the bicontinuum double porosity approach, has been developed to simulate water flow and reactive chemical transport in both the soil matrix and the macropore region. Flow of water in both regions is governed by Richards' equation and chemical transport is based upon convective-dispersive mechanisms with linear kinetic sorption and first-order decay. The pressure-water content and the pressure-conductivity relationship for the unsaturated soil in each of the two regions are based upon the van Genuchten-Mualem relationships. The developed model was in one- and two-space dimensions and it contained several improvements over the previously proposed one-dimensional conceptual flow and transport models.Convergence was a problem when the magnitude of coupling or the size of the time step was large. Analyses of the magnitudes of the flow and transport parameters on the prediction of pressure and concentration profiles were presented. These include fluid and solute mass transfer coefficients, saturated hydraulic conductivity of the interface through which mass transfer takes place, sorption distribution coefficient and reaction rate, decay rate, and root extraction of water. From the pressure and the concentration profiles, it was observed that the fluid and the solute mass transfer coefficients have the greatest impact on model predictions. Sorption distribution coefficients and reaction rates appeared to have moderate impact on concentration profiles. For short durations of simulation (e.g. minutes to a few hours), decay rates and root uptake of water appeared to have negligible effect.The model was used to study the impact of agricultural management practices such as row versus nonrow crops, conventional versus conservation tillage, organic amendment, and furrow irrigation on the prediction of pressure and concentration profiles for a local soil with a given degree of macroporosity. With the chosen set of parameters, the importance of macropores on the deep movement of water and chemicals was observed. Once the model parameters for a given soil type are estimated and the model is validated, it can effectively be used as a management tool to evaluate the impact of agricultural management practices.Made available in DSpace on 2011-05-07T12:40:23Z (GMT). No. of bitstreams: 2
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Previous issue date: 1994Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding ([email protected]) on 2011-05-07T14:44:10Z
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Hard and soft cap groundwater allocations: a comparison of groundwater pumping restrictions on hydrologic and economic outcomes
Get PDF"While groundwater is an important primary and supplementary source of water in the western United States, its overuse can lead to negative consequences such as stream depletion, seawater intrusion, or land subsidence. An increasing number of groundwater management districts are restricting individual pumping in an effort to limit, or even reverse, such consequences.
The nature of groundwater availability lends itself to more flexible allocation schemes, which I call ""hard caps"" and ""soft caps."" While a hard cap sets a groundwater user's maximum pumping in a single year, a soft cap allows a groundwater user to meet a multi-year average so that the user may pump more in some years and less in others. While there are many examples of hard and soft caps for groundwater in practice, no study to date has compared the resulting hydrologic and economic outcomes of each scheme.
Using coupled agronomic, economic, and hydrologic models, I examine the performance of hard and soft caps for groundwater-fed irrigation. Irrigated agriculture uses the majority of groundwater in the United States and therefore the sector represents a significant stakeholder in the development of allocation schemes. I model the profit-maximizing decisions for an agricultural producer growing irrigated corn in western Nebraska. I illustrate the hydrologic and economic outcomes in the case of groundwater-induced stream depletion, which is a spatially and temporally heterogeneous consequence, or externality, of groundwater pumping. To do so, I combine datasets on regional climate, soils, economic parameters, and aquifer properties and use them as inputs to an instraseasonal crop-water model, an economic optimization, and a stream depletion model.
I show that at moderate allocation levels, the soft cap results in higher expected profits and lower variance of profits. However, it can come at a cost: In exceptionally dry years, the soft cap can result in acute groundwater pumping, and therefore, stream depletion. The severity of this result depends on the hydrologic properties of the aquifer and well location. At non-binding or very binding allocation levels, the performances of the caps are similar. The implications for developing appropriately flexible allocations will depend on the combined needs for groundwater management and well-specific properties, meaning that a blended approach to caps in some instances may be desirable."Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-03-13 without embargo termsThe student, Richael Young, accepted the attached license on 2017-12-13 at 11:33.The student, Richael Young, submitted this Thesis for approval on 2017-12-13 at 12:18.This Thesis was approved for publication on 2017-12-14 at 15:55.DSpace SAF Submission Ingestion Package generated from Vireo submission #11970 on 2018-03-13 at 10:12:23Made available in DSpace on 2018-03-13T15:49:16Z (GMT). No. of bitstreams: 3
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Previous issue date: 2017-12-1
Agent-based models to couple natural and human systems for watershed management analysis
Get PDFThis dissertation expands conventional physically-based environmental models with human factors for watershed management analysis. Using an agent-based modeling framework, two approaches, one based on optimization and the other on data mining-are applied to modeling farmers' pumping decision-making processes in the High Plains aquifer within the hydrological observatory area. The resulting agent-based models (ABMs) are coupled with a physically-based groundwater model to investigate the interactions between farmers and the underlying groundwater system.
With the optimization-based approach, the computational intensity arises from the execution of the resulting coupled ABM and groundwater model. This dissertation develops a computational framework that utilizes multithreaded programming and Hadoop-based cloud computing to address the computational issues. The framework allows multiple users to access and execute the web-based application of the coupled models simultaneously without an increase in latency via computer network. In addition, another computational framework to combine Hadoop-based Cloud Computing techniques with Polynomial Chaos Expansion (PCE) based variance decomposition approach is developed to conduct global sensitivity analysis with the coupled models, and influential behavioral parameters which are used to simulate agents’ behavior are identified.
Being different from the optimization-based approach, which assumes all agents are rational, the data-driven approach attempts to account for the influences of agents’ bounded rationality on their behavior. A directed information graph (DIG) algorithm is used to exploit the causal relationships between agents’ decisions (i.e., groundwater irrigation depth) and time-series of environmental, socio-economical and institutional variables, and a machine learning technique, boosted regression tree (BRT) is applied to converting these causal relationships to agents’ behavioral rules. It is found that, in comparison with the optimization-based approach, crop profits and water tables as the result of agents’ pumping behavior derived using the data-driven approach can better mimic the actual observations. Thus, we can conclude that the data-driven approach using DIG and BRT outperforms the optimization-based approach when capturing agents’ pumping behavioral uncertainty as the result of bounded rationality, and for simulating real-world behaviors of agents.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-05-01The student, Yao Hu, accepted the attached license on 2016-02-01 at 09:52.The student, Yao Hu, submitted this Dissertation for approval on 2016-02-01 at 10:23.This Dissertation was approved for publication on 2016-02-09 at 11:34.DSpace SAF Submission Ingestion Package generated from Vireo submission #9058 on 2016-07-07 at 14:16:01Made available in DSpace on 2016-07-07T21:04:22Z (GMT). No. of bitstreams: 2
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Previous issue date: 2016-02-09Embargo set by: Seth Robbins for item 93218
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Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 93218
Lift date: 2018-07-07T21:14:52Z
Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 93218
Lift date: 2018-07-07T21:18:16Z
Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemLimited Restriction Lifted for Item 93218 on 2018-07-08T09:15:09Z
The optimal design of groundwater quality monitoring networks under conditions of uncertainty
No full textThe design of a monitoring network to provide initial detection of groundwater contamination at a waste disposal facility is complicated by uncertainty in both the characterization of the subsurface and the nature of the contaminant source. In addition, monitoring network design requires the resolution of multiple conflicting objectives. A method is presented that both incorporates system uncertainty in monitoring network design and provides network alternatives that are noninferior with respect to several objectives. Monte Carlo simulation is the method of uncertainty analysis. The random inputs to the simulation are the hydraulic conductivity field and the contaminant source location. A transport model is used to generate a series of random plumes representing equally likely contamination scenarios. For each plume, the method finds the set of potential monitoring locations at which the plume is detectable. In addition, the area of each plume is recorded at the time when it reaches each potential monitoring location. This information is used to formulate one of two optimization models. The design objectives considered are (1) minimize the number of monitoring wells, (2) maximize the probability of detecting a contaminant leak, and (3) minimize the expected area of contamination at the time of detection. The network design method is applied to a generic problem. Results illustrate the tradeoffs between objectives and the configurations of noninferior network solutions. The probability of detection is increased by using more monitoring wells or by locating the wells farther from the source. The latter case results in an increase in the average area of the detected plumes. If monitoring is carried out very close to the contaminant source to reduce the expected area of a detected plume, a large number of wells is required to provide a high probability of detection. These tradeoffs are an important factor in network design unless the cost (as expressed by the number of monitoring wells) is of limited concern. The importance of using a sufficiently large number of plume realizations in the Monte Carlo simulation is demonstrated. Finally, a sensitivity analysis illustrates the importance of several model parameters, including the hydraulic conductivity field variance and correlation scale, the transverse dispersivity, and the size of the contaminant source.Made available in DSpace on 2011-05-07T13:43:46Z (GMT). No. of bitstreams: 2
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Previous issue date: 1992Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding ([email protected]) on 2011-05-07T14:58:26Z
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Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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