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Title: ANALYZING FACTORS WHICH AFFECT LEGIONELLA OCCURRENCE IN A FULL-SCALE GREEN BUILDING PREMISE PLUMBING SYSTEM
Creator: Julien, Ryan
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Water consumption in the United States has decreased in recent decades due to improved water efficiency and adoption of water conservation practices. However, plumbing design guidance has not been updated to reflect this change, resulting in increased hydraulic retention time, disinfectant decay, and the proliferation of opportunistic premise plumbing pathogens (OPPPs) such as Legionella pneumophila. Time spent in premise plumbing systems has been shown to impact water quality through such...
Show moreWater consumption in the United States has decreased in recent decades due to improved water efficiency and adoption of water conservation practices. However, plumbing design guidance has not been updated to reflect this change, resulting in increased hydraulic retention time, disinfectant decay, and the proliferation of opportunistic premise plumbing pathogens (OPPPs) such as Legionella pneumophila. Time spent in premise plumbing systems has been shown to impact water quality through such mechanisms as the loss of residual disinfectant, leaching of pipe materials, biofilm formation, and increased concentrations of opportunistic pathogens such as Legionella spp.Quantitative Microbial Risk Assessment (QMRA) is a tool used to evaluate human health risks, and has been used to assess risks associated with Legionella. However, these assessments require data regarding the concentration of Legionella in water. Due to the ubiquity of Legionella in plumbing systems, their growth in biofilms, and the sporadic nature of biofilm detachment, Legionella concentrations are poorly understood, thus limiting the utility of QMRA in this instance. Factors which influence the prevalence of Legionella have been studied at the bench scale, but never in a full-scale building water system. The work presented herein takes a risk factor approach in exploring how to better monitor or predict concentrations of Legionella spp. This dissertation presents research to help better understand factors which best predict Legionella spp. Research objectives of this work were to: (1) identify variables which most effectively predict Legionella spp. concentrations using multiple relevant statistical methods, (2) determine the time water spends stored in building plumbing prior to use using a novel model, and (3) determine whether compliance with common temperature guidelines to limit Legionella proliferation has a significant influence on Legionella spp. concentrations. This research employs a rich data set from a full-scale home, equipped with flowmeters and temperature sensors to assess water conditions. Analytical samples were also collected to determine common water quality variables, as well as enumeration of Legionella spp. Multiple statistical analyses, including Spearman’s rank correlation, principal component analysis, generalized linear modeling, and a Bayesian variable selection technique were used to investigate variable relationships and to evaluate the value of model results in predicting Legionella spp. concentrations. Principal component analysis suggests that water age and biofilm detachment are the primary drivers of changes observed in water quality, accounting for 53% of the total variance in the data. General linear modeling revealed that heterotrophic plate count, total organic carbon, total cell count, maxTSL and meanTSL (metrics describing water use), and modeled water age were each significant predictors of Legionella spp. concentrations at the p < 0.05 level. Bayesian variable selection indicated that the 95th percentile of water age and maxTSL were most predictive of Legionella spp. concentrations. Results from the water age model were evaluated, indicating that modeled water age is indeed a statistically significant (p < 0.05) predictor of Legionella spp. Compliance with common temperature guidelines was found to be significantly correlated (ρ = 0.22, p < 0.001) to Legionella spp Results of this research indicate that water quality and water use have significant implications to Legionella occurrence. These results provide a framework to investigate Legionella spp. using a limited set of variables which are more commonly and cheaply measured than direct measurement, which could encourage more widespread monitoring for Legionella and ultimately reduce the incidence of illness. While water age remains poorly understood, these results show that water age is a critical factor in determining Legionella spp. prevalence. This knowledge should be applied to plumbing design and maintenance to limit water age and thereby Legionella spp. concentrations. Statistical significance between compliance with commonly cited temperature guidelines and Legionella spp. concentrations indicate that these guidelines do provide some level of control, and should be considering in design and operation of premise plumbing systems.
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Title: Water content effect on nutrient removal in stormwater bioretention systems
Creator: Bender, Rebecca Marian
Date: 2019
Collection: Electronic Theses & Dissertations
Description: "Bioretention cells and constructed wetlands are both established best management practices (BMPs) for stormwater quality improvement. These systems vary in terms of hydraulic loading where processes such as retention, sedimentation, absorption, infiltration, filtration, phytoremediation, nitrification and denitrification remove waterborne pollutants. However, the boundary between bioretention and wetlands can be blurred when it comes to design and operational parameters, and it is therefore...
Show more"Bioretention cells and constructed wetlands are both established best management practices (BMPs) for stormwater quality improvement. These systems vary in terms of hydraulic loading where processes such as retention, sedimentation, absorption, infiltration, filtration, phytoremediation, nitrification and denitrification remove waterborne pollutants. However, the boundary between bioretention and wetlands can be blurred when it comes to design and operational parameters, and it is therefore important to explore the causes and consequences of performance variability in these systems. In an experiment to observe optimum water content for treatment pathways for ecological pollutants, five bioretention bays (2-22% water content) and fifteen bioretention columns (7-47% water content, as much as complete pore space saturation) were used to run parallel tests. Pollutant concentrations were reduced in field bays for COD, TN, and total solids (TS), although there was no difference between treatment groups in terms of any pollutant concentrations. Asclepias incarnata, Carex vulpinoidea, Scirpus validus, and Juncus effusus grew slightly taller in wetter bays, although survival of Sagittaria latifolia was uniformly poor in all treatment groups. No net pollutant removal occurred in columns, although effluent concentrations and mass export were significantly lower for near-saturation treatment groups for chemical oxygen demand (COD), nitrate, and total nitrogen (TN). There was no soil moisture level in which COD, nitrate, TN, phosphate, and TS were simultaneously improved."--Page ii.
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Title: Modeling risk for intranasal, inhalation, and corneal exposures to opportunistic pathogens of concern in drinking water
Creator: Dean, Kara
Date: 2019
Collection: Electronic Theses & Dissertations
Description: "This study developed dose response models for determining the probability of eye, respiratory or central nervous system infections from previously conducted studies using Naegleria fowleri, Acanthamoeba spp. and Pseudomonas aeruginosa. These opportunistic pathogens have been identified in drinking water and premise plumbing systems, and a lack of dose response models for the appropriate exposure routes of concern has prevented researchers from quantifying the risk they pose to human health....
Show more"This study developed dose response models for determining the probability of eye, respiratory or central nervous system infections from previously conducted studies using Naegleria fowleri, Acanthamoeba spp. and Pseudomonas aeruginosa. These opportunistic pathogens have been identified in drinking water and premise plumbing systems, and a lack of dose response models for the appropriate exposure routes of concern has prevented researchers from quantifying the risk they pose to human health. Using the newly developed dose response model for P. aeruginosa, a reverse quantitative microbial risk assessment (QMRA) was completed to determine the threshold concentrations of P. aeruginosa associated with an annual risk of 10-4 for corneal and inhalation exposures. The results indicated that an average concentration of 1 CFU/L in the bulk water could result in an annual risk greater than the guideline set by the Environmental Protection Agency. The threshold concentration responsible for a 10 -4 risk of pneumonia from P. aeruginosa was 11 orders of magnitude greater than the threshold concentration for bacterial keratitis. Modeling all possible exposure routes of concern for opportunistic pathogens in drinking water is critical, as the exposure route dramatically affects the concentrations of concern. This reverse QMRA and future risk assessments that utilize the dose response models developed in this study can be used to inform decisions on drinking water treatment, monitoring protocols, and future plumbing design."--Page ii.
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Title: Quantifying the relationships between lake nutrients and agricultural land use/cover at different spatial scales
Creator: Droscha, Katie L.
Date: 2012
Collection: Electronic Theses & Dissertations
Description: QUANTIFYING THE RELATIONSHIPS BETWEEN LAKE NUTRIENTS AND AGRICULTURAL LAND USE/COVER AT DIFFERENT SPATIAL SCALESByKatie L. DroschaI quantified agricultural land use/cover using five spatial metrics to determine which spatial metric was most strongly correlated with seasonal lake total phosphorus and total nitrogen concentrations in 204 Michigan lakes. I used two distance based spatial metrics, two flow direction spatial metrics and full lake catchments. Agricultural land use/cover was...
Show moreQUANTIFYING THE RELATIONSHIPS BETWEEN LAKE NUTRIENTS AND AGRICULTURAL LAND USE/COVER AT DIFFERENT SPATIAL SCALESByKatie L. DroschaI quantified agricultural land use/cover using five spatial metrics to determine which spatial metric was most strongly correlated with seasonal lake total phosphorus and total nitrogen concentrations in 204 Michigan lakes. I used two distance based spatial metrics, two flow direction spatial metrics and full lake catchments. Agricultural land use/cover was categorized as pasture, forage, row crop and total agriculture. My research questions were 1) which agricultural type and spatial metric is the most strongly correlated with lake nutrient concentrations, 2) How similar are the spatial metrics that quantify agricultural land use/cover in lake catchments, 3) Do the different spatial metrics each contribute to explaining lake nutrients, 4) Do different agricultural types have similar effects on lake nutrients. I used correlation coefficients to quantify the relationship between agricultural types, spatial metrics and seasonal lake TP and TN. Among agricultural types, the catchment, moderate-highly contributing area and 100 m lake-stream buffer spatial metrics were strongly correlated with each other, meaning they measured similar aspects of the landscape. Row crop and total agriculture had the strongest correlations for spring and summer lake nutrients, however, linear regression analysis within the 100 m lake-stream buffer reveal that for a change in one unit of pasture or forage, there is a larger change in lake nutrients when compared to row crop and total agriculture. Further research is needed to more fully quantify the effects of pasture and forage lands on lake nutrients.
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Title: Evaluation of three plant species for stormwater treatment in bioretention basins
Creator: Stuber, Jenifer Cracroft
Date: 2012
Collection: Electronic Theses & Dissertations
Description: "Stormwater frequently contains contaminants that pollute ground and surface waters. As water becomes an increasingly scarce commodity, groundwater recharge and preventing water pollution has been identified as a key aspect of sustainability. Recent research shows bioretention basins as an effective management practice to reduce pollutants of concern in stormwater including total suspended solids, oil and grease, heavy metals, pathogenic bacteria, and some forms of nutrients. This study...
Show more"Stormwater frequently contains contaminants that pollute ground and surface waters. As water becomes an increasingly scarce commodity, groundwater recharge and preventing water pollution has been identified as a key aspect of sustainability. Recent research shows bioretention basins as an effective management practice to reduce pollutants of concern in stormwater including total suspended solids, oil and grease, heavy metals, pathogenic bacteria, and some forms of nutrients. This study evaluates three different plant species for use in bioretention basins. Two native wetland species, Carex comosa and Iris virginica and one non native plant species, Poa pratensis were tested to evaluate stormwater treatment in bioretention basins."--From abstract.
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Title: Effectiveness of wastewater land application : monitoring and modeling
Creator: Dong, Younsuk
Date: 2018
Collection: Electronic Theses & Dissertations
Description: Wastewater land application has been used for decades because of its low cost, energy use, and maintenance requirements, compared to a conventional wastewater treatment system. The performance of treatment depends on the hydraulic and organic wastewater loadings, soil characteristics, and soil conditions. Understanding the complexity of soil is important. The aerobic or anaerobic condition of the soil may result in nitrate leaching and metal mobilization into groundwater, respectively....
Show moreWastewater land application has been used for decades because of its low cost, energy use, and maintenance requirements, compared to a conventional wastewater treatment system. The performance of treatment depends on the hydraulic and organic wastewater loadings, soil characteristics, and soil conditions. Understanding the complexity of soil is important. The aerobic or anaerobic condition of the soil may result in nitrate leaching and metal mobilization into groundwater, respectively. Currently, design criteria are generally based on empirical relationships, which do not adequately consider site and waste-specific conditions. Because organic and hydraulic loadings are generally fixed based on production, dosing is the only operational parameter that can be adjusted to enhance treatment for site-specific conditions. In this study, an evaluation of domestic and food processing wastewaters land application systems were performed including examining their benefits, effectiveness, and techniques for modeling. Monitoring strategies at the demonstration site showed the viability of using land application to treat food processing wastewater and helps in making an operation decision. The HYDRUS Constructed Wetland 2D (CW2D) model was successfully calibrated and validated using data from laboratory experiments. The modeling results showed that most of the COD removal in a domestic wastewater land application system occurs within a 30.5 cm (1 ft) depth for a sandy loam soil. Increasing the dosing frequency was effective in slightly reducing the COD effluent concentration. An increase in nitrate removal by changing dosing frequency while providing sufficient carbon was found to be possible.
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Title: Metamodeling framework for simultaneous multi-objective optimization using efficient evolutionary algorithms
Creator: Roy, Proteek Chandan
Date: 2019
Collection: Electronic Theses & Dissertations
Description: Most real-world problems are comprised of multiple conflicting objectives and solutions to those problems are multiple Pareto-optimal trade-off solutions. The main challenge of these practical problems is that the objectives and constraints do not have any closed functional forms and they are expensive for computation as well. Objectives coming from finite element analysis, computational fluid dynamics software, network flow simulators, crop modeling, weather modeling or any other simulations...
Show moreMost real-world problems are comprised of multiple conflicting objectives and solutions to those problems are multiple Pareto-optimal trade-off solutions. The main challenge of these practical problems is that the objectives and constraints do not have any closed functional forms and they are expensive for computation as well. Objectives coming from finite element analysis, computational fluid dynamics software, network flow simulators, crop modeling, weather modeling or any other simulations which involve partial differential equations are good examples of expensive problems. These problems can also be regarded as l03000300ow-budget'' problems since only a few solution evaluations can be performed given limited time. Nevertheless, parameter estimation and optimization of objectives related to these simulations require a good number of solution evaluations to come up with better parameters or a reasonably good trade-off front. To provide an efficient search process within a limited number of exact evaluations, metamodel-assisted algorithms have been proposed in the literature. These algorithms attempt to construct a computationally inexpensive representative model of the problem, having the same global optima and thereby providing a way to carry out the optimization in metamodel space in an efficient way. Population-based methods like evolutionary algorithms have become standard for solving multi-objective problems and recently Metamodel-based evolutionary algorithms are being used for solving expensive problems. In this thesis, we would like to address a few challenges of metamodel-based optimization algorithms and propose some efficient and innovative ways to construct these algorithms. To approach efficient design of metamodel-based optimization algorithm, one needs to address the choice of metamodeling functions. The most trivial way is to build metamodels for each objective and constraint separately. But we can reduce the number of metamodel constructions by using some aggregated functions and target either single or multiple optima in each step. We propose a taxonomy of possible metamodel-based algorithmic frameworks which not only includes most algorithms from the literature but also suggests some new ones. We improve each of the frameworks by introducing trust region concepts in the multi-objective scenario and present two strategies for building trust regions. Apart from addressing the main bottleneck of the limited number of solution evaluations, we also propose efficient non-dominated sorting methods that further reduce computational time for a basic step of multi-objective optimization. We have carried out extensive experiments over all representative metamodeling frameworks and shown that each of them can solve a good number of test problems. We have not tried to tune the algorithmic parameters yet and it remains as our future work. Our theoretical analyses and extensive experiments suggest that we can achieve efficient metamodel-based multi-objective optimization algorithms for solving test as well as real-world expensive and low-budget problems.
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Title: Agronomic management of corn using seasonal climate predictions, remote sensing and crop simulation models
Creator: Jha, Prakash Kumar
Date: 2019
Collection: Electronic Theses & Dissertations
Description: Management decisions in corn (Zea mays mays L) production are usually based on specific growth stages. However, because of climate and weather variability, phenological stages vary from season to season across geographic locations. This variability in growth and phenology entails risks and quantifying it will help in managing climate related risks. Crop simulation models can play a significant role in minimizing these risks through designing management strategies; however, they are not always...
Show moreManagement decisions in corn (Zea mays mays L) production are usually based on specific growth stages. However, because of climate and weather variability, phenological stages vary from season to season across geographic locations. This variability in growth and phenology entails risks and quantifying it will help in managing climate related risks. Crop simulation models can play a significant role in minimizing these risks through designing management strategies; however, they are not always accurate. Remote sensing observations and climate predictions can improve the accuracy in managing time bound climate-sensitive decisions at larger spatiotemporal scale. However, there is also a disconnect between climate forecasts and crop models. The unavailability of downscaling tool that can downscale rainfall and temperature forecasts simultaneously make this task more challenging. To address these knowledge gaps, this dissertation consists of three studies focused on interdisciplinary approaches to agronomic management of corn.In the first study, we calibrated and validated genetic coefficients of CERES-Maize using field data from the Michigan corn performance trials. Multiple methods of estimating genetic coefficients GENCALC (Genotype Coefficient Calculator), GLUE (Generalized Likelihood Uncertainty Estimate), and NMCGA (Noisy Monte Carlo Genetic Algorithm) were evaluated and ensembled to estimate more reliable genetic coefficients. The calibrations were done under irrigated conditions and validation under rainfed conditions. The results suggested that ensembled genetic coefficients performed best among all, with d-index of 0.94 and 0.96 in calibration and validation for anthesis and maturity dates, and yield.In the second study, simulated growth stages from the calibrated crop model were used to develop site-specific crop coefficients (kc) using ensembled ET and reference ET from the nearest weather station. ET from multiple models were ensembled and validated with the measured ET from eddy-covariance flux towers for 2010 - 2017. Results suggest that the ensembled ET performed best among all ET models used, with highest d-index of 0.94. Likewise, the performance of the newly derived kc-curve was compared with FAO-kc curve using a soil water balance model. Then, the derived region-specific Kc-curve was used to design irrigation scheduling and results suggest that it performed better than FAO Kc-curve in minimizing the amount irrigation while maintaining a prescribed allowable water stress.The third study used the calibrated crop model to simulate anthesis using downscaled seasonal climate forecasts. The predicted anthesis and downscaled seasonal climate forecasts were used to develop risk analysis model for ear rot disease management in corn. In this study an innovative downscaling tool, called FResamplerPT, was introduced to downscale rainfall and temperature simultaneously. The results suggest that temperature and relative humidity are better predictors (combined) as compared to temperature and rainfall (combined). With this risk analysis model, growers can evaluate and assess the future climatic conditions in the season before planting the crops. The seasonal climate information with the lead-time of 3 months can help growers to prepare integrated management strategies for ear rot disease management in maize.
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Title: Edge impact in graphs and social network matrix completion
Creator: Ross, Dennis
Date: 2016
Collection: Electronic Theses & Dissertations
Description: Every graph G can be associated with many well-known invariant properties along with their corresponding values. A framework is proposed to measure the change in any particular invariant upon addition of a new edge $e$ in the resulting graph G+e. In graphs, the P-impact of an edge e is the `magnitude' of the difference between the values of the invariant P in graphs G+e from G. Several famous invariants are explored and a proof towards optimal edge addition for distance-impact in trees is...
Show moreEvery graph G can be associated with many well-known invariant properties along with their corresponding values. A framework is proposed to measure the change in any particular invariant upon addition of a new edge $e$ in the resulting graph G+e. In graphs, the P-impact of an edge e is the `magnitude' of the difference between the values of the invariant P in graphs G+e from G. Several famous invariants are explored and a proof towards optimal edge addition for distance-impact in trees is given. A natural application to measuring the impact of edge addition to a graph is that of link prediction. An efficient algorithm for link prediction even with cold-start vertices using a subspace sharing method that decouples matrix completion and side information transduction is presented. This method is extended to predict ratings in user-item recommender systems where both may be cold-start.
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Title: Treatment of agricultural wastewater with constructed wetlands
Creator: Adhikari, Umesh
Date: 2012
Collection: Electronic Theses & Dissertations
Description: Animal manure contains essential plant nutrients such as nitrogen, phosphorus and potassium along with high number of bacteria, viruses and parasites. Pollutants contained in manure enter water bodies both as diffuse or non-point source and as point source from concentrated livestock production systems. Pathogens may flow to the water bodies when manure is applied to fields prior to rainfall. Nutrients that reach water bodies cause eutrophication and pathogens pose health risk. Two separate...
Show moreAnimal manure contains essential plant nutrients such as nitrogen, phosphorus and potassium along with high number of bacteria, viruses and parasites. Pollutants contained in manure enter water bodies both as diffuse or non-point source and as point source from concentrated livestock production systems. Pathogens may flow to the water bodies when manure is applied to fields prior to rainfall. Nutrients that reach water bodies cause eutrophication and pathogens pose health risk. Two separate studies were conducted to evaluate the applicability of constructed wetlands in treating pollutants originating from animal manure. One constructed wetland system was spiked with high number of E. coli and bacteriophage P22 for a short period of time to simulated tile-drain flow and the number of E. coli and bacteriophage P22 in the effluent were monitored in winter and summer seasons. The other constructed wetland system was continuously supplied with diluted dairy wastewater and removal of pollutants and recovery of nutrients were measured. On average, 0.54 and 0.69 log reduction of E. coli were obtained in summer and winter months, respectively from the surface flow (SF) wetlands subjected to pulse loading. With similar loading, 3.16 and 1.23 log reduction of E. coli were obtained from subsurface flow (SSF) wetlands in summer and winter months, respectively. E. coli removal in subsurface flow wetland was higher than in surface flow wetland in both seasons. Two models one based on the convection dispersion equation (CDE) and the other based on colloid filtration theory did not adequately describe E. coli removal in constructed wetlands. Higher removal of bacteriophage P22 was observed in both SF and SSF wetlands in both winter and summer months in the wetlands subjected to pulse loading. P22 removal rates in SSF wetlands were 41 times the removal rate in SF wetlands in winter and 19 times in summer. The CDE model could accurately describe bacteriophage P22 removal in constructed wetlands.In the wetland systems that were subjected to continuous manure loading, chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP) and E. coli in influent and effluent were measured. Duckweed was harvested every week to explore the nutrient recovery potential. Average COD, TN and TP removal obtained in surface flow wetlands from dairy wastewater were 28%, 28% and 16% respectively. Average annual mass removal of COD, TN and TP in the wetlands were 2137 g COD/m2/year, 149.5 g N/m2/year and 10.3 g P/m2/year, respectively. First order removal model that includes background concentration was found more suitable than first order model or DUBWAT model for predicting effluent COD, TN and TP removal in constructed wetlands. Average N and P recovered by harvesting duckweed across all the wetlands were 22.4 g N/m2/year and 5.6 P/m2/year, respectively.
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Title: Hydrologic analysis of a semi-arid watershed using kinematic wave and SCS flow models
Creator: Syed, Atiq Ur-Rehman
Date: 2012
Collection: Electronic Theses & Dissertations
Description: This research explores the efficiency of kinematic wave and Soil Conservation Service (SCS) flow models at a watershed scale in a semi-arid environment. The scope of this research is based on the hypothesis that flow models based on the simplest approximation of the full dynamic equations (kinematic wave, hydraulic) produce output variables that are representative of the watershed system compared to flow models that rely only on the continuity equation (SCS, hydrologic). The overall objective...
Show moreThis research explores the efficiency of kinematic wave and Soil Conservation Service (SCS) flow models at a watershed scale in a semi-arid environment. The scope of this research is based on the hypothesis that flow models based on the simplest approximation of the full dynamic equations (kinematic wave, hydraulic) produce output variables that are representative of the watershed system compared to flow models that rely only on the continuity equation (SCS, hydrologic). The overall objective of this research study is to provide an improved understanding of kinematic wave and SCS flow models and compare their efficiencies to the observe flow data. Physical data such as precipitation, runoff, soils, and topography was derived from the Walnut Gulch Experimental Watershed (WGEW) in the southwest United States. Several important conclusions have emerged from this study that can prove useful to a practicing engineer/hydrologist. First, the kinematic-wave model proved to be a satisfactory tool to predict surface runoff in semi-arid watersheds, where transmission losses are a significant factor besides initial abstraction in the overall water budget computations. Analysis of the "Peak-Weighted Root Mean Squared Error" (PRMSE) values between the computed models (kinematic wave and SCS flow) and observed flow data for the three study watersheds show that the kinematic wave flow model has lower values of objective function compared to SCS flow model. Since PRMSE function is an implicit measure of comparison of the magnitudes of the peaks, volumes, and times of peak of the two hydrographs, it means that the kinematic wave flow model is more accurate than the SCS flow model. Second, the percent difference in peak flows between the observed data and computed flow results indicates that the kinematic wave model is no more likely to over-predict than to under-predict. On the other hand, the majority of the percent difference in peak flows between the observed and the SCS flow model indicates that the SCS model has a strong tendency to under predicted peak flows. Finally, the kinematic wave accuracy is demonstrated with data encompassing a relatively wide range of field conditions, where the kinematic wave flow model proved advantageous in that it can process spatial and/or temporal rainfall and overland and channel roughness variations, which the SCS model, by virtue of it being a lumped model, cannot.
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Title: GIS-enabled modeling of Michigan's groundwater systems
Creator: Oztan, Mehmet
Date: 2011
Collection: Electronic Theses & Dissertations
Description: In this paper we systematically evaluate a GIS-enabled and data-intensive modeling system to assess the system's capability to simulate the regional-scale ground water flow in 26 of 38 USGS 8-digit Hydrologic Unit Codes (HUCs) across Michigan's Lower Peninsula. All models simultaneously simulate the glacial and uppermost bedrock aquifer layer in the peninsula. Specifically, the modeling system is used to simulate the long-term average static water levels and base flow in the glacial aquifer,...
Show moreIn this paper we systematically evaluate a GIS-enabled and data-intensive modeling system to assess the system's capability to simulate the regional-scale ground water flow in 26 of 38 USGS 8-digit Hydrologic Unit Codes (HUCs) across Michigan's Lower Peninsula. All models simultaneously simulate the glacial and uppermost bedrock aquifer layer in the peninsula. Specifically, the modeling system is used to simulate the long-term average static water levels and base flow in the glacial aquifer, and long-term average piezometric heads in the bedrock aquifer. The results are compared with the measured ground water levels from Michigan's statewide ground water database (MSGWD) and USGS-estimated base flow values. The coupling between the models and data allows real-time and interactive analysis of the assumptions and boundary conditions used in the conceptual models. Overall, this paper presents the most extensive regional-scale modeling and evaluation effort in Michigan to date that provides critical insight into the state's ground water systems. We postulate that the results of this research will have significant implications on the sustainable management of Michigan's ground water resources and ground water dependent ecosystems.
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Title: Assessing the impacts of post-construction best management practices on stormwater runoff in an ultra-urban environment
Creator: Novaes, Valerie
Date: 2015
Collection: Electronic Theses & Dissertations
Description: The effects of urbanization on water resources in the United States and around the world have been well documented by scientists and engineers. Traditional storm sewer systems coupled with detention basins have historically been implemented to mitigate the increased stormwater runoff volume and peak flow rates from urbanized areas. However, this solution has been found to exacerbate the problems associated with increased peak flow rates and runoff volumes in the receiving streams by extending...
Show moreThe effects of urbanization on water resources in the United States and around the world have been well documented by scientists and engineers. Traditional storm sewer systems coupled with detention basins have historically been implemented to mitigate the increased stormwater runoff volume and peak flow rates from urbanized areas. However, this solution has been found to exacerbate the problems associated with increased peak flow rates and runoff volumes in the receiving streams by extending the duration of bank-full flows. Future effectiveness of addressing urbanization must seek to mimic the natural hydrologic processes that occurred prior to urbanization. Low Impact Development is an alternative approach to sewer systems that has been implemented to promote the natural hydrologic processes including evaporation, infiltration, and transpiration. However, detailed full-scale water quantity performance data is scarce. To address this knowledge gap, the following research objectives were developed: (1) evaluate the influential factors that impact infiltration rate in engineered soils, (2) determine the relation between the percentage of unfilled pore space, soil compaction, and plant health, (3) analyze the overall health of the planted community, and (4) evaluate how the bioretention systems have modified the surface runoff hydrograph with respect to change in total volume, the time to peak, the peak flow rate and the overall shape of the runoff hydrograph. An EPA SWMM model was developed to analyze the Michigan Avenue bioretention facilities. Results indicate that a viable alternative exists to the conventional stormwater drainage system that provides substantial reductions in runoff volume, peak flow rates, and increase the time of concentration while changing the overall shape of the runoff hydrograph. Additionally, improvements in data collection and performance testing were provided.
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Title: Online Learning Algorithms for Mining Trajectory data and their Applications
Creator: Wang, Ding
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Trajectories are spatio-temporal data that represent traces of moving objects, such as humans, migrating animals, vehicles, and tropical cyclones. In addition to the geo-location information, a trajectory data often contain other (non-spatial) features describing the states of the moving objects. The time-varying geo-location and state information would collectively characterize a trajectory dataset, which can be harnessed to understand the dynamics of the moving objects. This thesis focuses...
Show moreTrajectories are spatio-temporal data that represent traces of moving objects, such as humans, migrating animals, vehicles, and tropical cyclones. In addition to the geo-location information, a trajectory data often contain other (non-spatial) features describing the states of the moving objects. The time-varying geo-location and state information would collectively characterize a trajectory dataset, which can be harnessed to understand the dynamics of the moving objects. This thesis focuses on the development of efficient and accurate machine learning algorithms for forecasting the future trajectory path and state of a moving object. Although many methods have been developed in recent years, there are still numerous challenges that have not been sufficiently addressed by existing methods, which hamper their effectiveness when applied to critical applications such as hurricane prediction. These challenges include their difficulties in terms of handling concept drifts, error propagation in long-term forecasts, missing values, and nonlinearities in the data. In this thesis, I present a family of online learning algorithms to address these challenges. Online learning is an effective approach as it can efficiently fit new observations while adapting to concept drifts present in the data. First, I proposed an online learning framework called OMuLeT for long-term forecasting of the trajectory paths of moving objects. OMuLeT employs an online learning with restart strategy to incrementally update the weights of its predictive model as new observation data become available. It can also handle missing values in the data using a novel weight renormalization strategy.Second, I introduced the OOR framework to predict the future state of the moving object. Since the state can be represented by ordinal values, OOR employs a novel ordinal loss function to train its model. In addition, the framework was extended to OOQR to accommodate a quantile loss function to improve its prediction accuracy for larger values on the ordinal scale. Furthermore, I also developed the OOR-ε and OOQR-ε frameworks to generate real-valued state predictions using the ε insensitivity loss function.Third, I developed an online learning framework called JOHAN, that simultaneously predicts the location and state of the moving object. JOHAN generates its predictions by leveraging the relationship between the state and location information. JOHAN utilizes a quantile loss function to bias the algorithm towards predicting more accurately large categorical values in terms of the state of the moving object, say, for a high intensity hurricane.Finally, I present a deep learning framework to capture non-linear relationships in trajectory data. The proposed DTP framework employs a TDM approach for imputing missing values, coupled with an LSTM architecture for dynamic path prediction. In addition, the framework was extended to ODTP, which applied an online learning setting to address concept drifts present in the trajectory data.As proof of concept, the proposed algorithms were applied to the hurricane prediction task. Both OMuLeT and ODTP were used to predict the future trajectory path of a hurricane up to 48 hours lead time. Experimental results showed that OMuLeT and ODTP outperformed various baseline methods, including the official forecasts produced by the U.S. National Hurricane Center. OOR was applied to predict the intensity of a hurricane up to 48 hours in advance. Experimental results showed that OOR outperformed various state-of-the-art online learning methods and can generate predictions close to the NHC official forecasts. Since hurricane intensity prediction is a notoriously hard problem, JOHAN was applied to improve its prediction accuracy by leveraging the trajectory information, particularly for high intensity hurricanes that are near landfall.
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Title: Reducing Water and Agrochemical Movement from Container Nursery Production Using Bioreactors and Irrigation Management
Creator: Abdi, Damon Edward
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Container crop production is an input intensive agricultural sector, oftentimes demanding frequent, typically daily irrigation, substantial fertilizer use, and multiple pesticide applications throughout the production cycle. The combination of these factors increases the risk for agrochemical movement in irrigation return flow (IRF). Over the course of two studies, a model nursery designed to collect surface and subsurface IRF was used to investigate water use and agrochemical movement in a...
Show moreContainer crop production is an input intensive agricultural sector, oftentimes demanding frequent, typically daily irrigation, substantial fertilizer use, and multiple pesticide applications throughout the production cycle. The combination of these factors increases the risk for agrochemical movement in irrigation return flow (IRF). Over the course of two studies, a model nursery designed to collect surface and subsurface IRF was used to investigate water use and agrochemical movement in a model nursery. An overhead control was compared to micro-irrigation (SS) and substrate volumetric moisture content (θ) sensor based overhead irrigation (OH) in the volume of water applied, volume of water lost to IRF, and associated fertilizer and pesticide content transported. Irrigating using OH and SS reduced the volume of irrigation applied by 49% and 78% compared to the control. Surface IRF was reduced by 80% using OH and was largely eliminated using SS; however, subsurface IRF was generally equivalent between the control and treatments. Surface IRF movement of nitrate and phosphate was reduced by 72% - 76% when irrigating using OH, and up to 98% when irrigating using SS. Pesticide mobility in irrigation return flow was reflective of pesticide physiochemical properties, with more soluble pesticides exhibiting greater movement than less soluble pesticides, particularly in subsurface IRF. OH reduced surface IRF movement of the 10 pesticides by 43-89%, while SS reduced surface IRF movement by 77-100%. There were typically no differences in subsurface IRF pesticide movement between the control and treatments. For all studied taxa (Cornus obliqua 'Powell Gardens', Cornus sericea 'Farrow', Hydrangea paniculata 'Limelight', Physocarpus opulifolius 'Seward', Rosa x'Meipeporia', Spiraea japonica 'SMNSJMFP', and Weigela florida 'Elvera') an equivalent growth index to the control was achieved when irrigating based on θ. Irrigation treatments were capable of producing an equivalent weight of shoot dry biomass for all taxa except C. obliqua, P. opulifolius, and S. japonica where the control was greater than all treatments. For the three species where root dry biomass was investigated (H. paniculata, R. x., and S. japonica), only S. japonica exhibited reduced root dry biomass under the OH and SS treatment compared to the control. Irrigating based on θ, regardless of the delivery method, can produce woody ornamental species of equivalent quality, while also reducing water use and agrochemical export in irrigation return flow; however, bioactive concentrations of agrochemicals may still be present. Woodchip bioreactors (WB) and adsorbent aggregate filters (AF) are treatment technologies that are capable of remediating or sequestering contaminants from IRF via biological and sorptive processes, provided a sufficient hydraulic retention time (HRT). A 72 hour HRT reduced over 99% of influent nitrate in WB and up to 87% of phosphate in AF; whereas, an HRT of 21 minutes was insufficient for nutrient remediation. An HRT of 21 minutes was effective in reducing the movement of bifenthrin, chlorpyrifos, and oxyfluorfen by 76%, 63%, and 31%, respectively, using WB. Microbial analysis of WB identified shifts in species composition when exposed to pesticides, enriching for a number of species within the Pseudomonas and Exiguobacterium genus, while decreasing the number and diversity of Bacillus species compared to the nutrient only control.
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