A number of mathematical models are used nowadays to describe behaviour of the reallife water distribution system (WDS). It is a well known fact that, to have any meaningful use, any WDS mathematical model must be calibrated first. Here, calibration is defined as process in which a number of WDS model parameters are adjusted until the model mimics behaviour of the real WDS as closely as possible. In this thesis, WDS mathematical models that are used to model water quantity aspect only are analysed. Three hydraulic models considered here are: (1) steady-state flow model, (2) quasi-steady flow (extended period simulation) model and (3) unsteady flow model. The calibration problem analysed here is formulated as a constrained optimisation problem of weighted least square type with the objective defined in a way that enables effective incorporation of prior information on calibration parameters. WDS calibration problem is then analysed in detail, including special issues of identifiability, uniqueness and stability of the problem solution. A list of diagnostic and other statistics and analysis is presented to improve existing calibration approaches by providing partial insight into the calibration process. Calibration of WDS hydraulic models is further improved by the development of new hybrid optimisation method. Being closely related to calibration, the problem of sampling design for calibration of WDS hydraulic models is also addressed here. First, sampling design is formulated as a constrained two-objective optimisation problem. Then, two novel models are developed to solve it. The first model is based on standard, single-objective Genetic Algorithms (SOGA). The second model is based on multi-objective Genetic Algorithms (MOGA). Finally, all novel methodologies presented here are verified successfully on multiple case studies that involve both artificial and real-life WDS. At the end, relevant conclusions are drawn and suggestions for further research work are made.

Bioassessment is the concept of evaluating the ecological condition of habitats by surveying the resident assemblages of living organisms. Conducting bioassessment with benthic macroinvertebrates is still evolving and continues to be refined. There are strongly divided opinions about study design, sampling methods, laboratory analyses, and data analysis. Two issues that are currently being debated about study design for bioassessment in streams were examined here: 1) what habitats within streams should be sampled; 2) and is it necessary to identify organisms to the species level? The influence of habitat sampling design and level of taxonomic identification on the interpretation of ecological conditions of ten small streams in western Virginia was examined. Cattle watering and grazing heavily affected five of these streams (impaired sites). The other five streams, with no recent cattle activity or other impact by man, were considered to be reference sites because they were minimally impaired and represented best attainable conditions. Inferential and non-inferential statistical analyses concluded that multiple habitat sampling design was more effective than a single habitat design (riffle only) at distinguishing impaired conditions, regardless of taxonomic level. It appeared that sampling design (riffle habitat versus multiple habitats) is more important than taxonomic identification level for distinguishing reference and impaired ecological conditions in this bioassessment study. All levels of taxonomic resolution, which were studied, showed that the macroinvertebrate assemblages at the reference and impaired sites were very different and the assemblages at the impaired sites were adversely affected by perturbation. This study supported the sampling of multiple habitats and identification to the family level as a design for best determining the ecological condition of streams in bioassessment. / Master of Science

A potable water distribution system (WDS) consists of pipes, pumps, valves, storage tanks, control and supporting components. Traditionally, it has two basic functions. First, provides end users with potable water at sufficient pressures and good water quality. Second, provides sufficient pressure and flow for fire fighting. Currently, potable water is still the least expensive material for fire fighting. To accomplish these two goals, water utilities have to consider the integrity and security of the water network. As a result, this research selected three research topics that are closely related to the daily operation of water utilities and water quality simulation. The first study is on optimal sampling design for chlorine decay model calibration. Three questions are investigated: (1) What is the minimum number of chlorine sample locations a water network needs? (2) How many combinations of sampling locations are available? (3) What is the optimal location combination? To answer the first two questions, the mathematical expressions of the chlorine concentrations between any two sampling locations are developed and sampling point relationship matrices are generated, then a mixed integer programming (MIP) algorithm is developed. Once obtained, the solutions to the first two questions are used to calculate the chlorine decay wall reaction coefficients and sensitivity matrix of chlorine concentration wall reaction coefficients; then, sampling location combinations achieved in the second question are sorted using a D-optimality algorithm. The model frame is demonstrated in a case study. The advantage of this method, compared to the traditional iterative sensitivity matrix method, is that a prior knowledge or estimation of wall reaction coefficients is not necessary. The second study is on optimizing the operation scheduling of automatic flushing device (AFD) in water distribution system. Discharging stagnant water from the pipeline through AFD is a feasible method to maintain water quality. This study presents a simulation-based optimization method to minimize total AFD discharge volume during a 24-hour horizon. EPANET 2.0 is used as hydraulics and water quality simulator. This is formulated as a single objective optimization problem. The decision variables are the AFD operation patterns. The methodology has three phases. In the first phase, AFD discharge capacities are calculated, whether existing AFDs are able to maintain chlorine residuals in the water network is also evaluated. In the second phase, the decision variables are converted to AFD discharge rates. A reduced gradient algorithm is used to quickly explore and narrow down the solution space. At the end of this phase, decision variables are switched back to the AFD operation patterns. In the third phase, simulated annealing is used to search intensively to exploit the global minimum. The method is demonstrated on the water system located at the south end of Pinellas County, Florida where AFD optimal operation patterns are achieved. The third study is on simulating contaminant intrusion in water distribution system. When contaminant matrix is introduced into water distribution system, it reacts with chlorine in bulk water rapidly and causes fast disinfectant depletion. Due to the difficulties in identifying contaminant types and chemical and biological properties, it is a challenging task to use EPANET-MSX to simulate chlorine decay under contaminant attack. EPANET 2.0 is used in the study to accomplish this goal. However, EPANET 2.0 cannot directly simulate chlorine depletion in the event of contamination attack because it assigns one time-independent bulk reaction coefficient to one specific pipe during the simulation. While under contaminant intrusion, chlorine decay bulk coefficient is not a constant. Instead, it is a temporal and spatial variable. This study presents an innovative approach for simulating contaminant intrusion in water distribution systems using EPANET multiple times. The methodology has six general steps. First, test bulk reaction coefficients of contaminant matrix in chemical lab. The uniqueness of this study is that the contaminant matrix is studied as a whole. The investigations of chemical, biological properties of individual aqueous constituents are not needed. Second, assume the contaminants as nonreactive, using EPANET 2.0 to identify where, when and at what concentrations of the inert contaminants will pass by in the water network. Third, determine the number of chlorine residual simulations based on the results in step two. Fourth, use EPANET to simulate the chlorine residual in the water network without the occurrence of contamination. Fifth, assign contaminated bulk coefficients to contaminated pipes; use EPANET to simulate the chlorine residual in the pipe network. Lastly, the chlorine concentrations of the impacted moments of impacted junctions are replaced with the results calculated in step five. This methodology is demonstrated in the south Pinellas County water distribution system.

Considering the continuous design activities that are performed throughout the design projects, design students go through several stages of decision makings, and sometimes they experience problematic situations in between consecutive supervisory meetings. Revealing all experiences during the discussions with supervisors, thus communicating the ideas could be sometimes difficult. In order to provide a better guidance, it is also important for supervisors to understand students&rsquo / process in between these meetings. There are available tools used in the fields like education or health in order to monitor an individual&rsquo / s daily life in relation to the context (e.g. time, place, activity) and personal circumstances (e.g. emotions, feelings, ideas). These tools are developed based on experience sampling method (ESM), a research method focus on collecting self-reported data from participants in order to measure their daily life experiences, especially during a long period of time. Since the target group and experience has different characteristics for each context, design of experience sampling tools are also gaining importance to address these specific experience according to individuals&rsquo / needs and expectations. Aiming at assisting design students to do regular self-reporting on their experiences, this study presents a background research for designing experience sampling tools that would be used by students and supervisors to keep track of students&rsquo / experiences throughout design projects. In this sense, this study intends assisting students self-reporting activities, translate the main design requirements of experience sampling tools into the context of design projects, as well as revealing guidelines for the future implications of ESM tools in design education

This work considers the sampling design for detection problems.Firstly,we focus on studying the effect of signal shape on sampling design for Gaussian detection problem.We then investigate the sampling design for distributed detection problems and compare the performance with the single sensor context. We also propose a sampling design scheme for the cluster-based wireless sensor networks.The cluster head employs a linear combination fusion to reduce the dimension of the sampled observation.Mathematical verification and simulation result show that the performance loss caused by the dimensionality reduction is exceedingly small as compared with the benchmark scheme,which is the sampling scheme without dimensionality reduction.In particular,there is no performance loss when the identical sampling points are employed at all sensor nodes.

Doctor of Philosophy / Department of Statistics / Dallas E. Johnson / Over the years sampling and experimental design have developed independently with little mutual compatibility. However, many studies do (or should) involve both a sampling design and an experimental design. For example, a polluted site may be exhaustively partitioned into area plots, a random sample of plots selected, and the selected plots randomly assigned to three clean-up regimens. In this research the relationship between sampling design and experimental design is discussed and a basic review of each is given. An estimator that combines sampling and experimental design is presented and it's development explained. Properties of this estimator will be derived and some applications of the estimator will be examined. Finally, a simulation study comparing this estimator with the traditional estimator will be presented.

The presence of outlying anchor items is an issue faced by many testing agencies. The decision to retain or remove an item is a difficult one, especially when the content representation of the anchor set becomes questionable by item removal decisions. Additionally, the reason for the aberrancy is not always clear, and if the performance of the item has changed due to improvements in instruction, then removing the anchor item may not be appropriate and might produce misleading conclusions about the proficiency of the examinees. This study is conducted in two parts consisting of both a simulation and empirical data analysis. In these studies, the effect on examinee classification was investigated when the decision was made to remove or retain aberrant anchor items. Three methods of detection were explored; (1) delta plot, (2) IRT b-parameter plots, and (3) the RPU method. In the simulation study, degree of aberrancy was manipulated as well as the ability distribution of examinees and five aberrant item schemes were employed. In the empirical data analysis, archived statewide science achievement data that was suspected to possess differential opportunity to learn between administrations was re-analyzed using the various item parameter drift detection methods. The results for both the simulation and empirical data study provide support for eliminating the use of flagged items for linking assessments when a matrix-sampling design is used and a large number of items are used within that anchor. While neither the delta nor the IRT b-parameter plot methods produced results that would overwhelmingly support their use, it is recommended that both methods be employed in practice until further research is conducted for alternative methods, such as the RPU method since classification accuracy increases when such methods are employed and items are removed and most often, growth is not misrepresented by doing so.

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This diploma thesis is devoted to the sampling strategies in forestry. It describes their theoretical aspects and their applications on a real landscape. The sampling methods in forestry are of particular importance in forest inven- tory. The aim of sampling methods is to estimate population characteristics based on the knowledge of sample. Two basic approaches can be distinguished according to the size of population, we speak about discrete or continuous population. Several types of sampling designs and corresponding estimators of target values are described for both approaches. Besides estimates of po- pulation total or average, we mention the formulas for computing variance of these estimates and the methods for their estimation for different sampling designs. The thesis also contains the comparison of studied methods based on computer simulations.

Model reliability is important when groundwater models are used for evaluation of environmental impact and water resource management. Model attributes such as geohydrologic units and parameter values need to be quantified in order to obtain reliable results. A primary objective of sampling design for groundwater models is to increase the reliability of modelling results by selecting effective measurement locations and times. It is advantageous to employ simulation models to guide measurement strategies already in early investigation stages. Normally, optimal design is only possible when model attributes are known prior to constructing a design. This is not a meaningful requirement as the model attributes are the final result of the analysis and are not known beforehand. Thus, robust design methods are required that are effective for ranges of parameter values, measurement error types and for alternative conceptual models. Parameter sensitivity is the fundamental model property that is used in this thesis to create effective designs. For conceptual model uncertainty, large-scale sensitivity analysis is used to devise networks that capture sufficient information to determine which model best describes the system with a minimum of measurement points. In fixed conceptual models, effective parameter- and error-robust designs are based on criteria that minimise the size of the parameter covariance matrix (D-optimality). Optimal designs do not necessarily have observations with the highest parameter sensitivities because D-optimality reduces parameter estimation errors by balancing high sensitivity and low correlation between parameters. Ignoring correlation in sparse designs may result in considerably inefficient designs. Different measurement error assumptions may also give widely different optimal designs. Early stage design often involves simple homogenous models for which the design effectiveness may be seriously offset by significant aquifer heterogeneity. Simple automatic and manual methods are possible for design generation. While none of these guarantee globally optimal designs, they do generate designs that are more effective than those normally used for measurement programs. Effective designs are seldom intuitively obvious, indicating that this methodology is quite useful. A general benefit of this type of analysis, in addition to the actual generation of designs, is insight into the relative importance of model attributes and their relation to different measurement strategies.