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To effectively operate and manage the complex wastewater treatment system, simplified representations, known as wastewater modeling, are critical. Wastewater modeling allows for the understanding, monitoring, and prediction of wastewater treatment processes by capturing intricate relationships within the system. Process-driven models (PDMs), which rely on a set of interconnected hypotheses and assumptions, are commonly used to capture the physical, chemical, and biological mechanisms of wastewater treatment. More recently, with the development of advanced algorithms and sensor techniques, data-driven models (DDMs) that are based on analyzing the data about a system, specifically finding relationships between the system state variables without relying on explicit knowledge of the system, have emerged as a complementary alternative. However, both PDMs and DDMs suffer from their limitations. For example, uncertainties of PDMs can arise from imprecise calibration of empirical parameters and natural process variability. Applications of DDMs are limited to certain objectives because of a lack of high-quality dataset and struggling to capture changing relationship. Therefore, this dissertation aims to enhance the stable operation and effective management of WWTPs by addressing these limitations through the pursuit of three objectives: (1) investigating an efficient data-driven approach for uncertainty analysis of process-driven secondary settling tank models; (2) developing data-driven models that can leverage sparse and imbalanced data for the prediction of emerging contaminant removal; (3) exploring an advanced data-driven model for influent flow rate predictions during the COVID-19 emergency. / Thesis / Doctor of Philosophy (PhD) / Ensuring appropriate treatment and recycling of wastewater is vital to sustain life. Wastewater treatment plants (WWTPs), which have complicated processes that include several intricate physical, chemical, and biological procedures, play a significant role in the water recycling. Due to stricter regulations and complex wastewater composition, the wastewater treatment system has become increasingly complex. Therefore, it is crucial to use simplified versions of the system, known as wastewater modeling, to effectively operate and manage the complex system. The aim of this thesis is to develop data-driven approaches for wastewater modeling.

Open government data, concerned with the opening and publishing of government data in a free, accessible, and machine-readable format, aims to encourage public participation in government affairs, increasing government transparency and accountability. It is also posited that open government data will inspire businesses, the public and government agencies to use it and contribute to economic growth and value creation. The Canadian federal, provincial, and local governments have been actively opening and releasing open datasets about multiple subjects of interest to the public. However, evidence of the benefits of using open government datasets by Canadian businesses is scant, with no empirical research undertaken in Canada to understand how the data are used and what value is being created. Based on a qualitative approach, this thesis focuses on the works and experiences of 17 professed open data intermediary firms in Canada. It aims to discover patterns and themes that provide insights into how open government data were used, the challenges facing open data intermediaries, the state of open government data, and the economic value created. The data collection is based on semi-structured interviews conducted virtually with the founder or company's executives. In addition, the findings highlight the key similarities and differences in the activities open data intermediaries performed and the importance of resources and capabilities in developing products/services that contribute to economic value creation. Finally, five critical challenges impacting the use of open government data are identified: awareness, quality of open government data, competencies of users, data standards, and value creation.

Automated machine learning (AutoML) has become a new trend which is the process of automating the complete pipeline from the raw dataset to the development of machine learning model. It not only can relief data scientists' works but also allows non-experts to finish the jobs without solid knowledge and understanding of statistical inference and machine learning. One limitation of AutoML framework is the data quality differs significantly batch by batch. Consequently, fitted model quality for some batches of data can be very poor due to distribution shift for some numerical predictors. In this dissertation, we develop an intelligent binning to resolve this problem. In addition, various regularized regression classifiers (RRCs) including Ridge, Lasso and Elastic Net regression have been tested to enhance model performance further after binning. We focus on the binary classification problem and have developed an AutoML framework using Python to handle the entire data preparation process including data partition and intelligent binning. This system has been tested extensively by simulations and real datasets analyses and the results have shown that (1) All the models perform better with intelligent binding for both balanced and imbalance binary classification problem. (2) Regression-based methods are more sensitive than tree-based methods using intelligent binning. RRCs can work better than other tree methods by using intelligent binning technique. (3) Weighted RRC can obtain the best results compared to other methods. (4) Our framework is an effective and reliable tool to conduct AutoML.

Data representation tools are commonly used as means of understanding data. However, new ways of representing data such as using physical objects can have a different advantage as well. It is not only understanding the data, which is important, but giving meaning to data to inspire change. This field, called data physicalization, is still new, meaning that limited research exists about it which made us interested in exploring it further. We chose to do this by comparing a physicalization tool with a digital representation tool. We chose to limit the scope of our study to group collaboration and investigate the advantages and disadvantages of both tools from this perspective. We found this angle interesting since most major decisions require a group to work together and the representation tools used for assistance should encourage this. We investigated this by having focus groups where participants solved problems in a group using one representation tool at a time followed by individual interviews. We observed the behavior of the participants and compared it to the answers they gave in the interviews to uncover the main advantages and disadvantages of the data visualization and data physicalization tools. The biggest advantage uncovered by our study for data visualization is the ability to sort and filter data which makes it easier to understand the data. The biggest disadvantage is that only one person at a time has control over the mouse and thus the tool, creating a hierarchical group dynamic. The biggest advantage of the physicalization tool is its dynamic nature which enables the users to interact with the data thus supporting the understanding and exploration of ideas. One of the biggest disadvantages is that data physicalization is a new research field, which results in people needing time to understand how to use it. New data representation tools can be developed based on these advantages and disadvantages. / Datarepresentationsverktyg används vanligen som ett sätt att förstå data. Men nya sätt att representera data på, såsom att använda fysiska objekt, kan ha en ytterligare fördel. Det handlar inte bara om att förstå datan, utan att ge en bättre känsla för datan för att inspirera till förändring. Detta område, som kallas fysikalisering är fortfarande nytt vilket innebär att det finns begränsad forskning om det, vilket gjorde oss intresserade av att utforska det vidare. Vi valde att göra detta genom att jämföra ett fysikaliseringsverktyg med ett digitalt representationsverktyg. Vi valde att begränsa omfattningen av vår studie till samarbete i grupp och att undersöka fördelarna och nackdelarna med båda verktygen från detta perspektiv. Vi fann denna vinkel intressant eftersom de flesta stora beslut kräver att en grupp arbetar tillsammans och att representationsverktygen som används då bör stödja detta. Detta undersöktes genom att hålla i fokusgrupper där deltagarna löste problem i grupp med ett representationsverktyg åt gången, följt av individuella intervjuer. Vi observerade deltagarnas beteende och jämförde det med svaren de gav i intervjuerna för att hitta de största fördelarna och nackdelarna med visualiserings- och fysikaliseringsverktygen. Den största fördelen för datavisualisering som hittades under vår studie är förmågan att sortera och filtrera data, vilket gör det lättare att förstå datan. Den största nackdelen är att bara en person åt gången har kontroll över datormusen och därmed verktyget, vilket skapar en hierarkisk gruppdynamik. Den största fördelen med fysikaliseringsverktyget är dess dynamiska natur som möjliggör för användarna att interagera med datan och därigenom stödja förståelsen och utforskningen av idéer. En av de största nackdelarna är att fysikalisering är ett nytt forskningsområde, vilket innebär att människor behöver tid för att förstå hur man använder det. Baserat på dessa fördelar och nackdelar kan nya datarepresentationsverktyg kan utvecklas.

Plantilla para elaborar Tesis de Maestría en Data Science para optar el grado académico de Maestro en Data Science en el Programa de Maestría en Data Science. Escuela de Postgrado. Universidad Peruana de Ciencias Aplicadas

Understanding data on novel coronavirus (COVID-19) pandemic, and modeling such data over time are crucial for decision making at managing, fighting, and controlling the spread of this emerging disease. This thesis work looks at some aspects of exploratory analysis and modeling of COVID-19 data obtained from the Florida Department of Health (FDOH). In particular, the present work is devoted to data collection, preparation, description, and modeling of COVID-19 cases and deaths reported by FDOH between March 12, 2020, and April 30, 2021. For modeling data on both cases and deaths, this thesis utilized an autoregressive integrated moving average (ARIMA) times series model. The "IDENTIFY" statement of SAS PROC ARIMA suggests a few competing models with suggested values of the parameter p (the order of the Autoregressive model), d (the order of the differencing), and q (the order of the Moving Average model). All suggested models are then compared using AIC (Akaike Information Criterion), SBC (Schwarz Bayes Criterion), and MAE (Mean Absolute Error) values, and the best-fitting models are then chosen with smaller values of the above model comparison criteria. To evaluate the performance of the model selected under this modeling approach, the procedure is repeated using the first six month's data and forecasting the next 7 days data, nine month's data and forecasting the next 7 days data, and then all reported FDOH data from March 12, 2020, to April 30, 2021, and forecasting the future data. The findings of exploratory data analysis that suggests higher COVID-19 cases for females compared to males and higher male deaths compared to females are taken into consideration by evaluating the performance of final models by gender for both cases and deaths' data reported by FDOH. The gender-specific models appear to be better under model comparison criteria Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) compared to models based on gender aggregated data. It is observed that the fitted models reasonably predicted the future numbers of confirmed cases and deaths. Given similarities in reported COVID-19 data, the proposed modeling approach can be applied to data in the USA and many other States, and countries around the world.