<p>Streamflow prediction in ungauged basins (PUB) is a process generating streamflow time series at ungauged reaches in a river network. PUB is essential for facilitating various engineering tasks such as managing stormwater, water resources, and water-related environmental impacts. Machine Learning (ML) has emerged as a powerful tool for PUB using its generalization process to capture the streamflow generation processes from hydrological datasets (observations). ML’s generalization process is impacted by two major components: data splitting process of observations and the architecture design. To unveil the potential limitations of ML’s generalization process, this dissertation explores its robustness and associated uncertainty. More precisely, this dissertation has three objectives: (1) analyzing the potential uncertainty caused by the data splitting process for ML modeling, (2) investigating the improvement of ML models’ performance by incorporating hydrological processes within their architectures, and (3) identifying the potential biases in ML’s generalization process regarding the trend and periodicity of streamflow simulations.</p><p>The first objective of this dissertation is to assess the sensitivity and uncertainty caused by the regular data splitting process for ML modeling. The regular data splitting process in ML was initially designed for homogeneous and stationary datasets, but it may not be suitable for hydrological datasets in the context of PUB studies. Hydrological datasets usually consist of data collected from diverse watersheds with distinct streamflow generation regimes influenced by varying meteorological forcing and watershed characteristics. To address the potential inconsistency in the data splitting process, multiple data splitting scenarios are generated using the Monte Carlo method. The scenario with random data splitting results accounts for frequent covariate shift and tends to add uncertainty and biases to ML’s generalization process. The findings in this objective suggest the importance of avoiding the covariate shift during the data splitting process when developing ML models for PUB to enhance the robustness and reliability of ML’s performance.</p><p>The second objective of this dissertation is to investigate the improvement of ML models’ performance brought by Physics-Guided Architecture (PGA), which incorporates ML with the rainfall abstraction process. PGA is a theory-guided machine learning framework integrating conceptual tutors (CTs) with ML models. In this study, CTs correspond to rainfall abstractions estimated by Green-Ampt (GA) and SCS-CN models. Integrating the GA model’s CTs, which involves information on dynamic soil properties, into PGA models leads to better performance than a regular ML model. On the contrary, PGA models integrating the SCS-CN model's CTs yield no significant improvement of ML model’s performance. The results of this objective demonstrate that the ML’s generalization process can be improved by incorporating CTs involving dynamic soil properties.</p><p>The third objective of this dissertation is to explore the limitations of ML’s generalization process in capturing trend and periodicity for streamflow simulations. Trend and periodicity are essential components of streamflow time series, representing the long-term correlations and periodic patterns, respectively. When the ML models generate streamflow simulations, they tend to have relatively strong long-term periodic components, such as yearly and multiyear periodic patterns. In addition, compared to the observed streamflow data, the ML models display relatively weak short-term periodic components, such as daily and weekly periodic patterns. As a result, the ML’s generalization process may struggle to capture the short-term periodic patterns in the streamflow simulations. The biases in ML’s generalization process emphasize the demands for external knowledge to improve the representation of the short-term periodic components in simulating streamflow.</p>

The aim of this thesis is to review the current state of machine learning in music composition and to train a computer on Beatles' songs using research project Magenta from the Google Brain Team to produce its own music. In order to explore the qualities of the generated music more thoroughly, we restrict our- selves to monophonic melodies only. We train three deep learning models with three different configurations (Basic, Lookback, and Attention) and compare generated results. Even though the generated music is not as interesting as the original Beatles, it is quite likable. According to our analysis based on musically informed metrics, generated melodies differ from the original ones especially in lengths of notes and in pitch differences between consecutive notes. Generated melodies tend to use shorter notes and higher pitch differences. In theoretical background, we cover the most commonly used machine learning algorithms, introduce neural networks and review related work of music generation. 1

This thesis examines the potential of Artificial Intelligence (AI) in improving the demand forecasting with in the supply chain. To be precise it undergoes a comparative study between traditional models like ARIMA and more advanced neural networks such as LSTM. The research conducts a robust methodological framework combining both qualitative and quantitative approaches for determining the forecast for a pharmacy store having historical sales data for 6 years (2014-2019). Through implementation of both the models and evaluation of results, underlines the strength and weakness of each model, offering realistic insights for the supply chain practitioners. Thorough literature review showcases the previous research done and future scope for implementation of AI for demand forecast in industry. The study not only contributes to academic disclosure but provides practical insight for the industry stakeholders to optimize their forecasting decisions in a modern world with dynamic consumer behavior and highly volatile demand. / Diese Arbeit untersucht das Potenzial der Künstlichen Intelligenz (KI) zur Verbesserung der Nachfrageprognose innerhalb der Lieferkette. Genauer gesagt, wird eine vergleichende Studie zwischen traditionellen Modellen wie ARIMA und fortschrittlicheren neuronalen Netzwerken wie LSTM durchgeführt. Die Forschung basiert auf einem robusten methodischen Rahmen, der sowohl qualitative als auch quantitative Ansätze kombiniert, um Prognosen für eine Apotheke mit historischen Verkaufsdaten über einen Zeitraum von sechs Jahren (2014–2019) zu erstellen. Durch die Implementierung beider Modelle und die Auswertung der Ergebnisse werden die Stärken und Schwächen jedes Modells hervorgehoben, wodurch praxisnahe Einblicke für Fachleute im Bereich der Lieferkette gewonnen werden. Eine umfassende Literaturrecherche gibt einen Überblick über bisherige Forschungsarbeiten und zeigt zukünftige Anwendungsmöglichkeiten von KI für die Nachfrageprognose in der Industrie auf. Die Studie trägt nicht nur zur wissenschaftlichen Diskussion bei, sondern bietet auch praktische Erkenntnisse für Branchenakteure, um ihre Prognoseentscheidungen in einer modernen Welt mit dynamischem Konsumverhalten und hoher Nachfragevolatilität zu optimieren.