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Recent Submissions

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    Emergency System Strain, Code Black Events, and Climate Change in Thunder Bay, Ontario: A Data-Driven Evaluation of Contributing Factors and Predictive Insights
    (University of Waterloo, 2026-07-29) Coady, Isabella
    Background: Code Black ambulance events, a blackout period in emergency medical services, occur when paramedics are unable to be dispatched on new calls. Typically, due to delays in transferring previous patients from paramedic care to hospital staff in the ED, Code Blacks pose a serious risk to public health and safety. At the Thunder Bay Regional Health Sciences Centre (TBRHSC) and Superior North EMS, Code Black events have become a common occurrence. This Master's thesis aims to investigate the underlying causes of Code Black events and EMS strain by analyzing ED and EMS data. It will also examine the influence of climate change and extreme weather conditions on Code Blacks. Research Question: The objective of this thesis is to understand the factors contributing to Code Black events and EMS strain in Thunder Bay, and how an exploratory analysis of ED, EMS, and climate data, along with machine learning methods, can be used to understand system overload. Methods: This retrospective observational study integrates perspectives from public health, emergency medicine, and climate change to address the research question. ED, EMS, and climate data will be used to identify patterns and predictive indicators of Code Black events at TBRHSC. A variety of methods will be employed to address the research question, including descriptive statistical analyses, data visualization techniques, and machine learning approaches. Expected Outcomes: The goal of this thesis is to identify the factors contributing to Code Black events and system strain to develop predictive insights that support equitable and resilient resource planning.
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    Biocultural Restoration in the Urban Green Commons
    (University of Waterloo, 2026-07-29) Kelly, Patrick
    Biodiversity and habitat loss, affective social-ecological disconnection, and a lack of community cohesiveness are complex, interwoven problems, partially stemming from widespread cultural shifts toward unsustainable values and practices. One proposed solution is biocultural restoration – a place-based, process-oriented approach to ecological restoration that explicitly builds local knowledge and ownership through holistic, land-based relational practices. However, there has been little research into its application in urban spaces, where increasing development pressures and concomitant ecological disconnection present new challenges and meaningful opportunities for the approach. To help address this gap, I explored community-led land co-management of neighbourhood parks and gardens in the City of Guelph, Ontario using a biocultural lens, with the expectation that an understanding of the facilitators of urban co-management practice and biocultural identity development could inform future urban biocultural restoration efforts. Ten interviews were completed June-August 2025 with neighbourhood park stewardship and garden coordinators, followed by reflexive thematic analysis. I found that many coordinators were overburdened with labour for their groups, which generally had low participation for collective maintenance and leadership responsibilities. Additionally, the establishment of a biocultural identity relied on practical and sensory experiences over declarative ecological knowledge. This establishment was complicated by negative sensory associations with the urban environment, like pollution. Though a potentially transformative practice in community green spaces, the results from this case study suggest that engagement, identity development, and potentially overburdening under-supported coordinators are challenges for urban biocultural restoration that require consideration.
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    Rethinking Deep Learning-Based Rainfall-runoff Modelling for Data-Rich Hydrology
    (University of Waterloo, 2026-07-28) Yu, Qiutong
    Sequential deep learning (DL) architectures, particularly Long Short-Term Memory (LSTM) networks, have become widely used in large-sample rainfall-runoff modelling. By training a single model across many basins using meteorological forcings and static basin attributes, these models can exploit cross-basin rainfall–runoff patterns and often achieve strong overall predictive performance. This modelling paradigm has developed alongside the increasing availability of large hydrological datasets. While previous DL-based hydrological studies mainly benefited from increasing the number of gauged basins for regional training, hydrological datasets have continued to expand the information available to DL models in other ways. Lake-river routing network products and catchment discretization provide finer spatial detail, long-term hydrometeorological archives extend historical coverage, and high-frequency forcing and streamflow records support sub-daily prediction. These developments create opportunities for DL-based rainfall–runoff modelling, but they also raise methodological questions about how expanded datasets should be represented, selected, and matched with suitable model architectures. This thesis investigates how DL-based rainfall-runoff modelling can be adapted to use hydrological datasets that provide finer spatial detail, longer historical coverage, and higher temporal resolutions, through three complementary studies on subbasin-scale modelling, recency-aware training, and model architecture for data-intensive settings. The first study examines how subbasin-scale spatial information can be incorporated into LSTM-based streamflow prediction. Regional LSTM models typically use basin-averaged forcings and attributes, treating each basin as a lumped response unit. This representation supports large-sample training, but it also reduces information on within-basin variability and drainage connectivity. To address this limitation, the study proposed the Spatially Recursive (SR) model, a hybrid framework that applies a regionally trained lumped LSTM at the subbasin scale and routes the resulting local streamflow predictions through a lake–river hydrological routing model. The method was evaluated in the Great Lakes region and compared with the original lumped LSTM. The SR model achieved comparable performance at trained locations and improved prediction in larger basins. These results show that lake–river routing network information and subbasin-level response units can help regional LSTM models use spatially detailed hydrological data more effectively. The second study investigates how long historical records should be used when many training basins are available. Reanalysis products and hydrometric archives provide decades of meteorological forcing and streamflow data, but the relevance of older observations depends on their similarity to the prediction period. Using hydrometeorological records from 1374 North American watersheds spanning 1950–2023, this study evaluates the effects of training-period length and data recency through backward-expanding, forward-expanding, and sliding-window experiments. The results show that recent records have greater value for LSTM training than distant historical records. Extending the training period with older data produced limited gains and sometimes reduced performance. The benefit of increasing the number of training watersheds also depended on data recency, especially for prediction in ungauged basins. Spatial diversity improved generalization most clearly when recent observations were included. Peak-flow analyses further showed that very short recent windows may provide insufficient exposure to rare high-flow events, indicating that recency-aware training still requires adequate temporal coverage. These findings suggest that large-sample training data should be selected by considering both spatial diversity and temporal relevance. The third study explores whether enhanced recurrent architecture can support large-sample hourly rainfall-runoff modelling. At hourly resolution, models must process longer input sequences and preserve relevant information across many more time steps than in daily modelling. To examine this issue, this study implemented MF-xLSTM, which combines a multi-frequency daily–hourly input structure with an xLSTM model backbone. The model was evaluated using hourly data from 430 Canadian basins and compared with LSTM-based benchmarks under the same multi-frequency input setting. MF-xLSTM achieved performance comparable to the LSTM benchmarks when predicting unseen periods and showed stronger overall accuracy in pseudo-ungauged basins. It also produced low-flow volume bias closer to zero, suggesting that xLSTM’s memory mechanisms may help retain information from earlier parts of long input sequences that are relevant to delayed storage release such as groundwater baseflow. These results indicate that xLSTM is a plausible model backbone for large-sample hourly rainfall–runoff modelling. Overall, this thesis demonstrates that the benefits of expanded hydrological datasets depend on how these data are used in model development. In a data-rich hydrological setting, simply adding more data does not necessarily lead to better generalization. Finer spatial data need to be represented in ways that preserve within-basin heterogeneity and drainage connectivity; long historical records need to be selected with attention to temporal relevance; and high-frequency observations require architectures capable of retaining information across longer sequences. This thesis provides evidence on how DL-based rainfall-runoff modelling can make more effective use of expanded hydrological datasets.
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    Characteristic words as fixed points of homomorphisms
    (University of Waterloo, 1991-12) Shallit, J. O.
    With each real number 0, 0 < 0 < 1, we can associate the so-called characteristic word w = w(0), defined by wn = [(n+1)0]-[n0], for n>1. We prove the following: if 0 has a purely periodic continued fraction expansion, then w(0) is a fixed point of a certain homomorphism y=y0.
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    Comparative stylistics in an integrated machine translation system
    (University of Waterloo, 1991-10) Mah, K.
    Comparative stylistics is a subfield of stylistics that attempts to account for the differences in style between languages. Rules of comparative stylistics are commonly presented, in textbooks of translation, as "rules-of-thumb", but if we hope to incorporate a knowledge of comparative stylistics into natural language understanding systems, we must take a more formal approach. In particular, we will develop a computational model of comparative stylistics for machine translation that could be used to guide translation and thereby improve the quality of the translated output. An implementation of this model would provide additional information to the machine translation system about the potential modulations to the translated text and their effects, enabling it to make a more informed decision. In this thesis, we develop a set of formal rules of syntactic French-English comparative stylistics to be used as a component of a model of comparative stylistics. As the foundation for the formal rules, we adapt theoretical rules of syntactic French-English comparative stylistics compiled by Guillemin-Flescher [1981] and the formal representation of syntactic style developed by DiMarco [1990]. A corpus of French sentences and their English translations is analyzed to convert the theoretical rues to a set of formal rules that builds on DiMarco's grammars of syntactic style. Thus, we present a formal grammar of French-English comparative stylistics. We also suggest a method for incorporating these rules into an existing machine translation system.