4,676 research outputs found
Flood dynamics derived from video remote sensing
Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models.
Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science
Advances in machine learning algorithms for financial risk management
In this thesis, three novel machine learning techniques are introduced to address distinct
yet interrelated challenges involved in financial risk management tasks. These approaches
collectively offer a comprehensive strategy, beginning with the precise classification of credit
risks, advancing through the nuanced forecasting of financial asset volatility, and ending
with the strategic optimisation of financial asset portfolios.
Firstly, a Hybrid Dual-Resampling and Cost-Sensitive technique has been proposed to combat the prevalent issue of class imbalance in financial datasets, particularly in credit risk
assessment. The key process involves the creation of heuristically balanced datasets to effectively address the problem. It uses a resampling technique based on Gaussian mixture
modelling to generate a synthetic minority class from the minority class data and concurrently uses k-means clustering on the majority class. Feature selection is then performed
using the Extra Tree Ensemble technique. Subsequently, a cost-sensitive logistic regression
model is then applied to predict the probability of default using the heuristically balanced
datasets. The results underscore the effectiveness of our proposed technique, with superior
performance observed in comparison to other imbalanced preprocessing approaches. This
advancement in credit risk classification lays a solid foundation for understanding individual
financial behaviours, a crucial first step in the broader context of financial risk management.
Building on this foundation, the thesis then explores the forecasting of financial asset volatility, a critical aspect of understanding market dynamics. A novel model that combines a
Triple Discriminator Generative Adversarial Network with a continuous wavelet transform
is proposed. The proposed model has the ability to decompose volatility time series into
signal-like and noise-like frequency components, to allow the separate detection and monitoring of non-stationary volatility data. The network comprises of a wavelet transform
component consisting of continuous wavelet transforms and inverse wavelet transform components, an auto-encoder component made up of encoder and decoder networks, and a
Generative Adversarial Network consisting of triple Discriminator and Generator networks.
The proposed Generative Adversarial Network employs an ensemble of unsupervised loss derived from the Generative Adversarial Network component during training, supervised
loss and reconstruction loss as part of its framework. Data from nine financial assets are
employed to demonstrate the effectiveness of the proposed model. This approach not only
enhances our understanding of market fluctuations but also bridges the gap between individual credit risk assessment and macro-level market analysis.
Finally the thesis ends with a novel proposal of a novel technique or Portfolio optimisation. This involves the use of a model-free reinforcement learning strategy for portfolio
optimisation using historical Low, High, and Close prices of assets as input with weights of
assets as output. A deep Capsules Network is employed to simulate the investment strategy, which involves the reallocation of the different assets to maximise the expected return
on investment based on deep reinforcement learning. To provide more learning stability in
an online training process, a Markov Differential Sharpe Ratio reward function has been
proposed as the reinforcement learning objective function. Additionally, a Multi-Memory
Weight Reservoir has also been introduced to facilitate the learning process and optimisation of computed asset weights, helping to sequentially re-balance the portfolio throughout
a specified trading period. The use of the insights gained from volatility forecasting into
this strategy shows the interconnected nature of the financial markets. Comparative experiments with other models demonstrated that our proposed technique is capable of achieving
superior results based on risk-adjusted reward performance measures.
In a nut-shell, this thesis not only addresses individual challenges in financial risk management but it also incorporates them into a comprehensive framework; from enhancing the
accuracy of credit risk classification, through the improvement and understanding of market
volatility, to optimisation of investment strategies. These methodologies collectively show
the potential of the use of machine learning to improve financial risk management
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Breaking Virtual Barriers : Investigating Virtual Reality for Enhanced Educational Engagement
Virtual reality (VR) is an innovative technology that has regained popularity in recent years. In the field of education, VR has been introduced as a tool to enhance learning experiences. This thesis presents an exploration of how VR is used from the context of educators and learners. The research employed a mixed-methods approach, including surveying and interviewing educators, and conducting empirical studies to examine engagement, usability, and user behaviour within VR. The results revealed educators are interested in using VR for a wide range of scenarios, including thought exercises, virtual field trips, and simulations. However, they face several barriers to incorporating VR into their practice, such as cost, lack of training, and technical challenges. A subsequent study found that virtual reality can no longer be assumed to be more engaging than desktop equivalents. This empirical study showed that engagement levels were similar in both VR and non-VR environments, suggesting that the novelty effect of VR may be less pronounced than previously assumed. A study against a VR mind mapping artifact, VERITAS, demonstrated that complex interactions are possible on low-cost VR devices, making VR accessible to educators and students. The analysis of user behaviour within this VR artifact showed that quantifiable strategies emerge, contributing to the understanding of how to design for collaborative VR experiences. This thesis provides insights into how the end-users in the education space perceive and use VR. The findings suggest that while educators are interested in using VR, they face barriers to adoption. The research highlights the need to design VR experiences, with understanding of existing pedagogy, that are engaging with careful thought applied to complex interactions, particularly for collaborative experiences. This research contributes to the understanding of the potential of VR in education and provides recommendations for educators and designers to enhance learning experiences using VR
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
2023-2024 Catalog
The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation
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