Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 42 for “"Data-Scarce"”.
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Enhanced Soil Moisture and Streamflow Estimation in Ungauged or Data-Scarce Watersheds
… patterns and using satellite soil moisture data for model calibration in ungauged and data-scarce watersheds, especially for watersheds where variable source area runoff mechanism dominate. Chapter 2 proposes the topographic index (TI) as a tool to represent the spatial soil moisture …
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Fluvial and climatic controls on tropical agriculture and adaptation strategies in data-scarce contexts
… in contexts lacking reliable environmental data, where their low-quality and low representativeness weaken their reliability, compromising the reliability of the outcomes as well. This thesis seeks to respond to the increasing need of realistically addressing environmental phenomena that …
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MODELLING, ASSESSMENT, AND REDUCTION OF FLOOD RISK IN NEPAL
… risk assessment models that are applied for data-scarce flood-prone areas and where flood risk is quantified as economic damage. In addition, there is a lack of flood risk assessment models towards flood risk reduction in data-scarce flood-prone areas in Asia, including Nepal. Also, there is …
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Overcoming Data Scarcity in Deep Learning of Scientific Problems
Data-driven approaches such as machine learning have been increasingly applied to the natural sciences, e.g. for property prediction and optimization or material discovery. An essential criteria to ensure the success of such methods is the need for extensive amounts of labeled data, making it …
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The development of Intensity Duration Frequency curves under climate change considerations using Remote Sensed rainfall data in Uganda
… to hydrological infrastructure, particularly in data-scarce regions such as Uganda. Reliable intensity–duration–frequency (IDF) curves are essential for climate-resilient design, yet their development is constrained by the lack of long-term subdaily rainfall records. The challenge addressed in …
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Drone Imagery Applied to Enhance Flood Modeling
Accessible flood modeling for low-resource, data-scarce communities currently does not exist. This paper proposes using drone imagery to compensate for the lack of other flood modeling data (i.e. streamflow measurements). Three flood models were run for Dzaleka Refugee Camp, located in Dowa, …
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Specialization of Vision Representations with Personalized Synthetic Data
… vision tasks, which are both fine-grained and data-scarce. Recent works have successfully applied synthetic data to general-purpose representation learning, while advances in Text-to-Image (T2I) diffusion models have enabled the generation of personalized images from just a few real examples. …
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Learning without Labels - Reducing Supervision in Training, Inference, and Evaluation of Deep Neural Networks
… deploying deep neural networks in real-world, data-scarce, and open-ended settings.
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Trainable Pre-Filtering for Deep Neural Networks and Applications
… neural networks depend heavily on large labeled datasets and purely data-driven feature learning, which limits robustness, efficiency, and real-world deployability, especially in noisy, data-scarce, and resource-constrained environments. This dissertation proposes a unified framework that …
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Counting animals in ecological images
… analyzing large volumes of image, video or audio data and manual counting. Automating the process of counting animals would be invaluable to researchers as it will eliminate the tedious time-consuming task of counting. The purpose of this dissertation is to address manual counting in images by …
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Empirical Approaches to Challenges in Neural Network Training and Deployment
… to decide on what optimizer to use, the exact data to train on, the training curriculum, and many other factors-- all of which affect the final performance. Once the model is trained, we need to make sure that it is ready to be used and works as intended.This thesis aims to address these …
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Artificial Intelligence and Spatial Modeling to Estimate Traffic Volume Measures on Local Roadways
… (AADT) on local roadways, which are often data-scarce yet crucial for transportation planning and infrastructure development. Traditional traffic monitoring methods, such as permanent traffic count stations and short-term manual counts, are cost-prohibitive and fail to capture the …
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Investigating Compound Flood Using Remote Sensing And Hydrodynamic Modeling
… develops a framework to investigate CF in a data-scarce, cloudy region and creates probabilistic flood hazard maps using satellite imagery. The results conclude that the flood delineation algorithm developed in this study is simple (not cloud restricted), yet effective and comparable to …
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Training Physics-Guided Neural Networks with Multiple Constraints: An Application in Lake Ecology Modeling
… in modeling such systems, sparse environmental data often limits the ability of machine learn- ing models to produce physically consistent predictions or generalize to novel conditions. As a result, many existing approaches rely on computationally intensive physics-biogeochemical simulations to …
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Data-efficient Neural Appearance Manipulations
… This dissertation explores the potential of data-efficient learning-based techniques for manipulating three core aspects of appearance: fine details, transient attributes, and reflectance. It introduces two novel contributions: (1) an ML-based image map representation designed for fine detail …
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Scalable predictive modeling for spatiotemporally evolving phenomena
… occurred alongside the availability of diverse data that can be leveraged by model-fitting algorithms. This dissertation focuses on leveraging deep learning methods to model spatiotemporally evolving phenomena by combining sparse but high-precision in situ measurement data with voluminous, …
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Optimization methods for parameter identifications in settings with only partial knowledge
… speed of these algorithms on real and simulated datasets. Finally, both algorithms are implemented in an open-source python package pysr3. Conveniently, this package offers complete compatibility with scikit-learn, so all pysr3 models can be used in a pipeline with classic modeling blocks such as …
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Genomic Language Models for Protein Function and Property Prediction
… on enormous corpora of unlabeled sequence data have demonstrated state-of-the-art performance on a variety of downstream tasks. This approach is appealing because one model can be easily adapted to do well in many modalities, rather than requiring many specialized models. This same …
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Using machine learning to derive insights from sports location data
… of high-resolution event location and tracking data has led to many new opportunities in sports research. However, it is often challenging to apply machine learning to understand a particular aspect of a sport. These tasks typically require learning on high-dimensional data, scarce labels, and …
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