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oai:mountainscholar.org:10217/2410282025-12-30T03:37:39Zcom_10217_100532com_10217_100000com_10217_100388com_10217_100303col_10217_182111col_10217_100389

Khandelwal, Paahuni, author Pallickara, Sangmi Lee, advisor Pallickara, Shrideep, committee member Ghosh, Sudipto, committee member Andales, Allan, committee member 2025-06-02T15:21:09Z 2027-05-28 2025 Khandelwal_colostate_0053A_18815.pdf https://hdl.handle.net/10217/241028 https://doi.org/10.25675/3.02551 Spatiotemporally evolving phenomena occur in epidemiology, atmospheric sciences, agriculture, and traffic management, among others. Models can be used to understand and inform decision-making in these settings. There has been a growth in both mechanistic and physics-informed methods to model phenomena. A challenge in such models is the need for extensive parametrization and calibration, which can be difficult for modeling phenomena at the continental scale. This has 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, low-precision satellite imagery. The research explores techniques to integrate scientific models and make use of diverse data sources, overcoming their disparities in precision, spatial coverage, and temporal resolution. We explore several methods to leverage scientific models and harness available data despite disparities in their precision, spatial scope, or temporal frequencies. We also regulate how the networks learn by designing custom multipart loss functions that combine traditional measures of accuracy alongside physics/domain-informed variability. As data volumes increase, there is a corresponding increase in the resource requirements – GPU, memory, disk, and network I/O – requirements for model training. To address scalability issues, we designed a framework that manages multi-dimensional data volumes, partitions data effectively, curtails modeling costs, and transfer learning schemes to improve the efficiency of model training workflows. By incorporating scientific knowledge into the learning process, this research addresses the challenges of limited data availability and the data-intensive nature of deep neural networks. The method generalize effectively, enabling the way for scalable and accurate models in data-scarce domains. born digital doctoral dissertations English eng Colorado State University. Libraries 2020- Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. Embargo expires: 05/28/2027. distributed learning high-resolution imaging transfer learning GeoAI deep neural network science-guided learning Scalable predictive modeling for spatiotemporally evolving phenomena Text 2027-05-28 2027-05-28 This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). Doctor of Philosophy (Ph.D.) Doctoral Computer Science Colorado State University embargo