Khandelwal, Paahuni, authorPallickara, Sangmi Lee, advisorPallickara, Shrideep, committee memberGhosh, Sudipto, committee memberAndales, Allan, committee member2025-06-022027-05-282025https://hdl.handle.net/10217/241028Spatiotemporally 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 digitaldoctoral dissertationsengCopyright 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.distributed learninghigh-resolution imagingtransfer learningGeoAIdeep neural networkscience-guided learningTextEmbargo expires: 05/28/2027.