Spatio-Temporal Deep Learning for Rapid Time-Series Forecasting and Data Synthesis

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There are many scientific and engineering fields that require spatio-temporal analysis such as flood risk forecasting for early warning, air pollution forecasting for air quality zone management, and precision sod management. Current models employ complex models to estimate necessary spatio-temporal inputs. Albeit successful, these methods are computationally intensive and cannot provide results for localized requirements due to long run times and unavailable or out-of-date datasets. The lack of accurate and up to date datasets for models can greatly impact results and decisions based on these results, especially in emergency management situations. To address the limitations of existing spatio-temporal methods, this project will develop new deep learning-based architectures to allow for the rapid evaluation of complex and time-sensitive management problems in space and time. The results of this research will address the local spatio-temporal requirements that time-series estimation (TSE) models need by updating/filling data gaps in temporal and spatial inputs to rapidly construct more accurate forecast estimates. The resulting methodology will provide near-instantaneous model input updates for real-time forecasts, employing remote sensing, integrating measurements from networks of distributed sensors, monitoring stations, and web-based resources, among others. Our goal is to be able to take current imagery and detailed geospatial datasets and convert them into formats that can be used to accurately model spatio-temporal events such as flooding and air concentration dispersion over time. These events are impacted directly by topography, land use, and local physical features. By providing the most up to date representation of the local area, the more accurate the model outputs will be. These new methodologies will provide near-real-time information to support emergency responders, environmental planners, and otherdecision-makers.