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

用于快速时间序列预测和数据合成的时空深度学习

• 批准号: 548397-2019
• 负责人: Gharabaghi, BahramBG
• 金额: $ 10.93万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=753141
• 关键词: Spatio; Temporal; Deep; Learning; Rapid

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intensive and cannot provide results for localized requirements due to long run times and unavailable or out-of-date datasets.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.

有许多科学和工程领域需要进行时空分析，如用于预警的洪水风险预报，用于空气质量区域管理的空气污染预报，以及精确的草皮管理。目前的模型使用复杂的模型来估计必要的时空输入。虽然这些方法是成功的，但由于运行时间长以及数据集不可用或过时，这些方法计算密集，无法提供本地化需求的结果。缺乏准确和最新的模型数据集可能会极大地影响基于这些结果的结果和决策，特别是在紧急情况下。为了解决现有时空方法的局限性，该项目将开发新的基于深度学习的架构，以便能够在空间和时间上快速评估复杂和对时间敏感的管理问题。这项研究的结果将解决时间序列估计(TSE)模型通过更新/填补时间和空间输入中的数据缺口来快速构建更准确的预测估计所需的局部时空需求。由此产生的方法将为实时预报提供近乎即时的模型输入更新，利用遥感，整合来自分布式传感器、监测站和基于网络的资源等网络的测量结果。我们的目标是能够获取当前的图像和详细的地理空间数据集，并将它们转换为可用于准确模拟时空事件的格式，例如洪水和空气浓度随时间的扩散。这些事件直接受到地形、土地利用和当地自然特征的影响。通过提供本地区域的最新表示，模型输出将更加准确。这些新方法将提供近乎实时的信息，为应急人员、环境规划者和其他决策者提供支持。