Puerto Rico Testsite for Exploring Contamination Threats (PROTECT)

波多黎各探索污染威胁的试验场（PROTECT）

• 批准号: 10382038
• 负责人: Akram N Alshawabkeh
• 金额: $ 1.5万
• 依托单位: NORTHEASTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-21 至 2022-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10382038
• 关键词: Affect; Area; Artificial Intelligence; Award; Big Data Methods; Catchment Area; Characteristics; Classification; Computational Technique; Consumption; Data; Detection; Environment; Fresh Water; Human Resources; Imagery; Modeling; Output; Pattern; Precipitation; Public Health; Puerto Rico; Research; Research Personnel; Source; Surface; Surface Properties; System; Training; Water; externship; feature extraction; ground water; insight; machine learning algorithm; mathematical model; remote sensing; response; transmission process

Project Abstract Karst terrains show distinctive surface and subsurface features associated with sinkholes, springs, and caves. Many of the hydrological and hydraulic characteristics associated with these features make groundwater systems in karst areas a significant freshwater resource for human consumption and ecological integrity. Unfortunately, these characteristics also make karst aquifers highly vulnerable to contamination. Karst springs, specifically, provide researchers insight to the interaction between input precipitation, karst aquifers as the transmission medium, and discharge as the output response. A karst catchment area or spring catchment is a mapping unit defined by the surface area and the subsurface drain system that contributes to spring discharge. Therefore, being able to determine this catchment area, and understand how hydrologic conditions in karst environments affect contaminant fate and transport in springs, is crucial to establishing effective source water protection areas (SWPAs) to safeguard groundwater and public health. However, due to the inherent complexity of karst systems, it has been challenging for the PROTECT team to (1) forecast spring response, and (2) infer subsurface properties from surface features in karst environments, since both require the application of advanced computational techniques, such as big data analytics and machine learning (ML) algorithms. Hence, the proposed project for the KC Donnelly Externship award will allow me to train along with Mr. Sean Griffin and his team on the core concepts and models behind geospatial artificial intelligence to automate object detection, object classification, and feature extraction from remotely sensed imagery. Mastering the application of ML algorithms will allow me to discover patterns in my research data, and to construct predictive mathematical models using these discoveries.

项目摘要