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 岩溶地形显示出与天坑相关的独特的地表和地下特征， 泉水和洞穴许多水文和水力特性与这些 岩溶地区地下水系统的特点使其成为人类重要的淡水资源 消费和生态完整性。不幸的是，这些特征也使得喀斯特 极易受到污染的含水层。特别是岩溶泉， 对输入降水、作为传输介质的岩溶含水层 并放电作为输出响应。岩溶集水区或泉水集水区是一种制图 由表面积和地下排水系统定义的单位，有助于春季 放电因此，能够确定这个集水区，并了解如何 岩溶环境水文条件影响污染物在泉水中的归宿和迁移， 建立有效的水源保护区（SWPA）， 地下水和公共卫生。然而，由于岩溶系统固有的复杂性， 对于CITT团队来说，（1）预测春季响应，（2）推断地下 特性，因为两者都需要应用 先进的计算技术，如大数据分析和机器学习（ML） 算法因此，建议的KC唐纳利外部奖项目将允许我 与Sean Griffin先生及其团队一起沿着学习核心概念和模型 地理空间人工智能，用于自动化对象检测、对象分类和要素 从遥感图像中提取。掌握ML算法的应用将允许 让我在我的研究数据中发现模式，并构建预测数学模型 利用这些发现。