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 喀斯特地形显示与下沉洞相关的独特表面和地下特征， 泉水和洞穴。与这些有关的许多水文和液压特征 功能使喀斯特地区的地下水系统成为人类的重要淡水资源 消费和生态完整性。不幸的是，这些特征也使得 含水层极易受到污染。特别是喀斯特弹簧为研究人员提供 深入了解输入降水之间的相互作用，喀斯特含水层作为传输介质， 作为输出响应，放电。喀斯特集水区或春季集水区是映射 由表面积和地下排水系统定义的单位 释放。因此，能够确定该集水区，并了解如何 喀斯特环境中的水文条件影响污染物的命运和弹簧的运输，是 对于建立有效的源防水区（SWPA）至关重要 地下水和公共卫生。但是，由于喀斯特系统的固有复杂性，它具有 保护团队（1）预测春季响应和（2）推断地下方面的挑战 来自喀斯特环境中表面特征的属性，因为两者都需要应用 高级计算技术，例如大数据分析和机器学习（ML） 算法。因此，KC Donnelly实习奖的拟议项目将使我能够 与肖恩·格里芬（Sean Griffin）及其团队一起训练核心概念和模型 地理空间人工智能自动化对象检测，对象分类和功能 从远程感知的图像中提取。掌握ML算法的应用将允许 我在研究数据中发现模式，并构建预测性数学模型 使用这些发现。