Formatted Title
Leveraging Cloud and AI Technologies for Remediation Analytics and Digital Delivery within an Environmental Data Science Framework
Background/Objectives
The Jacobs Environmental Data Science Framework (EDSF) was initially developed to offer a unified digital delivery platform, integrating a wide array of data sources, automating analysis, and providing secure, real-time decision support for remediation stakeholders. With the expansion of cloud technologies and the advent of artificial intelligence (AI), Jacobs has advanced the EDSF by utilizing serverless computing, custom Application Programming Interfaces (APIs), and generative AI capabilities to elevate data democratization, analytics, and reporting for remediation clients. The expansion of the EDSF equips stakeholders with state-of-the-art tools for data-driven decision-making and greater efficiency in delivery.
Approach/Activities
The Jacobs EDSF offers flexible data storage, analysis, visualization, and reporting all powered by cloud technologies that empower stakeholders to adapt to the unique requirements of various remediation sites. Several recent applications are highlighted: 1) a custom API deployed to the cloud to leverage complex PFAS characterization analytics for groundwater remediation sites; 2) automated SCADA system data pipelines for remedial systems were constructed with serverless computing and delivered via API to near real-time dashboards used to monitor and report operations and maintenance activities for various remedial systems; 3) remote monitoring data from a network of 30 groundwater transducers was piped to a dashboard via API along with open-source rainfall and stage data from the United States Geological Survey to aid in surface water, groundwater interaction evaluation; 4) an interactive digital CSM was constructed using historical digital files and data that allows stakeholders to interactively view boring logs, cross-sections, well construction inventory as well as a full suite of environmental data; 5) AI used within the EDSF to perform Optical Character Recognition (OCR) to transpose handwritten boring logs to tabular data, and aid developers in writing code for remediation data analysis, app development, and automated narrative-based reporting.
Results/Lessons Learned
Cloud-based data access with the EDSF increases data democratization enabling remote collaboration for stakeholders while ensuring data privacy and security. Leveraging modular, serverless cloud-based API analytics enables the construction of adaptable custom solutions, leading to cost savings and expedited development, ultimately facilitating swift project decision support. The integration of cloud technologies and AI offers the environmental remediation consulting market the potential to expedite decision-making processes and empower data-driven insights, fostering more efficient and informed approaches to environmental challenges.