42-24 Development of an AI Cross Sector Data Integration Tool for Ground Water Management Decision Making

Abstract:

Groundwater problem-solving is frequently slowed not by a lack of scientific knowledge, but by data fragmentation across government, industry, and academia that communicate poorly across institutions. This study presents an AI-powered data and research integration tool designed to close that gap by unifying institutional project archives with published literature into a single searchable resource, allowing practitioners to retrieve comparable precedent, prior project outcomes, and relevant research simultaneously rather than sequentially. To evaluate the tool's practical utility, it was tested against a real-world use case. The tool was tasked with siting a recharge structure in a high-energy, densely vegetated intermittent stream reach within the Arbuckle-Simpson aquifer. The aquifer is a karst system with known regulatory complexity from municipal, county, state and federal oversight. Rather than returning a single design output, the tool synthesized considerations across hydrogeology, regulatory context, and stakeholder jurisdiction simultaneously. The tool correctly identifying the site's karst-specific flow behavior, relevant permitting authority, and data gaps. Specifically, the tool highlighted the absence of reach-scale conduit mapping needed to site the structure with confidence. This outcome illustrates the tool's core value not as a design generator, but as a rapid cross-disciplinary synthesis layer, surfacing relevant precedent, regulatory context, and research gaps that would otherwise require separately consulting hydrogeologic, legal, and institutional sources. These findings suggest a path toward reducing fragmentation in groundwater problem-solving. AI-assisted retrieval tools offer a practical, near-term mechanism at the project level, and policy-driven data infrastructure offering a complementary, longer-term path across institutions. 

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