AI Knowledge Graph Turns Scattered Levee Data Into a Flood-Safety Question Machine

America’s levees protect millions of people and trillions of dollars in property, yet the information about them has long been trapped in fragmented databases, spreadsheets, inspection reports, and agency silos. A new study published in Earth Science Informatics describes a hybrid Graph Retrieval-Augmented Generation (GraphRAG) framework that pulls these disparate levee datasets into a single, semantically consistent knowledge graph and lets users interrogate it in plain language. The work, led by Armita Davarpanah of Spelman College with colleagues at the National Geospatial-Intelligence Agency, reports a striking 95.16 percent exact-match accuracy on a purpose-built benchmark of ontology-aware levee questions, suggesting that the combination of formal ontologies, graph databases, and large language models may be mature enough for real-world infrastructure stewardship.

The core problem the researchers set out to solve is one that plagues nearly every large infrastructure domain: heterogeneity. Levee records are maintained by different organizations using different vocabularies, formats, and levels of detail. A levee system may be described one way in a federal database, another way in a state GIS layer, and yet another way in an engineering inspection report. This fragmentation limits interoperability, undermines semantic consistency, and ultimately hampers risk-informed decision-making. When flood managers cannot reliably connect a culvert record to the levee segment it penetrates, or trace a monitoring asset back to the system it serves, the consequences can be measured in degraded situational awareness during emergencies.

The team’s solution centers on transforming normalized levee datasets into what they call a Levee System Knowledge Graph, or LSKG. A knowledge graph represents information as entities, such as levee systems, segments, structural components, and monitoring assets, connected by meaningful relationships. Rather than treating records as isolated rows in a table, the graph encodes the actual structure of the world: this segment is part of that system, this component is located on that segment, this sensor monitors that structure. The researchers aligned their normalized datasets with the National Levee Database Ontology, abbreviated NLD-O, a formal schema that defines the classes and relationships relevant to levee infrastructure in the United States. Ontology alignment is what gives the graph its semantic spine, ensuring that the same concept is represented the same way regardless of which source dataset it came from.

The choice of ontology was not arbitrary. The National Levee Database, maintained by the U.S. Army Corps of Engineers, is the country’s authoritative repository of levee information, and the datasets analyzed in the study are publicly available from it. By anchoring the knowledge graph to an ontology derived from this national resource, the framework inherits a vocabulary that flood-risk professionals already use, which matters enormously for adoption. The authors drew on established ontology engineering practices, including the Common Core Ontologies suite and the Basic Formal Ontology, both of which provide rigorously specified mid-level frameworks for representing entities, processes, and their relationships. Tools such as Protégé, the long-standing ontology editor developed at Stanford, support this kind of formal knowledge modeling.

Building the graph, however, is only half the challenge. The real innovation lies in how the framework answers questions. The researchers constructed a graph-addressable semantic corpus and deployed a hybrid retrieval strategy that combines three complementary techniques. The first is TF-IDF vectorization, a classical information retrieval method that dates back to Karen Spärck Jones’s foundational 1972 work on term specificity. TF-IDF weighs terms by how frequently they appear in a document relative to how common they are across the whole corpus, allowing the system to find text passages that are statistically relevant to a query. The second technique is graph reasoning, which exploits the explicit structure of the knowledge graph itself, traversing relationships between entities to answer questions that no single document could answer alone. The third is large language model assistance, in which an LLM interprets the retrieved evidence and composes a natural-language response.

This hybrid architecture is significant because each component compensates for the weaknesses of the others. Pure vector retrieval can find text that looks relevant but miss answers that require multi-hop reasoning across the graph. Pure graph queries are precise but demand structured input that casual users cannot provide. Large language models are fluent but notoriously prone to hallucination when asked to reason about facts they have never seen. By grounding the LLM in retrieved evidence from both the semantic corpus and the graph, the framework follows the retrieval-augmented generation paradigm introduced by researchers at Facebook AI in 2020, while extending it with graph-native reasoning. Crucially, the design preserves traceability: every answer can be linked back to the specific infrastructure records that support it, a property that is essential in engineering contexts where accountability matters.

To evaluate the system rigorously, the authors went beyond simply demonstrating that a knowledge graph could be built. They introduced a quantitative benchmark of ontology-aware levee infrastructure questions spanning four task categories: entity retrieval, which tests whether the system can identify specific objects; attribute retrieval, which tests whether it can report properties of those objects; relationship reasoning, which tests whether it can navigate connections between entities; and aggregation tasks, which require counting, summing, or otherwise computing over sets of records. Performance was measured using Exact Match, which requires the response to match a reference answer precisely, and Contains Match, which accepts answers that contain the correct value, along with response latency.

The results were remarkable in their consistency. The framework achieved 95.16 percent on both Exact Match and Contains Match, indicating that when the system answered correctly, it did so with precision rather than vague approximation. For a domain where a misidentified levee segment or an incorrect culvert count could have safety implications, that level of exactness is the difference between a research demo and a decision-support tool. The benchmark itself is a contribution, since the authors note that evaluating GraphRAG systems on domain-specific, ontology-aware questions remains rare, and they explicitly call for broader validation using additional datasets and independent benchmarks.

The implications extend well beyond levees. Flood risk is escalating as climate change drives non-stationary hydrology, a shift famously summarized in a 2008 Science paper declaring that stationarity is dead in water management. Agencies including FEMA and the U.S. Army Corps of Engineers have emphasized that levee safety depends on integrating inspection records, geospatial data, and monitoring information, and recent research has explored UAV-based thermal imaging for seepage detection, InSAR satellite deformation monitoring, and machine learning for culvert inspection. All of these technologies generate yet more heterogeneous data, which makes a semantic integration layer arguably a prerequisite for any intelligent flood-risk governance. A knowledge graph that can absorb sensor observations, geospatial reasoning, and temporal information, as the authors envision in future extensions, could become the connective tissue for next-generation infrastructure intelligence.

The study also offers a template for how scientific domains broadly might tame their own data chaos. Geoscience knowledge graph efforts, from earthquake emergency support frameworks to large collaborative programs, have been converging on the same insight: large language models become dramatically more reliable when anchored to formally structured, domain-specific knowledge. The levee work demonstrates the full pipeline, from ontology development and data normalization through graph construction in a platform like Neo4j, to hybrid retrieval and benchmark evaluation. Supported by the National Geospatial-Intelligence Agency through the Oak Ridge Institute for Science and Education, and grounded in publicly available National Levee Database records, the framework shows that trustworthy, traceable, natural-language access to critical infrastructure knowledge is no longer a distant aspiration but an engineering achievement with a measured score of 95.16 percent to prove it.

Subject of Research: An ontology-driven GraphRAG framework integrating levee infrastructure data into a knowledge graph for semantic querying and flood-risk decision support

Article Title: Hybrid GraphRAG framework for semantic integration and querying of levee infrastructure data

Article References: Davarpanah, A., Scully, E. J., Lipscomb, M. R., & Morris, A. L. (2026). Hybrid GraphRAG framework for semantic integration and querying of levee infrastructure data. Earth Science Informatics, 19(11), Article 185. https://doi.org/10.1007/s12145-026-02234-3

Image Credits: AI Generated

DOI: 10.1007/s12145-026-02234-3

Keywords: levee infrastructure, knowledge graph, GraphRAG, ontology, large language models, flood risk, National Levee Database, semantic integration, information retrieval, TF-IDF, Neo4j, civil engineering