Research on risk decision-making generation method for water conservancy project based on multimodal knowledge graph and large language model

Libo Yang, Yuan Li, Junhua Tan and Libo Mao

PLOS ONE, 2025, vol. 20, issue 8, 1-21

Abstract: Traditional knowledge graphs of water conservancy project risks have supported risk decision-making. However, they are constrained by limited data modalities and low accuracy in information extraction. A multimodal water conservancy project risk knowledge graph is proposed in this study, along with a synergistic strategy involving multimodal large language models Risk decision-making generation is facilitated through a multi-agent agentic retrieval-augmented generation framework. To enhance visual recognition, a DenseNet-based image classification model is improved by incorporating single-head self-attention and coordinate attention mechanisms. For textual data, risk entities such as locations, components, and events are extracted using a BERT-BiLSTM-CRF architecture. These extracted entities serve as the foundation for constructing the multimodal knowledge graph. To support generation, a multi-agent agentic retrieval-augmented generation mechanism is introduced. This mechanism enhances the reliability and interpretability of risk decision-making outputs. In experiments, the enhanced DenseNet model outperforms the original baseline in both precision and recall for image recognition tasks. In risk decision-making tasks, the proposed approach—combining a multimodal knowledge graph with a multi-agent agentic retrieval-augmented generation method—achieves strong performance on BERTScore and ROUGE-L metrics. This work presents a novel perspective for leveraging multimodal knowledge graphs in water conservancy project risk management.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0330258

DOI: 10.1371/journal.pone.0330258

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