AccScience Publishing / AJWEP / Online First / DOI: 10.36922/AJWEP025330253
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ORIGINAL RESEARCH ARTICLE

Dam safety emergency response decision-making method based on a graph database and language model

Shilin Gong1,2* ,  Futing Sun2 ,  Keng Chen2
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1 Power China Huadong Engineering Corporation Limited, Hangzhou, Zhejiang, China
2 Large Dam Safety Supervision Center, National Energy Administration, Hangzhou, Zhejiang, China
AJWEP, 025330253 https://doi.org/10.36922/AJWEP025330253
Received: 13 August 2025 | Revised: 10 October 2025 | Accepted: 13 October 2025 | Published online: 3 November 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Risks and hidden dangers in dam safety management may occur suddenly, such as piping effects and dam overflow. Faced with these problems, several frontline dam managers are often at risk due to a lack of experience. To address this, a dam safety emergency response decision-making method based on a graph database and language model was proposed. First, a dam safety emergency knowledge system was constructed by identifying the potential components involved in the decision-making process. Relevant data were collected, organized, and stored according to this system, forming a knowledge graph of dam safety emergencies. Then, a Siamese Bidirectional Encoder Representations from Transformers Network was used to build a semantic matching model that effectively links dam safety emergency retrieval statements with corresponding cases in the graph database. A matching and sorting method was also developed to enable precise retrieval and intelligent recommendation of the most similar cases. The practical application of this method shows that it can effectively leverage professional expertise and typical cases in the dam safety domain, automatically providing decision support to dam operation safety management personnel through the integration of subgraphs and texts.

Keywords
Graph database
Language model
Dam
Emergency
Decision-making
Funding
This research was funded by the China Postdoctoral Science Foundation (2023M733315) and the National Key Research and Development Program of China (2021YFC3090101).
Conflict of interest
The authors declare that they have no competing interests.
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Asian Journal of Water, Environment and Pollution, Electronic ISSN: 1875-8568 Print ISSN: 0972-9860, Published by AccScience Publishing
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