Knowledge-driven intelligent recommendation method for emergency plans in water diversion projects
The emergency plans for water diversion projects suffer from weak knowledge correlation, inadequate timeliness, and insufficient support for intelligent decision-making. This study incorporates knowledge graph technology to enable intelligent recommendations for emergency plans in water diversion pr...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IWA Publishing
2023-11-01
|
| Series: | Journal of Hydroinformatics |
| Subjects: | |
| Online Access: | http://jhydro.iwaponline.com/content/25/6/2522 |
| _version_ | 1827615068670394368 |
|---|---|
| author | Lihu Wang Xuemei Liu Yang Liu Hairui Li Jiaqi Liu |
| author_facet | Lihu Wang Xuemei Liu Yang Liu Hairui Li Jiaqi Liu |
| author_sort | Lihu Wang |
| collection | DOAJ |
| description | The emergency plans for water diversion projects suffer from weak knowledge correlation, inadequate timeliness, and insufficient support for intelligent decision-making. This study incorporates knowledge graph technology to enable intelligent recommendations for emergency plans in water diversion projects. By employing pre-trained language models (PTMs) with entity masking, the model's ability to recognize domain-specific entities is enhanced. By leveraging matrix-based two-dimensional transformations and feature recombination, an interactive convolutional neural network (ICNN) is constructed to enhance the processing capability of complex relationships. By integrating PTM with ICNN, a PTM–ICNN method for joint extraction of emergency entity relationships is constructed. By utilizing the Neo4j graph database to store emergency entity relationships, an emergency knowledge graph is constructed. By employing the mutual information criterion, intelligent retrieval and recommendation of emergency plans are achieved. The results demonstrate that the proposed approach achieves high extraction accuracy (F1 score of 91.33%) and provides reliable recommendations for emergency plans. This study can significantly enhance the level of intelligent emergency management in water diversion projects, thereby mitigating the impact of unforeseen events on engineering safety.
HIGHLIGHTS
Pre-trained language models with entity masking to improve the ability of the models to recognize domain entities.;
Matrix-based two-dimensional transformation and feature reorganization to enhance the processing of complex relationships.;
Intelligent retrieval and recommendation of emergency plans based on mutual information criterion.; |
| first_indexed | 2024-03-09T09:04:51Z |
| format | Article |
| id | doaj.art-358a00010fe34a04882b83193355abee |
| institution | Directory Open Access Journal |
| issn | 1464-7141 1465-1734 |
| language | English |
| last_indexed | 2024-03-09T09:04:51Z |
| publishDate | 2023-11-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Journal of Hydroinformatics |
| spelling | doaj.art-358a00010fe34a04882b83193355abee2023-12-02T10:28:08ZengIWA PublishingJournal of Hydroinformatics1464-71411465-17342023-11-012562522254010.2166/hydro.2023.251251Knowledge-driven intelligent recommendation method for emergency plans in water diversion projectsLihu Wang0Xuemei Liu1Yang Liu2Hairui Li3Jiaqi Liu4 School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou, Henan 450046, China School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou, Henan 450046, China Collaborative Innovation Centre for Efficient Utilization of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou, Henan 450046, China School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou, Henan 450046, China Collaborative Innovation Centre for Efficient Utilization of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou, Henan 450046, China The emergency plans for water diversion projects suffer from weak knowledge correlation, inadequate timeliness, and insufficient support for intelligent decision-making. This study incorporates knowledge graph technology to enable intelligent recommendations for emergency plans in water diversion projects. By employing pre-trained language models (PTMs) with entity masking, the model's ability to recognize domain-specific entities is enhanced. By leveraging matrix-based two-dimensional transformations and feature recombination, an interactive convolutional neural network (ICNN) is constructed to enhance the processing capability of complex relationships. By integrating PTM with ICNN, a PTM–ICNN method for joint extraction of emergency entity relationships is constructed. By utilizing the Neo4j graph database to store emergency entity relationships, an emergency knowledge graph is constructed. By employing the mutual information criterion, intelligent retrieval and recommendation of emergency plans are achieved. The results demonstrate that the proposed approach achieves high extraction accuracy (F1 score of 91.33%) and provides reliable recommendations for emergency plans. This study can significantly enhance the level of intelligent emergency management in water diversion projects, thereby mitigating the impact of unforeseen events on engineering safety. HIGHLIGHTS Pre-trained language models with entity masking to improve the ability of the models to recognize domain entities.; Matrix-based two-dimensional transformation and feature reorganization to enhance the processing of complex relationships.; Intelligent retrieval and recommendation of emergency plans based on mutual information criterion.;http://jhydro.iwaponline.com/content/25/6/2522convolutional neural networkemergency planknowledge graphmutual information criterionwater diversion project |
| spellingShingle | Lihu Wang Xuemei Liu Yang Liu Hairui Li Jiaqi Liu Knowledge-driven intelligent recommendation method for emergency plans in water diversion projects Journal of Hydroinformatics convolutional neural network emergency plan knowledge graph mutual information criterion water diversion project |
| title | Knowledge-driven intelligent recommendation method for emergency plans in water diversion projects |
| title_full | Knowledge-driven intelligent recommendation method for emergency plans in water diversion projects |
| title_fullStr | Knowledge-driven intelligent recommendation method for emergency plans in water diversion projects |
| title_full_unstemmed | Knowledge-driven intelligent recommendation method for emergency plans in water diversion projects |
| title_short | Knowledge-driven intelligent recommendation method for emergency plans in water diversion projects |
| title_sort | knowledge driven intelligent recommendation method for emergency plans in water diversion projects |
| topic | convolutional neural network emergency plan knowledge graph mutual information criterion water diversion project |
| url | http://jhydro.iwaponline.com/content/25/6/2522 |
| work_keys_str_mv | AT lihuwang knowledgedrivenintelligentrecommendationmethodforemergencyplansinwaterdiversionprojects AT xuemeiliu knowledgedrivenintelligentrecommendationmethodforemergencyplansinwaterdiversionprojects AT yangliu knowledgedrivenintelligentrecommendationmethodforemergencyplansinwaterdiversionprojects AT hairuili knowledgedrivenintelligentrecommendationmethodforemergencyplansinwaterdiversionprojects AT jiaqiliu knowledgedrivenintelligentrecommendationmethodforemergencyplansinwaterdiversionprojects |