Hydraulic Engineering Applications of Artificial Intelligence, Deep Learning, and Digital Twin Technology, 2nd Edition
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydraulics and Hydrodynamics".
Deadline for manuscript submissions: 20 August 2026 | Viewed by 1009
Editors
Interests: smart dam construction; digital twin technology; dam safety monitoring; hydraulic structure; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: hydraulic structural health monitoring; machine learning; health diagnosis of hydraulic structures; smart hydraulic engineering; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: dam safety monitoring; statistical modelling; feature selection; intelligence algorithm; oblique photography; numerical simulation
Special Issues, Collections and Topics in MDPI journals
Interests: dynamic structural analysis; vibration response analysis; machine learning; oblique photography; hydraulic engineering safety monitoring
Special Issues, Collections and Topics in MDPI journals
Interests: structural engineering; dam safety monitoring; civil engineering; deep learning; digital twin technology
Interests: structural engineering; safety engineering; civil engineering; artificial neural network; artificial intelligence; data mining
Special Issues, Collections and Topics in MDPI journals
Interests: CFD simulation; numerical simulation; computational fluid dynamics; me-chanical engineering; waste to energy; intelligent water conservancy
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The development of intelligent hydraulic engineering has led to the widespread use of artificial intelligence (AI) and deep learning for automatic data perception, processing, and analysis. Notably, advances in sensor and measurement technologies, combined with deep learning, enhance the fusion of multi-source heterogeneous information, which significantly improves data efficiency. Digital twin technology is also essential in modern water conservation projects. It enables forecasting, early warning, simulation, and planning, providing precise support for management decisions. Furthermore, Knowledge Graphs and Graph Neural Networks (GNNs) offer powerful tools for structural health diagnosis, safety evaluation, and anomaly identification. By integrating these computational methods with traditional geotechnical tests and numerical simulations, complex engineering problems are effectively addressed. This hybrid approach is highly valuable for ensuring the long-term safety of hydraulic projects. Therefore, this Special Issue will focus on exploring the intersection of AI, deep learning, and digital twin technologies in hydraulic engineering construction. We invite you to submit your research papers to this Special Issue. Suitable topics include, but are not limited to, the following: hydraulic engineering information perception, the fusion of multi-source heterogeneous information, intelligent processing methods for safety monitoring data, the application of digital twin technology in hydraulic engineering, the application of Knowledge Graphs in engineering safety evaluation, intelligent safety monitoring models for hydraulic engineering, and digital twin platforms for hydraulic structures.
Best regards,
Dr. Chenfei Shao
Dr. Hao Gu
Dr. Xiangnan Qin
Dr. Yanxin Xu
Dr. Dongyang Yuan
Dr. Yating Hu
Dr. Huixiang Chen
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- artificial intelligence
- deep learning
- digital twin technology
- safety monitoring
- hydraulic engineering
- data preprocessing
- knowledge graphs
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