Hydraulic Engineering Applications of Artificial Intelligence, Deep Learning, and Digital Twin Technology
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydraulics and Hydrodynamics".
Deadline for manuscript submissions: 10 February 2025 | Viewed by 84
Special Issue 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: dynamic structural analysis; vibration response analysis; machine learning; oblique photography; hydraulic engineering safety monitoring
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: structural engineering; safety engineering; civil engineering; artificial neural network; artificial intelligence; data mining
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,
With the continuous developments in intelligent hydraulic engineering, artificial intelligence and deep learning methods have been widely used in this field for automatic data perception, intelligent processing, storage, and analysis. The emergence of digital twin technology provides technical support for intelligent water conservancy project construction, covering functions ranging from forecasting to early warning, previewing, and preplanning and providing forward-looking, scientific, precise, and safe support for decision-making and management. Combined with traditional hydraulic engineering safety monitoring methods, such as geotechnical tests and numerical simulation, artificial intelligence algorithms, deep learning methods, and digital twin technology can help solve more complex problems, providing more accurate and professional intelligent analyses and ubiquitous services, which is of great theoretical significance and application value, ensuring project safety. Therefore, this Special Issue will focus on artificial intelligence, deep learning methods, and digital twin technology in hydraulic engineering construction. We would like to invite you to submit your research papers on suitable topics including but not limited to the following: hydraulic engineering information perception, intelligent processing methods of safety monitoring data, application of digital twin technology in hydraulic engineering, intelligent safety monitoring models, and systems of hydraulic engineering.
Dr. Chenfei Shao
Dr. Hao Gu
Dr. Yanxin Xu
Dr. Xiangnan Qin
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 100 words) can be sent to the Editorial Office for announcement on this website.
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-blind 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
- safety monitoring data preprocessing
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