Applications of Machine Learning in Hydraulic Engineering
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydraulics and Hydrodynamics".
Deadline for manuscript submissions: closed (30 August 2023) | Viewed by 287
Special Issue Editors
Interests: hydrology; hydraulics; water resources engineering; hydroinformatics; SPH
Special Issue Information
Dear Colleagues,
Machine learning, as a branch of artificial intelligence, seeks to solve a variety of problems, such as classification, regression, clustering, and pattern recognition, without the need for massive quantities of data, boundary conditions, and initial conditions. The advancement of artificial intelligence methods has resulted in machine learning becoming an increasingly powerful tool for solving hydraulic and hydrodynamic problems. However, in order to solve these types of problems in a laboratory or numerical way, it is of course necessary to construct laboratory or numerical models. The process of changing laboratory models to investigate new conditions involves considerable time and expense, especially when there is a large number of test cases to be examined. Thanks to machine learning, water engineering problems can be solved without the need for extensive laboratory studies and numerical modeling, which will result in time and cost savings. This Special Issue aims to publish research related to the application of machine learning techniques in hydraulic and hydrodynamic problems. As such, we invite scholars in the field to submit research discussing the application of a variety of machine learning methods, including artificial neural networks, tree-based algorithms, kernel-based algorithms, ensemble methods, deep learning, and explainable machine learning.
Dr. Saeed Farzin
Prof. Dr. Seyed Farhad Mousavi
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- deep learning
- artificial intelligence
- hydraulic
- hydrodynamic
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