Guest Editor(s)
Dr. Balraj Singh
Email: balrajzinder@gmail.com
Affiliation: Department of Civil Engineering, Panipat Institute of Engineering and Technology, Samalkha, India
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Research Interests: river hydraulics and morphodynamics; local scour around hydraulic structures; computational fluid dynamics (CFD) modeling of flow and sediment transport; experimental investigation of flow-structure interaction; data-driven modeling in hydraulic engineering; flood hydraulics and river training structures
Dr. Parveen Sihag
Email: parveensihag.ce@geu.ac.in
Affiliation: Department of Civil Engineering, Graphic Era deemed to be univesity, Dehradun, Uttarakhand, India
Homepage:
Research Interests: hydraulic structure; hydrology, infiltration process, water resources engineering, hydrological modelling, soft computing and machine learning applications, groundwater recharge, water quality assessment, and sustainable infrastructure
Summary
Water scarcity, climate variability, environmental degradation, and rapid urbanization are placing increasing demands on hydraulic infrastructure and water resource systems. At the same time, the growing complexity and nonlinear nature of modern hydraulic and environmental processes are exposing the limitations of conventional engineering approaches in areas such as flood forecasting, sediment transport, irrigation management, urban drainage, and hydraulic structure design. These challenges are driving the development of new methodologies capable of improving prediction, control, and operational efficiency across fluid-based water systems.
In this context, Artificial Intelligence (AI) has emerged as a powerful framework for advancing hydraulic and environmental engineering. Machine learning, deep learning, and intelligent optimization techniques are increasingly integrated with computational and experimental fluid dynamics to analyze large datasets, identify hidden flow patterns, accelerate simulations, and support real-time decision-making. Such approaches are enabling significant progress in the modeling of free-surface and multiphase flows, river and coastal hydrodynamics, erosion and scouring phenomena, groundwater systems, and smart water distribution networks.
This Special Issue aims to present recent advances at the intersection of Artificial Intelligence, fluid dynamics, and hydraulic engineering, with particular relevance to the interdisciplinary scope of Fluid Dynamics & Materials Processing. Emphasis is placed on studies that combine AI-driven methodologies with the fundamental principles of fluid mechanics, transport phenomena, and computational modeling to address complex problems in water-related natural and engineered systems.We are especially interested in studies that use AI to:
· Predict sediment movement and scour around structures
· Estimating the hydraulic performance of a hydraulic structure
· Aapproximation of water quality and quantity in water bodies
· Improve irrigation and drainage systems
· Optimize the design and performance of hydraulic structures
· Understanding of water-soil interaction
· Forecast floods using smart models
Keywords
artificial intelligence, hydraulic engineering, machine learning, deep learning, sediment transport and scour, hydraulic structure optimization, flood forecasting.
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