Water bodies, both lotic and lentic systems, are vital to sustaining life on earth. To understand the behaviour of such systems, computational models are widely used by researchers and engineers. These models often introduce mathematical tools and approaches to simulate water flow and transport of substances in water bodies, statistical techniques to better understand the systems, and data-driven models to uncover complex relationships in the systems and predict their current and future status.
In the last decade, especially in response to the environmental threat due to climate change, substantial research has been carried out on each of the above-mentioned topics. In addition to the well-established grid-based methods for the simulation of water flow and transport of sediment and pollutants in rivers, lakes, coastal areas and oceans, the application of mesh-free methods such as Smoothed Particle Hydrodynamics has been extensively examined. Statistical methods and Artificial Intelligence have been employed for exploring and interpreting relationships in complex water systems such as clean water, urban drainage and wastewater systems. Specifically, Machine Learning has been used widely for predictive data analytics to help improve the management and operation of water infrastructure.
This Research Topic aims to provide a forum for communicating recent findings in the development and application of computational techniques for modelling the behaviour of natural and engineered water environments. This collection seeks contributions on computational results of original research studies which provide a new understanding of natural and man-made processes, at small or large scales. A broader outline of the scope includes the following.
• Numerical analysis of current and wave dynamics in rivers, coasts and oceans
• Multi-phase transport processes such as sediment and pollutant transport in water
• Numerical modelling of the interaction of water with impermeable and permeable structures
• Artificial Intelligence for classification, pattern recognition and prediction applications in water engineering (e.g. classification of water quality data and prediction of demand patterns in water distribution networks)
• Machine learning for assessment of the condition of natural and engineered water systems e.g. open channel hydraulics, water quality in rivers, flooding and blockage in sewer systems
• Statistical analysis and detection of failures in natural and engineered water systems, e.g. in urban water distribution networks through probabilistic modelling of pressure transients, etc.
• Mathematical models of sensing technologies for water asset management
Topic Editor, Prof. Vanessa L. Speight (University of Sheffield) received financial support from a number of UK water companies, UK Water Industry Research, and the UK water industry supply chain companies. All other Topic Editors declare no competing interests with regard to the Research Topic subject.
Water bodies, both lotic and lentic systems, are vital to sustaining life on earth. To understand the behaviour of such systems, computational models are widely used by researchers and engineers. These models often introduce mathematical tools and approaches to simulate water flow and transport of substances in water bodies, statistical techniques to better understand the systems, and data-driven models to uncover complex relationships in the systems and predict their current and future status.
In the last decade, especially in response to the environmental threat due to climate change, substantial research has been carried out on each of the above-mentioned topics. In addition to the well-established grid-based methods for the simulation of water flow and transport of sediment and pollutants in rivers, lakes, coastal areas and oceans, the application of mesh-free methods such as Smoothed Particle Hydrodynamics has been extensively examined. Statistical methods and Artificial Intelligence have been employed for exploring and interpreting relationships in complex water systems such as clean water, urban drainage and wastewater systems. Specifically, Machine Learning has been used widely for predictive data analytics to help improve the management and operation of water infrastructure.
This Research Topic aims to provide a forum for communicating recent findings in the development and application of computational techniques for modelling the behaviour of natural and engineered water environments. This collection seeks contributions on computational results of original research studies which provide a new understanding of natural and man-made processes, at small or large scales. A broader outline of the scope includes the following.
• Numerical analysis of current and wave dynamics in rivers, coasts and oceans
• Multi-phase transport processes such as sediment and pollutant transport in water
• Numerical modelling of the interaction of water with impermeable and permeable structures
• Artificial Intelligence for classification, pattern recognition and prediction applications in water engineering (e.g. classification of water quality data and prediction of demand patterns in water distribution networks)
• Machine learning for assessment of the condition of natural and engineered water systems e.g. open channel hydraulics, water quality in rivers, flooding and blockage in sewer systems
• Statistical analysis and detection of failures in natural and engineered water systems, e.g. in urban water distribution networks through probabilistic modelling of pressure transients, etc.
• Mathematical models of sensing technologies for water asset management
Topic Editor, Prof. Vanessa L. Speight (University of Sheffield) received financial support from a number of UK water companies, UK Water Industry Research, and the UK water industry supply chain companies. All other Topic Editors declare no competing interests with regard to the Research Topic subject.