Predicting water and energy consumption at high resolution over a short-term horizon is critical for water and energy resource management. Water and energy are shown to be closely interlinked in household consumption. However, hourly predictions are ...
Several preprocessing procedures are required for the classification of microplastics (MPs) in aquatic systems using spectroscopic analysis. Procedures such as oxidation, which are employed to remove natural organic matter (NOM) from MPs, can be time...
Modeling wastewater processes supports tasks such as process prediction, soft sensing, data analysis and computer assisted design of wastewater systems. Wastewater treatment processes are large, complex processes, with multiple controlling mechanisms...
Determining which substances on the global market could be classified as persistent, mobile and toxic (PMT) substances or very persistent, very mobile (vPvM) substances is essential to prevent or reduce drinking water contamination from them. This st...
Automated algae classification using machine learning is a more efficient and effective solution compared to manual classification, which can be tedious and time-consuming. However, the practical application of such a classification approach is restr...
Representing reality in a numerical model is complex. Conventionally, hydraulic models of water distribution networks are a tool for replicating water supply system behaviour through simulation by means of approximation of physical equations. A calib...
Real-time information on flooding extent, severity, and duration is necessary for effective metropolitan flood emergency management. Existing pluvial flood analysis methods are unable to simulate real-time regional flooding processes under spatiotemp...
This paper explores the use of 'conditional convolutional generative adversarial networks' (CDCGAN) for image-based leak detection and localization (LD&L) in water distribution networks (WDNs). The method employs pressure measurements and is based on...
Four different machine learning algorithms, including Decision Tree (DT), Random Forest (RF), Multivariable Linear Regression (MLR), Support Vector Regressions (SVR), and Gaussian Process Regressions (GPR), were applied to predict the performance of ...
Determination of coagulant dosage in water treatment is a time-consuming process involving nonlinear data relationships and numerous factors. This study provides a deep learning approach to determine coagulant dosage and/or the settled water turbidit...