Water quality prediction, a well-established field with broad implications across various sectors, is thoroughly examined in this comprehensive review. Through an exhaustive analysis of over 170 studies conducted in the last five years, we focus on the application of machine learning for predicting water quality. The review begins by presenting the latest methodologies for acquiring water quality data. Categorizing machine learning-based predictions for water quality into two primary segments—indicator prediction and water quality index prediction—further distinguishes between single-indicator and multi-indicator predictions. A meticulous examination of each method’s technical details follows. This article explores current cutting-edge research trends in machine learning algorithms, providing a technical perspective on their application in water quality prediction. It investigates the utilization of algorithms in predicting water quality and concludes by highlighting significant challenges and future research directions. Emphasis is placed on key areas such as hydrodynamic water quality coupling, effective data processing and acquisition, and mitigating model uncertainty. The paper provides a detailed perspective on the present state of application and the principal characteristics of emerging technologies in water quality prediction.

A three-dimensional numerical model of inclined turbulent jets with negatively buoyant discharge into stationary ambient water is presented in this paper to study certain jet parameters with turbulence schemes that have not been employed before in this context such as standard Boussinesq gradient diffusion hypothesis and general gradient diffusion hypothesis to account for the buoyancy-induced turbulence generation. Two jet discharge angles have been chosen for this study: 30° and 45° with the horizontal. These two angles are chosen in this study due to lower terminal rise heights for 30° and 45°, a fact which is critically important for discharges of effluent into shallow waters compared to higher angles than these values. The spatio-temporal jet evolutions for these cases have been modeled using OpenFOAM open-source CFD code, which is based on Finite-Volume Method. Results presented in this paper deal with the geometrical and flow properties of the inclined dense jets. The densimetric Froude number of the effluent at the nozzle ranges between 10 and 34. Two Reynolds-Averaged Navier–Stokes turbulence models are applied to evaluate the accuracy of the numerical predictions: the realizable k–ε (a two-equation model) and the Launder Reece Rodi (a Reynolds Stress Model – RSM).

The near-field flow and mixing properties of vertical buoyant jets subjected to lateral confinement are studied numerically for different cases, including different confinement indexes and jet densimetric Froude numbers. The performances of different turbulence models are investigated, such as the standard $k\text{-}ϵ$ turbulence model and buoyancy-modified $k\text{-}ϵ$ model. The modeled results are compared to previous and present experimental observations. The present paper confirms that the universally accepted model ($k\text{-}ϵ$ turbulence model) can be satisfactorily accurate, eliminating the need for an advanced modeling approach, as long as suitable modifications are performed. In contrast to previous studies, which used one single and constant value of $P_{tr}$ and $\mathsf{P}\mathsf{r}$ numbers, the present study links these two numbers to the $\mathsf{F}$ number, which is more practical and can produce very good results. This study also makes it possible to roughly quantify the rate at which the jet concentration spread width grows and identify the location where impingement occurs, which enables engineers or researchers to perform a quick estimation of the evolution and profile of a laterally confined vertical buoyant jet.

River bends are one of the common elements in most natural rivers, and secondary flow is one of the most important flow features in the bends. The secondary flow is perpendicular to the main flow and has a helical path moving towards the outer bank at the upper part of the river cross-section, and towards the inner bank at the lower part of the river cross-section. The secondary flow causes a redistribution in the main flow. Accordingly, this redistribution and sediment transport by the secondary flow may lead to the formation of a typical pattern of river bend profile. It is important to study and understand the flow pattern in order to predict the profile and the position of the bend in the river. However, there are a lack of comprehensive reviews on the advances in numerical modeling of bend secondary flow in the literature. Therefore, this study comprehensively reviews the fundamentals of secondary flow, the governing equations and boundary conditions for numerical simulations, and previous numerical studies on river bend flows. Most importantly, it reviews various numerical simulation strategies and performance of various turbulence models in simulating the flow in river bends and concludes that the main problem is finding the appropriate model for each case of turbulent flow. The present review summarizes the recent advances in numerical modeling of secondary flow and points out the key challenges, which can provide useful information for future studies.