Influence Mechanism of Geomorphological Evolution in a Tidal Lagoon with Rising Sea Level

A tidal lagoon system has multiple environmental, societal, and economic implications. To investigate the mechanism of influence of the geomorphological evolution of a tidal lagoon, the effect of critical erosion shear stress, critical deposition shear stress, sediment settling velocity, and initial bed elevation were assessed by applying the MIKE hydro- and morpho-dynamic model to a typical tidal lagoon, Qilihai Lagoon. According to the simulation results, without sediment supply, an increase of critical erosion, deposition shear stress, or sediment settling velocity gives rise to tidal networks with a stable terrain. Such an equilibrium state can be defined as when the change of net erosion has little variation, which can be achieved due to counter actions between the erosion and deposition effect. Moreover, the influence of the initial bed elevation depends on the lowest tidal level. When the initial bed elevation is below the lowest tidal level, the tidal networks tend to be fully developed. A Spearman correlation analysis indicated that the geomorphological evolution is more sensitive to critical erosion or deposition shear stress than sediment settling velocity and initial bed elevation. Exponential sea level rise contributes to more intensive erosion than the linear or the parabolic sea level rise in the long-term evolution of a tidal lagoon.

Constant flux layers with gravitational settling: links to aerosols, fog and deposition velocities

Turbulent boundary layer concepts of constant flux layers and surface roughness lengths are extended to include aerosols and the effects of gravitational settling. Interactions between aerosols and the Earth's surface are represented via a roughness length for aerosol which will generally be different from the roughness lengths for momentum, heat or water vapour. Gravitational settling will impact vertical profiles and the surface deposition of aerosols, including fog droplets. Simple profile solutions are possible in neutral and stably stratified atmospheric surface boundary layers. These profiles can be used to predict deposition velocities and to illustrate the dependence of deposition velocity on reference height, friction velocity and gravitational settling velocity.

Application of Machine Learning Model for the Prediction of Settling Velocity of Fine Sediments

Sedimentation management is one of the primary factors in achieving sustainable development of water resources. However, due to difficulties in conducting in-situ tests, and the complex nature of fine sediments, it remains a challenging task when dealing with issues related to settling velocity. Hence, the machine learning model appears as a suitable tool to predict the settling velocity of fine sediments in water bodies. In this study, three different machine learning-based models, namely, the radial basis function neural network (RBFNN), back propagation neural network (BPNN), and self-organizing feature map (SOFM), were developed with four hydraulic parameters, including the inlet depth, particle size, and the relative x and y particle positions. The five distinct statistical measures, consisting of the root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), mean absolute error (MAE), mean value accounted for (MVAF), and total variance explained (TVE), were used to assess the performance of the models. The SOFM with the 25 × 25 Kohonen map had shown superior results with RMSE of 0.001307, NSE of 0.7170, MAE of 0.000647, MVAF of 101.25%, and TVE of 71.71%.

A dimensionless framework for predicting submarine fan morphology

Limited observations of active turbidity currents at field scales challenges the development of theory that links flow dynamics to the morphology of submarine fans. Here we offer a framework for predicting submarine fan morphologies by simplifying critical environmental forcings such as regional slopes and properties of sediments, through densimetric Froude (ratio of inertial to gravitational forces) and Rouse numbers (ratio of settling velocity of sediments to shear velocity) of turbidity currents. We leverage a depth-average process-based numerical model to simulate an array of submarine fans and measure rugosity as a proxy for their morphological complexity. We show a systematic increase in rugosity by either increasing the densimetric Froude number or decreasing the Rouse number of turbidity currents. These trends reflect gradients in the dynamics of channel migration on the fan surface and help discriminate submarine fans that effectively sequester organic carbon rich mud in deep ocean strata.

Tailings Settlement Velocity Identification Based on Unsupervised Learning

In order to reasonably and accurately acquire the settlement interface and velocity of tailings, an identification model of tailing settlement velocity, based on gray images of the settlement process and unsupervised learning, is constructed. Unsupervised learning is used to classify stabilized tailing mortar, and the gray value range of overflow water is determined. Through the identification of overflow water in the settlement process, the interface can be determined, and the settlement velocity of tailings can be calculated. Taking the tailings from a copper mine as an example, the identification of tailings settling velocity was determined. The results show that the identification model of tailing settlement speed based on unsupervised learning can identify the settlement interface, which cannot be manually determined in the initial stage of settlement, effectively avoiding the subjectivity and randomness of manual identification, and provide a more scientific and accurate judgment. For interfaces that can be manually recognized, the model has high recognition accuracy, has a rapid and efficient recognition process, and the relative error can be controlled within 3%. It can be used as a new technology for measuring the settling velocity of tailings.