yes / River morphological dynamics are complex phenomena in natural and environmental flows. In particular, the sediment transport around braid mid-channel bars has not gained enough understanding from previous research. The effect of submergence ratio on the turbulence behavior in the proximity of the bar has been investigated in this study. The spatial distribution of turbulent flow in the proximity of bar has been studied by plotting the depth-averaged two-dimensional contours of turbulent kinetic energy. The high value of TKE has been observed in regions just downstream from the bar. It is due to the vortex shedding occurring in that region. The interaction of sweep and ejection events have been analyzed using the parameter Dominance Function obtained from the ratio of occurrence probability of ejection events to the occurrence probability of sweep events. This outcome indicates that the depth averaged parameter Dominance Function has successfully predicted the high scouring region which makes it an ideal parameter for analyzing the scour phenomena in real-world water management projects. The high scouring zone lies in the close proximity of the bar. This shows that the scouring effect from the bar is limited to its close region. The magnitude of scouring occurring at the upstream region of the bar also increases with the increment of submergence ratio. The relationship of quadrant event inclination angles with the sediment transport occurring in the proximity of bar has been also studied, where an Angle Ratio parameter has been utilized for linking the bed elevation change with the inclination angle. The results indicate that the AR parameter has been successfully tested in this study to show its competence to represent the turbulent burst-induced bed sedimentation and scouring. / The author has confirmed that no changes were made to the content of this proof on publication, although the paper is watermarked uncorrected proof.

Levees provide protection for vast amounts of commercial and residential properties. However, these structures degrade over time, due to the impact of severe weather, sand boils, subsidence of land, seepage, etc. In this research, we focus on detecting sand boils. Sand boils occur when water under pressure wells up to the surface through a bed of sand. These make levees especially vulnerable. Object detection is a good approach to confirm the presence of sand boils from satellite or drone imagery, which can be utilized to assist in the automated levee monitoring methodology. Since sand boils have distinct features, applying object detection algorithms to it can result in accurate detection. To the best of our knowledge, this research work is the first approach to detect sand boils from images. In this research, we compare some of the latest deep learning methods, Viola Jones algorithm, and other non-deep learning methods to determine the best performing one. We also train a Stacking-based machine learning method for the accurate prediction of sand boils. The accuracy of our robust model is 95.4%.

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