Flow over a side weir is one of the more complex flows to simulate in one-dimensional unsteady flow analysis. Various experiments have been applied, but no agreement is apparent in the literature about the best method. In this study, an Artificial Neural Network model has been used to extract a discharge equation for side weirs which accurately estimates overflow discharges. The proposed methodology gives the advantage of accounting for both the geometric and hydraulic characteristics of the overflow structure. The developed model is calibrated and validated using experimental data. Model calibration is achieved by using a Multi-Layer Perceptron (MLP), trained with the back-propagation algorithm. In order to highlight the advantage of the developed model over an existing model widely in use, the model's performance is evaluated according to three comparison criteria. The provided results clearly reflect the ability of the developed model to overcome the weakness of conventional models.

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Sediment transport processes at the sediment‐water interface are usually studied using flume tests. Due to technical limitations, extreme hydro‐sedimentary conditions are then rarely considered. Sediment profile imaging (SPI) is a widely used technique for mapping benthic habitat quality in soft sediments but several limitations exist that make the system ineffective for coarse or indurate sediment investigations and for transport processes studies. To address this problem, a modified system was designed to investigate these processes in situ, on a grain‐size scale, with high temporal resolution. A dynamic sediment profile imaging (DySPI) system was constructed with a new mode of penetration, an appropriate design and an imaging system based on high‐definition video recording. The supporting frame was instrumented with autonomous sensors to monitor boundary layer characteristics along with video observations. This system was deployed during spring tide on sediment characterized by a mixture of particle sizes dominated by coarse grains. Appropriate image processing allowed determination of the area of sediment entrained, movement threshold, size of moving particles, instantaneous transport rate and interface profile changes, in addition to usual SPI parameters. However, DySPI is a prototype and further development of the instrument and the image processing are possible to enlarge the scope presented in this study.