Influence of different skew angles of the submerged vane on pressure flushing performance.

Siltation significantly threatens a reservoir's original storage capacity and lifespan. Pressure flushing is an effective measure against siltation through the partial drawdown of the reservoir water level with limited flushed cone volumes in front of the bottom outlet. In this study, a novel configuration with submerged vanes has been proposed and tested experimentally to increase the flushed sediment volume during pressure flushing.

Application of classical and novel integrated machine learning models to predict sediment discharge during free-flow flushing.

In this study, the capabilities of classical and novel integrated machine learning models were investigated to predict sediment discharge (Q) in free-flow flushing. Developed models include Multivariate Linear Regression (MLR), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Group Method of Data Handling (GMDH), and four hybrid forms of GMDH and Support Vector Regression (SVR) in combination with Henry Gas Solubility Optimization (HGSO) and Equilibrium Optimizer (EO) algorithms. The number of 160 datasets that were applied to assess these models was extracted from the Janssen (PhD's Thesis, Engineering, Civil and Environmental Engineering.