Modeling sediment flow analysis for hydro-electric projects using deep neural networks

This study investigates sediment removal efficiency in river systems for optimizing hydroelectric projects using a novel Deep Neural Network (DNN) model tailored for river basin sediment transport. Aimed at evaluating sedimentation dynamics under various discharge conditions, the research explores sediment removal efficiency over extended run times (0–180 h) across discharge rates of 250 to 500 cumec. The DNN model employs river discharge and sediment concentration values as key inputs to predict sediment removal efficiency, capturing the complex interplay between discharge conditions and sediment transport patterns. Differential Evolution Optimization was applied to refine operational parameters, achieving an optimal discharge rate of 374.40 cumec and a sediment concentration of 2497.61 ppm, ensuring efficient operation while minimizing turbine wear due to sediment deposition. The model also identified optimal conditions for the diversion tunnel, set at 130 cumec and 2714.87 ppm, sustained for a runtime of 97.12 h to maintain a balanced sediment flow. Performance evaluation metrics, including R-squared (99.49%), MSE (0.1847), RMSE (0.4297), and MAE (0.2409), indicate superior model accuracy and predictive reliability. Additionally, Taylor diagrams were used to validate the model’s generalization capability across training and testing phases. This research highlights the DNN model’s potential in sedimentation forecasting, contributing to river management and environmental conservation, and suggests future integration with environmental variables for enhanced predictive capacity. The study demonstrates significant advancements in data-driven sediment analysis and underscores the critical role of optimization for sustainable hydroelectric operation.