Sediment transport control and river geometry prediction: AI-Driven model

The process of sediment movement significantly affects the development of river structures and regulates reservoir operational functions. The accumulation of extreme sediment items diminishes both reservoir capacity and increases operational challenges for hydroelectric facilities and irrigation systems while causing elevated flood-related dangers. In this present study the authors present a feedback control system based on Artificial Intelligence which predicts river geometry and controls sediment transport. This research analyzes three river areas with actual sedimentation issues i.e. Indus River Basin (Pakistan), Nile River Basin (Egypt), and Tigris-Euphrates System (Iraq/Turkey). An optimized sediment transport control system is developed by the combination of AI-driven modeling, hydrological simulations, GIS-based geospatial analysis and real-time data monitoring according to this research study. Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM) Networks and Random Forest Regression were used as AI models. Then pre and post conditions of AI implementation were evaluated in terms of sediment load, sediment control, water saving, etc. Deep learning model LSTM delivers the most successful results for sediment predictions through its R² score reaching 0.94. - Optimized AI-based flushing schedules decreased reservoir sedimentation rates on average by 17.7 percent. AI-based flushing schedules cut water consumption by 18.3% on average which enhances water preservation initiatives.