Deep recurrent neural networks for water hammer transient prediction and dynamic protection optimization in long distance pipelines

Water hammer phenomena pose significant threats to the operational safety and structural integrity of long-distance water transmission pipeline systems. This study develops an integrated intelligent system combining deep recurrent neural networks with distributed pressure sensor data fusion for water hammer transient prediction and dynamic protection optimization. A multi-layer bidirectional Long Short-Term Memory network with attention mechanism is constructed to capture spatial-temporal pressure dynamics from distributed sensor measurements. A Deep Q-Network based reinforcement learning algorithm generates optimal real-time protection strategies by coordinating multiple devices including surge tanks, relief valves, and valve closure sequences. Comprehensive validation demonstrates that the proposed system achieves superior prediction accuracy compared to conventional methods and significantly reduces maximum transient pressures while shortening stabilization duration. The intelligent decision framework provides water utilities with an adaptive tool for enhancing pipeline safety, minimizing infrastructure damage risks, and optimizing protection resource allocation in complex hydraulic systems.