Long‐Term Prediction Model for Erosion‐Deposition Topographic Evolution in the Sanmenxia‐To‐Xiaolangdi Reach of the Yellow River Based on Deep Learning

Abstract Reservoirs are essential for global water management and energy regulation, but sedimentation threatens their longevity. This study investigates a 130 km section of the Yellow River between the Sanmenxia and Xiaolangdi dams, using deep learning to predict long‐term erosion and deposition patterns. From 2009 to 2023, we gathered water depth data from 56 sites (840 measurements) with unmanned survey boats and drone‐based LiDAR (Light Detection and Ranging), alongside flow and sediment records. After preprocessing, we evaluated three machine learning models: Convolutional Network for Multimodal Time Series (CNN‐MTS), Convolutional Transformer for Multimodal Time Series (CNN‐Transformer‐MTS), and Convolutional Bi‐LSTM for Multimodal Time Series (CNN‐BiLSTM‐MTS). The CNN‐BiLSTM‐MTS model excelled, achieving a mean absolute error (MAE) of 17.84 m, a coefficient of determination (R2) of 0.9916, and reducing errors by up to 26% compared to alternatives. Key drivers of sediment dynamics included sediment load, maximum sediment concentration, and maximum flow. Data from 2009 to 2023 showed elevation shifts from −0.21 m near the dam to +1.158 m at the reservoir's tail. Predictions for 2024 to 2050 suggest varied riverbed changes, with the Guxian Reservoir's operation in 2036 expanding elevation ranges from −0.625 to 0.875 m. These findings highlight deep learning's potential for efficient sediment management in reservoirs and offer insights for sustainable hydraulic engineering. However, uncertainties persist in scaling the model, improving data resolution, and coordinating across regions.