Study on Sediment Removal Method of Reservoir Based on Double Branch Convolution

In response to the limitations and low computational efficiency of one-dimensional water and sediment models in representing complex hydrological conditions, this paper proposes a dual branch convolution method based on deep learning. This method utilizes the ability of deep learning to extract data features and introduces a dual branch convolutional network to handle the non-stationary and nonlinear characteristics of noise and reservoir sediment transport data. This method combines permutation variant structure to preserve the original time series information, constructs a corresponding time series model, models and analyzes the changes in the outbound water and sediment sequence, and can more accurately predict the future trend of outbound sediment changes based on the current sequence changes. The experimental results show that the DCON model established in this paper has good predictive performance in monthly, bimonthly, seasonal, and semi-annual predictions, with determination coefficients of 0.891, 0.898, 0.921, and 0.931, respectively. The results can provide more reference schemes for personnel formulating reservoir scheduling plans. Although this study has shown good applicability in predicting sediment discharge, it has not been able to make timely predictions for some non-periodic events in reservoirs. Therefore, future research will gradually incorporate monitoring devices to obtain more comprehensive data, in order to further validate and expand the conclusions of this study.