| Abstrakt: |
The prediction of sediment concentrations is of significant importance for watershed sediment control, water-sediment regulation, as well as water quality and environmental management. The upper reaches of the Yangtze River, vast area in size and abundant in tributaries with complex water and sediment sources, pose a great challenge to accurately predict the process of suspended sediment concentration (SSC) entering the Three Gorges Reservoir. In this study, we propose a deep learning model named RF-LSTM, which combines Random Forest (RF) algorithm and Long Short-Term Memory (LSTM) neural network for daily SSC prediction at Cuntan station. This model addresses the varying impacts of rainfall in the upper reaches of the Yangtze River, as well as the inflow of water and sediment from its main stream and tributaries, on the daily SSC observed at the station. Firstly, the RF algorithm is employed to identify water and sediment factors that exhibit a strong correlation with the SSC at Cuntan. These factors are then utilized as input variables for the LSTM neural network to discern the mapping relationship between the optimized set of factors and the SSC at Cuntan. Finally, the model is applied in the region spanning from Xiangjiaba to Cuntan of the upper Yangtze River to predict the daily SSC during flood season at Cuntan station under different forecast periods. Results show that, compared to the LSTM model, the RF-LSTM model can better account for the lagged effects of the predictor factors on the SSC, and effectively capture the features that are strongly correlated with the SSC at Cuntan station. Under all the four different forecast horizons considered, the RF-LSTM exhibits superior performance in terms of both prediction accuracy and overall capability. Specially, when considering no-forecast and 1-d forecast horizons, both models exhibit high prediction accuracy, with Nash-Sutcliffe efficiency coefficients exceeding 0.82 during the validation period. Notably, the RF-LSTM model achieves a Nash-Sutcliffe efficiency coefficient of 0.91 in the no-forecast horizon, outperforming the LSTM model by reducing mean absolute errors and root mean square errors by 8% and 13%, respectively. Furthermore, under both of these forecast horizons, the RF-LSTM model more precisely captures SSC peaks and their occurrence timings. However, as the forecast horizon increases to 2 days and 3 days, the accuracy of both models decrease significantly. Nevertheless, the RF-LSTM model continues to outperform the LSTM model in terms of computational accuracy, demonstrating its robustness and reliability across different forecast horizons. These findings highlight the potential of the RF-LSTM model as a valuable tool for SSC prediction in the upper reaches of the Yangtze River, offering a valuable reference for future studies in this domain. [ABSTRACT FROM AUTHOR] |
