Daily suspended sediment concentration forecast in the upper reach of Yellow River using a comprehensive integrated deep learning model

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@Article{FAN:2023:jhydrol,

• author = "Jinsheng Fan and Xiaofang Liu and Weidong Li",
• title = "Daily suspended sediment concentration forecast in the upper reach of Yellow River using a comprehensive integrated deep learning model",
• journal = "Journal of Hydrology",
• volume = "623",
• pages = "129732",
• year = "2023",
• ISSN = "0022-1694",
• DOI = "doi:10.1016/j.jhydrol.2023.129732",
• URL = "https://www.sciencedirect.com/science/article/pii/S0022169423006741",
• keywords = "genetic algorithms, genetic programming, Daily SSC prediction, Integrated deep learning, Wavelet transformation, CNN, LSTM, ANN",
• abstract = "The precise prediction of suspended sediment concentration (SSC) is of great importance for river reservoir construction planning, water resource management, and ecological environment restoration. This research aims to improve SSC prediction accuracy by constructing a comprehensive and integrated deep learning model Wavelet-MGGP-CNN-LSTM (ICNN-LSTM), combining wavelet transformation (WT), multi-gene genetic programming (MGGP), convolutional neural network (CNN), and long short-term memory (LSTM) simultaneously. In ICNN-LSTM, the WT decomposes the signal and extracts time and frequency domain information, while the MGGP filters out redundant information. Then, the CNN and LSTM are integrated in a parallel and loosely coupled manner to form an initial combined model CNN-LSTM (CNN combined with LSTM) to process filtered information by WT and MGGP. Furthermore, this study compares the performance of ICNN-LSTM with CNN, LSTM, CNN-LSTM, ICNN (CNN embedded with WT and MGGP), ILSTM (LSTM embedded with WT and MGGP), artificial neural network (ANN), and the traditional sediment rating curve (SRC). The evaluation of prediction accuracy for all models was conducted using root mean square error (RMSE), Nash-Sutcliffe coefficient (NSC), coefficient of determination (R2), and mean absolute error (MAE) as performance indicators. The daily discharge and suspended sediment concentration series data from Tangnaihai Hydrological Station in the upper reaches of the Yellow River spanning from 1977 to 1987 were selected to train and test the models. Results show that, first, deep learning networks such as CNN and LSTM outperform the shallow neural network ANN, with LSTM providing higher accuracy than CNN. Second, the CNN-LSTM hybrid outperforms both CNN and LSTM models, exhibiting a nearly 89percent improvement in NSC value compared to SRC in the test phase. Third, deep learning models such as ICNN, ILSTM, and ICNN-LSTM show significantly higher NSC values than CNN, LSTM, and CNN-LSTM models in the test phase, with improvements of 13.8percent, 5.7percent, and 12.1percent, respectively. Moreover, compared to SRC, the proposed ICNN-LSTM model improves NSC value by nearly 140percent in the test phase. The proposed ICNN-LSTM model, integrating the advantages of WT, deep learning, and ensemble learning, provides accurate and reliable predictions and serves as a reference for time series prediction modeling",

}

Genetic Programming entries for Jinsheng Fan Xiaofang Liu Weidong Li

Citations