# Prediction of River Sediment Transport Based on Wavelet Transform and Neural Network Model
###### Abstract  
The sedimentation problem is one of the critical issues affecting the long-term use of rivers, and the study of sediment variation in rivers is closely related to water resource, river ecosystem and estuarine delta siltation. Traditional research on sediment variation in rivers is mostly based on field measurements and experimental simulations, which requires a large amount of human and material resources, many influencing factors and other restrictions. With the development of computer technology, intelligent approaches have been applied to hydrological models to establish small information in river areas. In this paper, considering the influence of multiple factors on sediment transport, the validity of predicting sediment transport combined with wavelet transforms and neural network was analyzed. The rainfall and runoff cycles are extracted and decomposed into time series sub-signals by wavelet transforms; then, the data post-processing is used as the neural network training set to predict the sediment model. The results show that wavelet coupled neural network model effectively improves the accuracy of the predicted sediment model, which can provide a reference basis for river sediment prediction. wavelet transform; sediment prediction; rainfall and runoff; estuaries 2022 2  
prioritization to establish an effective coastal management system [5]. The difficulty of rainfall-runoff-sediment production hydrological processes remains challenging in terms of sand production prediction [6]. At the meantime, sediment data is highly dependent on runoff, thus rainfall-runoff models can be developed to improve sediment load prediction [7]. Therefore, the research on accurate forecasting of river hydro-sediment sequences, combining runoff and rainfall information, is of guiding significance for the rational use of water resources and flood control in disaster relief. The estimated sediment flow of a river basin is crucial for the planning and management of the basin. In recent years, as computing and artificial intelligence technology are developing, the artificial neural network has been widely used in mathematical model building and disaster prediction. Artificial intelligence models can be used to predict and cluster various hydrological problems, such as prediction of rainfall, runoff and sediment, in which the most used models are ANN, SVM/SVR, ANFIS, genetic algorithm (GA), particle swarm algorithm (PSO) and artificial bee colony algorithm (ABC) [8; 9; 10; 11]. Based on the hydrological model of ANN, the flow prediction problem with precipitation as an input showed that the wavelet derived value can provide relevant flow path and hydrological information [12]. The simple artificial intelligence model provides raw time series data as an input, which has limited training accuracy for complex river sand production prediction, while the coupled wavelet function will improve the prediction efficiency and accuracy. Artificial neural networks were introduced to the field of hydrology by Professor [PERSON] back in the 1990s [13]. Then, [PERSON] et al. [14] compared traditional autoregressive models with neural network models for validation and showed that neural network prediction is effective in high flow runoff, while the opposite is achieved in low flow and autoregressive models are more adept. [PERSON] et al. [15] proposed a data-driven approach for long short-term memory (LSTM) networks and applied the model to the Poyang Lake Basin (PYLB) with non-uniform hydrological characteristics across space. The results showed that the model outperforms artificial neural networks (ANN) and soil and water assessment tools (SWAT) to capture the peak of runoff more accurately. In addition, there is significant potential in extending data-based modeling approaches in the field of hydrology. The MLP model proposed by [PERSON] can be successfully used to predict runoff due to rainfall in the Jhelum River.