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.

Characterizing sedimentary processes in abandoned channel using compositional data analysis and wavelet transform

“…Previous studies have utilized the WT to quantify cyclic sedimentation patterns in different contexts (Rivera et al 2004;Prokoph and Patterson 2004;Tarar et al 2018;Wren et al 2019;Li et al, 2022). For example, the WT was used to study the Fig.…”

“…WT can simultaneously extract local and temporal information, including periods of cyclic events in spatial and time domains (Saadatinejad et al 2011). Researchers have widely explored the use of WT to interpret geological data, for instance (Perez-Muñoz et al 2013;Kadkhodaie and Rezaee 2017;Duesing et al 2021;Li et al, 2022). The commonly employed wavelet techniques are continuous wavelets transformation (CWT) and discrete wavelets transformation (DWT), and the Morlet family is the most used CWT family in earth science.…”

Forecasting Wind Speed Using the Proposed Wavelet Neural Network

“…Li et al combined wavelet transforms with ANN to forecast the sediment transport. Te fndings demonstrate that the predicted sediment model's accuracy is signifcantly increased by the wavelet combined the ANN model [33].…”