Prediction of River Sediment Transport Based on Wavelet Transform and Neural Network Model

• Published in: Applied sciences Vol. 12; no. 2; p. 647
• Main Authors: Li, Zongyu, Sun, Zhilin, Liu, Jing, Dong, Haiyang, Xiong, Wenhua, Sun, Lixia, Zhou, Hanyu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2022
• Subjects: Accuracy; Algorithms; Aquatic ecosystems; Artificial intelligence; Coasts; Decomposition; Estuaries; Fluvial sediments; Hydrologic models; Hydrology; Neural networks; Precipitation; Rainfall; rainfall and runoff; Rivers; Runoff; sediment prediction; Sediment transport; Signal processing; Time series; Water resources; wavelet transform; Wavelet transforms
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12020647

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.