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

Zongyu Li,Jing Liu,Hanyu Zhou,Lixia Sun,Wenhua Xiong,Haiyang Dong,Zhilin Sun

doi:10.3390/app12020647

Zongyu Li, Jing Liu + Show 5 more

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https://doi.org/10.3390/app12020647

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Journal: Applied sciences	Publication Date: Jan 10, 2022
Citations: 9	License type: CC BY 4.0

Affiliation: Zhejiang Ocean University, Zhejiang University

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.

Highlights

Due to the tremendous variations in midstream and downstream areas of rivers and in the vicinity of estuaries, under the influence of climate change or strong anthropogenic factors at present, high efficiency and accurate tools are urgently needed for describing and predicting runoff and sediment movement conditions of rivers [1,2,3]
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
The simulation of river sediment transport is highly variable and nonlinear in nature, the difficulty of the runoff–sediment production hydrological process remains challenging in terms of sediment prediction

Summary

Introduction

Due to the tremendous variations in midstream and downstream areas of rivers and in the vicinity of estuaries, under the influence of climate change or strong anthropogenic factors at present, high efficiency and accurate tools are urgently needed for describing and predicting runoff and sediment movement conditions of rivers [1,2,3]. The hydro-sediment variability of rivers has brought significant impacts on river evolution, hydropower plant construction, river ecology, channel development and estuarine delta siltation [4]. Sediment load is transported by water flow to tributaries, reservoirs and affected by the processes of sediment trapping, and eventually outflow to the ocean. These processes can be difficult to measure. The changes in sediment transport seriously affect the construction of water resources, and have a profound impact on the production and life of people in riverine areas. 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

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