水力发电2026，Vol.52Issue(5)：56-63,8.
基于机器学习模型的河道含沙量预测
Prediction of River Sediment Concentration Based on Machine Learning Models
摘要
Abstract
Accurate prediction of sediment content in rivers plays a significant role in water intake for irrigation,river navigation and sediment discharge from reservoirs.A lightweight architecture machine learning model named WCNN(Wavelet Convolutional Neural Network)with parallel computing capabilities for river sediment concentration prediction is employed in this study,and applied to the Waizhou Hydrological Station on the Nanchang reach of the lower Ganjiang River in China.Results demonstrate that,for the sediment concentration forecasting at Waizhou Station during 2017-2018,the WCNN model outperforms the commonly used recurrent neural networks LSTM and GRU.Particularly for 1-day-ahead predictions,it achieves RMSE and MAE values of 0.003 1 kg/m3 and 0.002 0 kg/m3,with NSE and R reaching 0.887 2 and 0.946 1 respectively,indicating high predictive accuracy.Concurrently,the parameter count of the WCNN model is reduced by 54.8%and 40.2%compared to LSTM and GRU models,while training time decreased by 44.3%and 27.9%.Overall comparative analysis demonstrates that the WCNN model delivers superior prediction performance with significantly reduced computational time.关键词
机器学习/WCNN模型/含沙量预测/特征选择/赣江Key words
machine learning/WCNN model/sediment concentration prediction/feature selection/Ganjiang River分类
建筑与水利引用本文复制引用
陈珺,梁晟涢,黄燕华,黄卫东,符育文,许慧..基于机器学习模型的河道含沙量预测[J].水力发电,2026,52(5):56-63,8.基金项目
国家重点研发计划(2021YFD1700802) （2021YFD1700802）
河南黄河河务局工程建设中心科技项目(YDFZSFH2024) （YDFZSFH2024）
