人民珠江2024，Vol.45Issue(3)：69-78,10.DOI:10.3969/j.issn.1001-9235.2024.03.008
基于WPT-ARO-DBN/WPT-EPO-DBN模型的月含沙量多步预测
Multi-step Prediction of Monthly Sediment Concentration Based on WPT-ARO-DBN/WPT-EPO-DBN Model
摘要
Abstract
Accurate multi-step sediment concentration prediction is of significance for regional soil erosion control,flood control and disaster reduction.To improve the multi-step prediction accuracy of sediment concentration and the prediction performance of the deep belief network(DBN),this paper proposes a multi-step prediction model of monthly sediment concentration by combining the artificial rabbit optimization(ARO)algorithm,eagle habitat optimization(EPO)algorithm,and DBN based on wavelet packet transform(WPT).The model is validated using time series data of monthly sediment concentration from Longtan Station in Yunnan Province.Firstly,WPT is employed to decompose the time series data of the monthly sediment concentration of the case in three layers,and eight more regular subsequence components are obtained.Secondly,the principles of ARO and EPO algorithms are introduced,and hyperparameters such as the neuron number in the hidden layer of DBN are optimized by ARO and EPO.Meanwhile,WPT-ARO-DBN and WPT-EPO-DBN prediction models are built,and WPT-PSO(particle swarm optimization)-DBN and WPT-DBN are constructed for comparative analysis.Finally,four models are adopted to predict each subsequence component,and the predicted values are superimposed to obtain the multi-step prediction results of the final monthly sediment concentration.The results are as follows.① WPT-ARO-DBN and WPT-EPO-DBN models have satisfactory prediction effects on the monthly sediment concentration of the case from one step ahead to four steps ahead.This yields sound prediction results for five steps ahead.The prediction effect for six steps ahead and seven steps ahead is average,and the prediction accuracy for eight steps ahead is poor and cannot meet the prediction accuracy requirements.②The multi-step prediction performance of WPT-ARO-DBN and WPT-EPO-DBN models is superior to WPT-PSO-DBN models and far superior to WPT-DBN models,with higher prediction accuracy,better generalization ability,and larger prediction step size.③ ARO and EPO can effectively optimize DBN hyperparameters,improve DBN prediction performance,and have better optimization effects than PSO.Additionally,WPT-ARO-DBN and WPT-EPO-DBN models can give full play to the advantages of WPT,new swarm intelligence algorithms and the DBN network and improve the multi-step prediction accuracy of monthly sediment concentration,and the prediction accuracy decreases with the increasing prediction steps.关键词
月含沙量预测/深度信念网络/人工兔优化算法/鹰栖息优化算法/小波包变换/组合模型Key words
prediction of monthly sediment concentration/deep belief network/artificial rabbit optimization algorithm/eagle perching optimization algorithm/wavelet packet transform/combining model分类
天文与地球科学引用本文复制引用
高雪梅,崔东文..基于WPT-ARO-DBN/WPT-EPO-DBN模型的月含沙量多步预测[J].人民珠江,2024,45(3):69-78,10.基金项目
国家重点研发计划项目(2019YFC0507500) （2019YFC0507500）
国家自然科学基金项目(41702278) （41702278）
中国地质调查局地质调查项目(DD20221758、DD20190326) （DD20221758、DD20190326）
