传感技术学报2026，Vol.39Issue(3)：518-527,10.DOI:10.3969/j.issn.1004-1699.2026.03.008
基于Stacking集成学习的超声粒径分布反演方法研究
Research on Ultrasonic Particle Size Distribution Inversion Using Stacking Ensemble Learning Approach
摘要
Abstract
The intricate and computationally-demanding constraints associated with conventional inversion models used for analyzing sus-pended sediment particle size distribution are delved into.A pioneering approach is presented which leverages the combination of base learners of extreme random trees,decision trees,and XGBoost,along with the meta-learner of random forest algorithm,denoting a new stacking ensemble learning model.The methodology encompasses the preparation of suspended sediment samples with predetermined size distributions achieved through sieving techniques,supplemented by ultrasonic attenuation experiments to establish the dataset.The process involves feature selection using the Boruta algorithm and fine-tuning model hyperparameters through an improved grey wolf opti-mization algorithm.In contrast to the individual,yet high efficient random forest algorithm model,the proposed model showcases remark-able accuracy improvements,leading to notable reductions in relative root mean square error:4.644 7%for RR distribution,0.207%for log-normal distribution,0.381%for beta distribution,and 0.397%for random distribution.Furthermore,the model demonstrates robust generalizability during assessments involving bimodal,geometric,and U-shaped distributions,thereby offering a reliable methodology for inferring the particle size distribution of suspended sediments.关键词
悬移质/粒径分布/Stacking集成学习/改进的灰狼优化算法Key words
suspended sediment/particle size distribution/stacking ensemble learning/improved grey wolf optimization algorithm引用本文复制引用
袁昌权,陶然,史占红,方卫华,谢代梁,徐雅,黄震威..基于Stacking集成学习的超声粒径分布反演方法研究[J].传感技术学报,2026,39(3):518-527,10.基金项目
国家重点研发计划项目(2022YFC3204504) （2022YFC3204504）
