Sediment-carrying capacity prediction using extreme learning machine. Issue 4 (September 2019)

• Record Type: Journal Article
• Authors: Wei, Xianglong; Li, Chang; Huang, Wei; Gu, Jiyi; liu, Jing; Zhu, Mingcheng
• Is Part Of: IOP conference series. Volume 304:Issue 4(2019)
• Publication Date: 2019-09
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/304/4/042006
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: Sediment-carrying capacity prediction is an important part in the river sediments movement simulation. In this paper, a novel machine learning approach was proposed to predict the sediment-carrying capacity by using the Extreme Learning Machine (ELM). The evaluation indexes and the average errors of these prediction models were analysed in detail. The result shows that the ELM model has a good generalization performance for the sediment-carrying capacity prediction, and the ELM approach could be an effective tool for the sediment movement simulation.