Fluvial bedload transport modelling: advanced ensemble tree-based models or optimized deep learning algorithms?

IF 5.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Engineering Applications of Computational Fluid Mechanics Pub Date : 2024-05-10 DOI:10.1080/19942060.2024.2346221

Khabat Khosravi, Aitazaz A. Farooque, Sayed M. Bateni, Changhyun Jun, Dorsa Mohammadi, Zahra Kalantari, James R. Cooper

引用次数: 0

Abstract

The potential of advanced tree-based models and optimized deep learning algorithms to predict fluvial bedload transport was explored, identifying the most flexible and accurate algorithm, and the o...

查看原文本刊更多论文

冲积层床面负荷迁移建模：基于树的高级集合模型还是优化的深度学习算法？

探索了基于树的先进模型和优化的深度学习算法在预测河流床面荷载运移方面的潜力，确定了最灵活、最准确的算法，并对其进行了优化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Engineering Applications of Computational Fluid Mechanics ENGINEERING, MULTIDISCIPLINARY-ENGINEERING, MECHANICAL

CiteScore

10.60

自引率

14.80%

发文量

109

审稿时长

3.4 months

期刊介绍： The aim of Engineering Applications of Computational Fluid Mechanics is a continuous and timely dissemination of innovative, practical and industrial applications of computational techniques to solve the whole range of hitherto intractable fluid mechanics problems. The journal is a truly interdisciplinary forum and publishes original contributions on the latest advances in numerical methods in fluid mechanics and their applications to various engineering fields including aeronautic, civil, environmental, hydraulic and mechanical. The journal has a distinctive and balanced international contribution, with emphasis on papers addressing practical problem-solving by means of robust numerical techniques to generate precise flow prediction and optimum design, and those fostering the thorough understanding of the physics of fluid motion. It is an open access journal.