提高稀疏数据区域的流量预测精度：水文-水动力和混合机器学习综合模型的全新视角

IF 5.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Engineering Applications of Computational Fluid Mechanics Pub Date : 2024-08-13 DOI:10.1080/19942060.2024.2387051

Saeed Khorram, Nima Jehbez

引用次数: 0

摘要

考虑到观测数据的差异和复杂的非线性关系，本研究综合了水文、水动力和时间序列模型，包括 SWAT+、MIKE2...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Improving the streamflow prediction accuracy in sparse data regions: a fresh perspective on integrated hydrological-hydrodynamic and hybrid machine learning models

Considering the differences and complex nonlinear relationships of the observational data, this research integrated the hydrological, hydrodynamic and time series models, including the SWAT+, MIKE2...

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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.