A rapid and efficient method for flash flood simulation based on deep learning

IF 5.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Engineering Applications of Computational Fluid Mechanics Pub Date : 2024-09-26 DOI:10.1080/19942060.2024.2407016

Xinying Wang, Miao Xiao, Yi Liu, Jun Guo, Yangyang Qin, Yunkang Zhang

引用次数: 0

Abstract

Among the various natural disasters, the death caused by flash flood is the highest. Recently, the combination of deep learning methods and hydrodynamic models has shown superior performance in the...

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基于深度学习的快速高效山洪模拟方法

在各种自然灾害中，山洪造成的死亡人数最多。最近，深度学习方法与流体力学模型的结合在山洪灾害中表现出了卓越的性能。

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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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.