A deep learning technique based flood propagation experiment-Reference-Cited by-同舟云学术

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A deep learning technique based flood propagation experiment

Published:2021-05-06 Issue:3 Volume:14 Page:
ISSN:1753-318X
Container-title:Journal of Flood Risk Management
language:en
Short-container-title:J Flood Risk Management

Author:

Hou Jingming¹,Li Xuan¹^ORCID,Bai Ganggang¹,Wang Xinhong¹,Zhang Zongxiao¹,Yang Lu¹,Du Ying'en¹,Ma Yongyong¹,Fu Deyu²,Zhang Xianguo²

Affiliation:

1. State Key Laboratory of Eco‐hydraulics in Northwest Arid Region of China Xi'an University of Technology Shaanxi China

2. Beijing Captial Co., Ltd Beijing China

Funder

National Basic Research Program of China

National Natural Science Foundation of China

Publisher

Wiley

Subject

Water Science and Technology,Safety, Risk, Reliability and Quality,Geography, Planning and Development,Environmental Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jfr3.12718

Reference40 articles.

1. TensorFlow: learning functions at scale

2. Discharge coefficient of sharp-crested weirs using potential flow

3. Automated crater shape retrieval using weakly-supervised deep learning

4. Closure to “Dam-Break Flows: Acquisition of Experimental Data through an Imaging Technique and 2D Numerical Modeling” by F. Aureli, A. Maranzoni, P. Mignosa, and C. Ziveri

5. Imaging Enhancement of Light-Sheet Fluorescence Microscopy via Deep Learning

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