The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology-Reference-Cited by-同舟云学术

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The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology

Published:2022-04 Issue:4 Volume:58 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resources Research

Author:

Fang Kuai¹²^ORCID,Kifer Daniel³^ORCID,Lawson Kathryn²^ORCID,Feng Dapeng²^ORCID,Shen Chaopeng²^ORCID

Affiliation:

1. Department of Earth System Science Stanford University Stanford CA USA

2. Department of Civil and Environmental Engineering Pennsylvania State University University Park PA USA

3. Department of Computer Science and Engineering Pennsylvania State University University Park PA USA

Funder

U.S. Department of Energy

National Science Foundation

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021WR029583

Reference72 articles.

1. Development of gridded surface meteorological data for ecological applications and modelling

2. Evaluating different machine learning methods to simulate runoff from extensive green roofs

3. Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges

4. Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach?

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1. Improving streamflow forecasting in semi-arid basins by combining data segmentation and attention-based deep learning;Journal of Hydrology;2024-11

2. Streamflow Prediction in Human‐Regulated Catchments Using Multiscale Deep Learning Modeling With Anthropogenic Similarities;Water Resources Research;2024-09

3. Higher-order internal modes of variability imprinted in year-to-year California streamflow changes;Communications Earth & Environment;2024-08-21

4. Representative Sample Size for Estimating Saturated Hydraulic Conductivity via Machine Learning: A Proof‐Of‐Concept Study;Water Resources Research;2024-08

5. Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations;Journal of Hydrology;2024-08

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