Generalization of Runoff Risk Prediction at Field Scales to a Continental‐Scale Region Using Cluster Analysis and Hybrid Modeling-Reference-Cited by-同舟云学术

Generalization of Runoff Risk Prediction at Field Scales to a Continental‐Scale Region Using Cluster Analysis and Hybrid Modeling

Published:2022-09-03 Issue:17 Volume:49 Page:
ISSN:0094-8276
Container-title:Geophysical Research Letters
language:en
Short-container-title:Geophysical Research Letters

Author:

Ford Chanse M.¹,Hu Yao²³^ORCID,Ghosh Chirantan⁴^ORCID,Fry Lauren M.⁵^ORCID,Malakpour‐Estalaki Siamak²^ORCID,Mason Lacey⁵^ORCID,Fitzpatrick Lindsay⁶,Mazrooei Amir⁷^ORCID,Goering Dustin C.⁸

Affiliation:

1. Department of Earth and Environmental Sciences Michigan State University East Lansing MI USA

2. Department of Geography and Spatial Sciences University of Delaware Newark DE USA

3. Department of Civil and Environmental Engineering University of Delaware Newark DE USA

4. Department of Computer and Information Sciences University of Delaware Newark DE USA

5. Great Lakes Environmental Research Lab (GLERL) National Oceanic and Atmospheric Administration Ann Arbor MI USA

6. Cooperative Institute for Great Lakes Research (CIGLR) University of Michigan Ann Arbor MI USA

7. Research Application Laboratory National Center for Atmospheric Research (NCAR) Boulder CO USA

8. North Central River Forecast Center National Weather Service National Oceanic and Atmospheric Administration Chanhassen MN USA

Funder

U.S. Environmental Protection Agency

National Oceanic and Atmospheric Administration

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022GL100667

Reference29 articles.

1. Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition

2. Are harmful algal blooms becoming the greatest inland water quality threat to public health and aquatic ecosystems?

3. Human fatalities from cyanobacteria: chemical and biological evidence for cyanotoxins.

4. XGBoost

5. Snowpacks decrease and streamflows shift across the eastern US as winters warm

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Application of a Multi-Model Fusion Forecasting Approach in Runoff Prediction: A Case Study of the Yangtze River Source Region;Sustainability;2024-07-12

2. Applications of XGBoost in water resources engineering: A systematic literature review (Dec 2018–May 2023);Environmental Modelling & Software;2024-03

3. A methodological framework for improving the performance of data-driven models: a case study for daily runoff prediction in the Maumee domain, USA;Geoscientific Model Development;2023-04-06