Data science applications in hydrology-Reference-Cited by-同舟云学术

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Data science applications in hydrology

Published:2023-09 Issue:9 Volume:37 Page:
ISSN:0885-6087
Container-title:Hydrological Processes
language:en
Short-container-title:Hydrological Processes

Author:

Maneta Marco¹^ORCID,Tetzlaff Dörthe²³

Affiliation:

1. Geosciences Department University of Montana Missoula Montana USA

2. Department of Ecohydrology and Biogeochemistry IGB Leibniz Institute of Freshwater Ecology and Inland Fisheries Berlin Berlin Germany

3. Geography Department Humboldt University Berlin Berlin Germany

Publisher

Wiley

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.14983

Reference11 articles.

1. A machine learning approach to water quality forecasts and sensor network expansion: Case study in the Wabash River Basin, United States

2. Improving the theoretical underpinnings of process-based hydrologic models

3. Suspended sediment concentration estimation in the Sacramento‐San Joaquin Delta of California using long short‐term memory networks

4. What can we learn from multi-data calibration of a process-based ecohydrological model?

5. Using machine learning to improve predictions and provide insight into fluvial sediment transport

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