Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?-Reference-Cited by-同舟云学术

Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?

Published:2022-04 Issue:4 Volume:36 Page:
ISSN:0885-6087
Container-title:Hydrological Processes
language:en
Short-container-title:Hydrological Processes

Author:

Varadharajan Charuleka¹^ORCID,Appling Alison P.²^ORCID,Arora Bhavna¹^ORCID,Christianson Danielle S.³^ORCID,Hendrix Valerie C.³^ORCID,Kumar Vipin⁴^ORCID,Lima Aranildo R.⁵^ORCID,Müller Juliane³^ORCID,Oliver Samantha²^ORCID,Ombadi Mohammed¹^ORCID,Perciano Talita³^ORCID,Sadler Jeffrey M²^ORCID,Weierbach Helen¹^ORCID,Willard Jared D.⁴^ORCID,Xu Zexuan¹^ORCID,Zwart Jacob²^ORCID

Affiliation:

1. Earth and Environmental Sciences Area Lawrence Berkeley National Laboratory Berkeley California 94010 USA

2. U.S. Geological Survey, Water Mission Area Reston Virginia USA

3. Computing Sciences Area Lawrence Berkeley National Laboratory Berkeley California 94720 USA

4. Department of Computer Science and Engineering University of Minnesota Minneapolis Minnesota USA

5. Aquatic Informatics Vancouver BC V6E 4M3 Canada

Funder

Biological and Environmental Research

National Science Foundation of Sri Lanka

Lawrence Berkeley National Laboratory

U.S. Geological Survey

Publisher