A machine learning approach to water quality forecasts and sensor network expansion: Case study in the Wabash River Basin, <scp>United States</scp>-Reference-Cited by-同舟云学术

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

Published:2022-06 Issue:6 Volume:36 Page:
ISSN:0885-6087
Container-title:Hydrological Processes
language:en
Short-container-title:Hydrological Processes

Author:

Balson Tyler¹^ORCID,Ward Adam S.²³^ORCID

Affiliation:

1. Luddy School of Informatics, Computing, and Engineering Indiana University Bloomington Indiana USA

2. O'Neill School of Public and Environmental Affairs Indiana University Bloomington Indiana USA

3. Biological & Ecological Engineering Oregon State University Corvallis Oregon USA

Funder

University of Birmingham

National Science Foundation

U.S. Department of Energy

Publisher

Wiley

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.14619

Reference79 articles.

1. An Empirical Comparison of Machine Learning Models for Time Series Forecasting

2. Climate change impact assessment on water resources under RCP scenarios: A case study in Mundaú River Basin, Northeastern Brazil

3. Aulenbach B. T. Buxton H. T. Battaglin W. A. &Coupe R. H.(2007).Streamflow and nutrient fluxes of the Mississippi‐Atchafalaya River Basin and subbasins for the period of record through 2005.U.S. Geological Survey Open‐File Report 2007‐1080.https://doi.org/10.3133/ofr20071080

4. Biophysical impacts of climate-smart agriculture in the Midwest United States

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2. Application of classification machine learning algorithms for characterizing nutrient transport in a clay plain agricultural watershed;Journal of Environmental Management;2023-11

3. Research on a multiparameter water quality prediction method based on a hybrid model;Ecological Informatics;2023-09

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