Machine Learning for Water Resources

7,146
Total downloads
38k
Total views and downloads

Articles

ORIGINAL RESEARCH

Published on 14 May 2021

Interpretability Versus Accuracy: A Comparison of Machine Learning Models Built Using Different Algorithms, Performance Measures, and Features to Predict E. coli Levels in Agricultural Water

Daniel L. Weller
Tanzy M. T. Love
Martin Wiedmann

doi 10.3389/frai.2021.628441

5,019 views
26 citations

EDITORIAL

Published on 13 May 2021

Editorial: Machine Learning for Water Resources

Gregoire Mariethoz
J. Jaime Gómez-Hernández

doi 10.3389/frai.2021.699862

2,183 views
5 citations

ORIGINAL RESEARCH

Published on 15 Apr 2021

An Application of an Embedded Model Estimator to a Synthetic Nonstationary Reservoir Model With Multiple Secondary Variables

Colin Daly

doi 10.3389/frai.2021.624697

2,023 views
5 citations

ORIGINAL RESEARCH

Published on 15 Apr 2021

Machine Learning Analysis of Hydrologic Exchange Flows and Transit Time Distributions in a Large Regulated River

Huiying Ren
Xuehang Song
Yilin Fang
Z. Jason Hou
Timothy D. Scheibe

doi 10.3389/frai.2021.648071

2,662 views
12 citations

ORIGINAL RESEARCH

Published on 18 Mar 2021

An Attempt to Boost Posterior Population Expansion Using Fast Machine Learning Algorithms

Przemysław Juda
Philippe Renard

doi 10.3389/frai.2021.624629

1,414 views
3 citations

ORIGINAL RESEARCH

Published on 04 Mar 2021

Assessment of a Spatiotemporal Deep Learning Approach for Soil Moisture Prediction and Filling the Gaps in Between Soil Moisture Observations

Mohamed ElSaadani
Emad Habib
Ahmed M. Abdelhameed
Magdy Bayoumi

doi 10.3389/frai.2021.636234

7,117 views
53 citations

ORIGINAL RESEARCH

Published on 09 Oct 2020

Adjusting for Conditional Bias in Process Model Simulations of Hydrological Extremes: An Experiment Using the North Wyke Farm Platform

Stelian Curceac
Peter M. Atkinson
Alice Milne
Lianhai Wu
Paul Harris

doi 10.3389/frai.2020.565859

3,557 views
15 citations

ORIGINAL RESEARCH

Published on 13 Aug 2020

Exploratory-Phase-Free Estimation of GP Hyperparameters in Sequential Design Methods—At the Example of Bayesian Inverse Problems

Michael Sinsbeck
Marvin Höge
Wolfgang Nowak

doi 10.3389/frai.2020.00052

2,242 views
2 citations

Share on

Frontiers in Artificial Intelligence

AI in Food, Agriculture and Water

Impact

38kTopic views
24kArticle views
7,146Article downloads

View impact

Articles

Interpretability Versus Accuracy: A Comparison of Machine Learning Models Built Using Different Algorithms, Performance Measures, and Features to Predict E. coli Levels in Agricultural Water

Editorial: Machine Learning for Water Resources

An Application of an Embedded Model Estimator to a Synthetic Nonstationary Reservoir Model With Multiple Secondary Variables

Machine Learning Analysis of Hydrologic Exchange Flows and Transit Time Distributions in a Large Regulated River

An Attempt to Boost Posterior Population Expansion Using Fast Machine Learning Algorithms

Assessment of a Spatiotemporal Deep Learning Approach for Soil Moisture Prediction and Filling the Gaps in Between Soil Moisture Observations

Adjusting for Conditional Bias in Process Model Simulations of Hydrological Extremes: An Experiment Using the North Wyke Farm Platform

Exploratory-Phase-Free Estimation of GP Hyperparameters in Sequential Design Methods—At the Example of Bayesian Inverse Problems

Frontiers in Artificial Intelligence

AI in Food, Agriculture and Water