Imputation of Contiguous Gaps and Extremes of Subhourly Groundwater Time Series Using Random Forests-Reference-Cited by-同舟云学术

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Imputation of Contiguous Gaps and Extremes of Subhourly Groundwater Time Series Using Random Forests

Published:2021 Issue: Volume: Page:
ISSN:2689-3967
Container-title:Journal of Machine Learning for Modeling and Computing
language:en
Short-container-title:J Mach Learn Model Comput

Author:

Dwivedi Dipankar,Mital Utkarsh,Faybishenko Boris,Dafflon Baptiste,Varadharajan Charuleka,Agarwal Deborah,Williams Kenneth Hurst,Steefel Carl,Hubbard Susan

Publisher

Begell House

Cited by 14 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. Automating property valuation at the macro scale of suburban level: A multi-step method based on spatial imputation techniques, machine learning and deep learning;Habitat International;2024-06

2. Time Series Missing Imputation Framework to Combine Multi-scale CNN and Weighted KNN Based on Information Entropy;Proceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence;2024-01-19

3. Monitoreo del nivel de las aguas subterráneas mediante pozos domésticos en explotación: eliminación de valores atípicos y sustitución de valores faltantes;Hydrogeology Journal;2023-12-23

4. GMA: Gap Imputing Algorithm for time series missing values;Journal of Electrical Systems and Information Technology;2023-08-30

5. Building Cross-Site and Cross-Network collaborations in critical zone science;Journal of Hydrology;2023-03

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