A probabilistic approach to training machine learning models using noisy data-Reference-Cited by-同舟云学术

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A probabilistic approach to training machine learning models using noisy data

Published:2024-08 Issue: Volume:179 Page:106133
ISSN:1364-8152
Container-title:Environmental Modelling & Software
language:en
Short-container-title:Environmental Modelling & Software

Author:

Alzraiee Ayman H.^ORCID,Niswonger Richard G.

Funder

USGS

Publisher

Elsevier BV

Reference56 articles.

1. A review of uncertainty quantification in deep learning: techniques, applications and challenges;Abdar;Inf. Fusion,2021

2. Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs;Adnan;J. Hydrol.,2020

3. Interpretable machine learning tools: a survey;Agarwal,2020

4. An Introduction to Outlier Analysis;Aggarwal,2017

5. Short‐term precipitation forecast based on the PERSIANN system and LSTM recurrent neural networks;Akbari Asanjan;J. Geophys. Res. Atmos.,2018

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1. Next Generation Public Supply Water Withdrawal Estimation for the Conterminous United States Using Machine Learning and Operational Frameworks;Water Resources Research;2024-07

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