Automatic Quality Control of Crowdsourced Rainfall Data With Multiple Noises: A Machine Learning Approach-Reference-Cited by-同舟云学术

Automatic Quality Control of Crowdsourced Rainfall Data With Multiple Noises: A Machine Learning Approach

Published:2021-11 Issue:11 Volume:57 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resources Research

Author:

Niu Geng¹²³^ORCID,Yang Pan³^ORCID,Zheng Yi²⁴^ORCID,Cai Ximing³^ORCID,Qin Huapeng¹^ORCID

Affiliation:

1. Key laboratory for Urban Habitat Environmental Science and Technology School of Environmental and Energy Peking University Shenzhen Graduate School Shenzhen China

2. School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen China

3. Department of Civil and Environmental Engineering University of Illinois at Urbana‐Champaign Champaign IL USA

4. Shenzhen Municipal Engineering Lab of Environmental IoT Technologies Southern University of Science and Technology Shenzhen China

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR029121

Reference79 articles.

1. Outlier Analysis

2. Quality Control in Crowdsourcing Systems: Issues and Directions

3. A predictive model for reach morphology classification in mountain streams using multilayer perceptron methods

4. Error analysis of TMI rainfall estimates over ocean for variational data assimilation

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