Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2020WR029121 |
Automatic Quality Control of Crowdsourced Rainfall Data with Multiple Noises: A Machine Learning Approach | |
Geng Niu; Pan Yang; Yi Zheng; Ximing Cai; Huapeng Qin | |
2021-10-20 | |
发表期刊 | Water Resources Research
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出版年 | 2021 |
英文摘要 | In geophysics, crowdsourcing is an emerging nontraditional environmental monitoring approach that support data acquisition from individual citizens. However, because of the involvement of undertrained citizens and imprecise low-cost sensors, crowdsourced data applications suffer from different types of noises that can deteriorate the overall monitoring accuracy. In this study, we propose a machine learning approach for automatic crowdsourced data quality control (CSQC) that detects and removes noisy data inputs in spatially and temporally discrete crowdsourced observations coming from both fixed-point sensors (e.g., surveillance cameras) and moving sensors (e.g., moving cars/pedestrians). We design a set of features from original and interpolated rainfall data and use them to train and test the CSQC models using both supervised and unsupervised machine learning algorithms. The performances of the CSQC models under various scenarios assuming no retraining are also tested (hereafter referred to as transferability). The results based on synthetic but realistic data show that the CSQC models can significantly reduce the overall rainfall estimate errors. Under the stationary assumption, the CSQC models based on both supervised and unsupervised algorithms perform well in noisy data identification and overall rainfall estimation error reduction; however, if the model is transferred to other cities with different rainfall patterns or noise compositions (without retraining), supervised multilayer perceptrons (MLPs) show the best performance. This article is protected by copyright. All rights reserved. |
领域 | 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/340912 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | Geng Niu,Pan Yang,Yi Zheng,et al. Automatic Quality Control of Crowdsourced Rainfall Data with Multiple Noises: A Machine Learning Approach[J]. Water Resources Research,2021. |
APA | Geng Niu,Pan Yang,Yi Zheng,Ximing Cai,&Huapeng Qin.(2021).Automatic Quality Control of Crowdsourced Rainfall Data with Multiple Noises: A Machine Learning Approach.Water Resources Research. |
MLA | Geng Niu,et al."Automatic Quality Control of Crowdsourced Rainfall Data with Multiple Noises: A Machine Learning Approach".Water Resources Research (2021). |
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