气候变化领域集成服务门户(CCPortal): Machine learning approach identifies water sample source based on microbial abundance

CCPortal

DOI	10.1016/j.watres.2021.117185
	Machine learning approach identifies water sample source based on microbial abundance
	Wang, Chenchen; Mao, Guannan; Liao, Kailingli; Ben, Weiwei; Qiao, Meng; Bai, Yaohui; Qu, Jiuhui
通讯作者	Bai, YH (通讯作者)
发表日期	2021
ISSN	0043-1354
EISSN	1879-2448
卷号	199
英文摘要	Water quality can change along a river system due to differences in adjacent land use patterns and discharge sources. These variations can induce rapid responses of the aquatic microbial community, which may be an indicator of water quality characteristics. In the current study, we used a random forest model to predict water sample sources from three different river ecosystems along a gradient of anthropogenic disturbance (i.e., less disturbed mountainous area, wastewater discharged urban area, and pesticide and fertilizer applied agricultural area) based on environmental physicochemical indices (PCIs), microbiological indices (MBIs), and their combination. Results showed that among the PCI-based models, using conventional water quality indices as inputs provided markedly better prediction of water sample source than using pharmaceutical and personal care products (PPCPs), and much better prediction than using polycyclic aromatic hydrocarbons (PAHs) and substituted PAHs (SPAHs). Among the MBI-based models, using the abundances of the top 30 bacteria combined with pathogenic antibiotic resistant bacteria (PARB) as inputs achieved the lowest median out-of-bag error rate (9.9%) and increased median kappa coefficient (0.8694), while adding fungal inputs reduced the kappa coefficient. The model based on the top 30 bacteria still showed an advantage compared with models based on PCIs or the combination of PCIs and MBIs. With improvement in sequencing technology and increase in data availability in the future, the proposed method provides an economical, rapid, and reliable way in which to identify water sample sources based on abundance data of microbial communities. (c) 2021 Elsevier Ltd. All rights reserved.
关键词	PHARMACEUTICALS RIVER CARBAMAZEPINE COMMUNITIES BACTERIAL IMPACTS
英文关键词	Source identification of water samples; Physicochemical indices; Microbial abundance; Machine learning classification; Random forest
语种	英语
WOS研究方向	Engineering ; Environmental Sciences & Ecology ; Water Resources
WOS类目	Engineering, Environmental ; Environmental Sciences ; Water Resources
WOS记录号	WOS:000659348700011
来源期刊	WATER RESEARCH
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/260153
推荐引用方式 GB/T 7714	Wang, Chenchen,Mao, Guannan,Liao, Kailingli,et al. Machine learning approach identifies water sample source based on microbial abundance[J]. 中国科学院青藏高原研究所,2021,199.
APA	Wang, Chenchen.,Mao, Guannan.,Liao, Kailingli.,Ben, Weiwei.,Qiao, Meng.,...&Qu, Jiuhui.(2021).Machine learning approach identifies water sample source based on microbial abundance.WATER RESEARCH,199.
MLA	Wang, Chenchen,et al."Machine learning approach identifies water sample source based on microbial abundance".WATER RESEARCH 199(2021).