Real-time prediction of river chloride concentration using ensemble learning

doi:10.1016/j.envpol.2021.118116

IEECAS OpenIR > 加速器质谱中心

	Real-time prediction of river chloride concentration using ensemble learning
	Zhang, Qianqian 1,2; Li, Zhong 2; Zhu, Lu 3; Zhang, Fei 4,5; Sekerinski, Emil 6; Han, Jing-Cheng 7; Zhou, Yang 7
通讯作者	Li, Zhong(zoeli@mcmaster.ca)
	2021-12-15
发表期刊	ENVIRONMENTAL POLLUTION
ISSN	0269-7491
卷号	291 页码:12
摘要	Real-time river chloride prediction has received a lot of attention for its importance in chloride control and management. In this study, an artificial neural network model (i.e., multi-layer perceptron, MLP) and a statistical inference model (i.e., stepwise-cluster analysis, SCA) are developed for predicting chloride concentration in stream water. Then, an ensemble learning model based on MLP and SCA is proposed to further improve the modeling accuracy. A case study of hourly river chloride prediction in the Grand River, Canada is presented to demonstrate the model applicability. The results show that the proposed ensemble learning model, MLP-SCA, provides the best overall performance compared with its two ensemble members in terms of RMSE, MAPE, NSE, and R-2 with values of 11.58 mg/L, 27.55%, 0.90, and 0.90, respectively. Moreover, MLP-SCA is more competent for predicting extremely high chloride concentration. The prediction of observed concentrations above 150 mg/L has RMSE and MAPE values of 9.88 mg/L and 4.40%, respectively. The outstanding performance of the proposed MLP-SCA, particularly in extreme value prediction, indicates that it can provide reliable chloride prediction using commonly available data (i.e., conductivity, water temperature, river flow rate, and rainfall). The high-frequency prediction of chloride concentration in the Grand River can supplement the existing water quality monitoring programs, and further support the real-time control and management of chloride in the watershed. MLP-SCA is the first ensemble learning model for river chloride prediction and can be extended to other river systems for water quality prediction.
关键词	Chloride prediction MLP-SCA Ensemble learning Stepwise-cluster analysis Multi-layer perceptron
DOI	10.1016/j.envpol.2021.118116
关键词[WOS]	STEPWISE CLUSTER-ANALYSIS ; LOW-FLOW NITRATE ; NEURAL-NETWORK ; MULTILAYER PERCEPTRON ; AIR-QUALITY ; WATER ; REGRESSION ; DISCHARGE ; SYSTEM ; MODEL
收录类别	SCI ; SCI
语种	英语
资助项目	MacDATA Institute at McMaster University, Canada
WOS研究方向	Environmental Sciences & Ecology
项目资助者	MacDATA Institute at McMaster University, Canada
WOS类目	Environmental Sciences
WOS记录号	WOS:000697348900006
出版者	ELSEVIER SCI LTD
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ieecas.cn/handle/361006/17032
专题	加速器质谱中心第四纪科学与全球变化卓越创新中心
通讯作者	Li, Zhong
作者单位	1.Chengdu Univ Informat Technol, Chengdu 610225, Peoples R China 2.McMaster Univ, Dept Civil Engn, Hamilton, ON L8S 4L8, Canada 3.McMaster Univ, Dept Chem Engn, Hamilton, ON L8S 4L8, Canada 4.Chinese Acad Sci, Inst Earth Environm, SKLLQG, Xian 710061, Peoples R China 5.CAS Ctr Excellence Quaternary Sci & Global Change, Xian 710061, Peoples R China 6.McMaster Univ, Dept Comp & Software, Hamilton, ON L8S 4L8, Canada 7.Shenzhen Univ, Coll Chem & Environm Engn, Water Sci & Environm Engn Res Ctr, Shenzhen 518060, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Qianqian,Li, Zhong,Zhu, Lu,et al. Real-time prediction of river chloride concentration using ensemble learning[J]. ENVIRONMENTAL POLLUTION,2021,291:12.
APA	Zhang, Qianqian.,Li, Zhong.,Zhu, Lu.,Zhang, Fei.,Sekerinski, Emil.,...&Zhou, Yang.(2021).Real-time prediction of river chloride concentration using ensemble learning.ENVIRONMENTAL POLLUTION,291,12.
MLA	Zhang, Qianqian,et al."Real-time prediction of river chloride concentration using ensemble learning".ENVIRONMENTAL POLLUTION 291(2021):12.