Comparison of models for predicting the changes in phytoplankton community composition in the receiving water system of an inter basin water transfer project

CORC > 生态环境研究中心 > 中国科学院生态环境研究中心 > 城市与区域生态国家重点实验室

	Comparison of models for predicting the changes in phytoplankton community composition in the receiving water system of an inter basin water transfer project
	Zeng, Qinghui; Liu, Yi; Zhao, Hongtao; Sun, Mingdong; Li, Xuyong
刊名	ENVIRONMENTAL POLLUTION
	2017-04-01
卷号	223 期号:28 页码:676-684
关键词	Random Forest Support Vector Machine Artificial Neural Network Phytoplankton Community Water Transfer
英文摘要	Inter-basin water transfer projects might cause complex hydro-chemical and biological variation in the receiving aquatic ecosystems. Whether machine learning models can be used to predict changes in phytoplankton community composition caused by water transfer projects have rarely been studied. In the present study, we used machine learning models to predict the total algal cell densities and changes in phytoplankton community composition in Miyun reservoir caused by the middle route of the South to-North Water Transfer Project (SNWTP). The model performances of four machine learning models, including regression trees (RT), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) were evaluated and the best model was selected for further prediction. The results showed that the predictive accuracies (Pearson's correlation coefficient) of the models were RF (0.974), ANN (0.951), SVM (0.860), and RT (0.817) in the training step and RF (0.806), ANN (0.734), SVM (0.730), and RT (0.692) in the testing step. Therefore, the RF model was the best method for estimating total algal cell densities. Furthermore, the predicted accuracies of the RF model for dominant phytoplankton phyla (Cyanophyta, Chlorophyta, and Bacillariophyta) in Miyun reservoir ranged from 0.824 to 0.869 in the testing step. The predicted proportions with water transfer of the different phytoplankton phyla ranged from -8.88% to 9.93%, and the predicted dominant phyla with water transfer in each season remained unchanged compared to the phytoplankton succession without water transfer. The results of the present study provide a useful tool for predicting the changes in phytoplankton community caused by water transfer. The method is transferrable to other locations via establishment of models with relevant data to a particular area. Our findings help better understanding the possible changes in aquatic ecosystems influenced by inter-basin water transfer. (C) 2017 Elsevier Ltd. All rights reserved.
内容类型	期刊论文
源URL	[http://ir.rcees.ac.cn/handle/311016/38971]
专题	生态环境研究中心_城市与区域生态国家重点实验室
推荐引用方式 GB/T 7714	Zeng, Qinghui,Liu, Yi,Zhao, Hongtao,et al. Comparison of models for predicting the changes in phytoplankton community composition in the receiving water system of an inter basin water transfer project[J]. ENVIRONMENTAL POLLUTION,2017,223(28):676-684.
APA	Zeng, Qinghui,Liu, Yi,Zhao, Hongtao,Sun, Mingdong,&Li, Xuyong.(2017).Comparison of models for predicting the changes in phytoplankton community composition in the receiving water system of an inter basin water transfer project.ENVIRONMENTAL POLLUTION,223(28),676-684.
MLA	Zeng, Qinghui,et al."Comparison of models for predicting the changes in phytoplankton community composition in the receiving water system of an inter basin water transfer project".ENVIRONMENTAL POLLUTION 223.28(2017):676-684.