title: Comparison of models for predicting the changes in phytoplankton community composition in the receiving water system of an inter-basin water transfer project.
abstract: 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.
authors.affiliation: State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; University of Chinese Academy of Sciences, Beijing 100049, China.
authors.forename: Qinghui
authors.initials: Q
authors.lastname: Zeng
authors.affiliation: The Center of Space Surveying and Mapping in China, Beijing 102102, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Liu
authors.affiliation: State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
authors.forename: Hongtao
authors.initials: H
authors.lastname: Zhao
authors.affiliation: State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
authors.forename: Mingdong
authors.initials: M
authors.lastname: Sun
authors.affiliation: State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China. Electronic address: xyli@rcees.ac.cn.
authors.forename: Xuyong
authors.initials: X
authors.lastname: Li
mesh_terms: D002681:China; D000460:Chlorophyta / Q000166:cytology; D000458:Cyanobacteria / Q000166:cytology / Q000302:isolation & purification; D017377:Diatoms / Q000166:cytology / Q000302:isolation & purification; D017753:Ecosystem; D004784:Environmental Monitoring; D000069550:Machine Learning; D016571:Neural Networks, Computer; D010839:Phytoplankton / Q000166:cytology / Q000302:isolation & purification*; D012621:Seasons; D060388:Support Vector Machine; D014871:Water Microbiology; D062066:Water Resources
keywords: Artificial neural network; Phytoplankton community; Random forest; Support vector machine; Water transfer