Comparison of models for predicting the changes in phytoplankton community composition in the receiving water system of an inter-basin water transfer project.
Comparison of models for predicting the changes in phytoplankton community composition in the receiving water system of an inter-basin water transfer project.
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DOI：
10.1016/j.envpol.2017.02.001
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发表时间：
2017-04
影响因子：
8.9
通讯作者：
Qinghui Zeng;Yi Liu;Hongtao Zhao;Mingdong Sun;Xuyong Li
中科院分区：
文献类型：
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作者：
Qinghui Zeng;Yi Liu;Hongtao Zhao;Mingdong Sun;Xuyong Li
跨流域调水工程可能会在受水区的水生生态系统中引起复杂的水化学和生物变化。机器学习模型是否可以用于预测调水工程引起的浮游植物群落组成的变化，目前还很少有人研究。利用机器学习模型预测了南水北调中线工程对密云水库藻类总密度和浮游植物群落组成的影响。四个机器学习模型，包括回归树（RT），随机森林（RF），支持向量机（SVM），人工神经网络（ANN）的模型性能进行了评估，并选择最好的模型进行进一步的预测。结果表明，模型的预测精度（Pearson相关系数）在训练阶段为RF（0.974），ANN（0.951），SVM（0.860）和RT（0.817），在测试阶段为RF（0.806），ANN（0.734），SVM（0.730）和RT（0.692）。因此，RF模型是估算总藻细胞密度的最佳方法。此外，密云水库优势浮游植物门（蓝藻门，绿藻门，硅藻门）的RF模型的预测精度在0.824至0.869之间的测试步骤。不同浮游植物门的水分转移的预测比例范围为-8.88%至9.93%，与没有水分转移的浮游植物演替相比，预测的优势门在每个季节的水分转移保持不变。本研究结果为预测调水引起的浮游植物群落变化提供了有用的工具。该方法可通过建立具有特定区域的相关数据的模型来转移到其他位置。我们的研究结果有助于更好地理解跨流域调水对水生生态系统可能产生的影响。
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.