Department of Natural Resources, College of Environment and Natural Resources, Zhejiang University, 866 Yu Hang Tang Road, Hangzhou, 310058, Zhejiang Province, China,
Environ Sci Pollut Res Int. 2014 Sep;21(18):11036-53. doi: 10.1007/s11356-014-3046-x. Epub 2014 Jun 5.
Water quality forecasting in agricultural drainage river basins is difficult because of the complicated nonpoint source (NPS) pollution transport processes and river self-purification processes involved in highly nonlinear problems. Artificial neural network (ANN) and support vector model (SVM) were developed to predict total nitrogen (TN) and total phosphorus (TP) concentrations for any location of the river polluted by agricultural NPS pollution in eastern China. River flow, water temperature, flow travel time, rainfall, dissolved oxygen, and upstream TN or TP concentrations were selected as initial inputs of the two models. Monthly, bimonthly, and trimonthly datasets were selected to train the two models, respectively, and the same monthly dataset which had not been used for training was chosen to test the models in order to compare their generalization performance. Trial and error analysis and genetic algorisms (GA) were employed to optimize the parameters of ANN and SVM models, respectively. The results indicated that the proposed SVM models performed better generalization ability due to avoiding the occurrence of overtraining and optimizing fewer parameters based on structural risk minimization (SRM) principle. Furthermore, both TN and TP SVM models trained by trimonthly datasets achieved greater forecasting accuracy than corresponding ANN models. Thus, SVM models will be a powerful alternative method because it is an efficient and economic tool to accurately predict water quality with low risk. The sensitivity analyses of two models indicated that decreasing upstream input concentrations during the dry season and NPS emission along the reach during average or flood season should be an effective way to improve Changle River water quality. If the necessary water quality and hydrology data and even trimonthly data are available, the SVM methodology developed here can easily be applied to other NPS-polluted rivers.
由于农业排水流域涉及复杂的非点源（NPS）污染传输过程和高度非线性问题的河流自净过程，因此水质预测具有一定难度。本研究旨在建立人工神经网络（ANN）和支持向量机（SVM）模型，预测中国东部受农业 NPS 污染影响的河流中任意位置的总氮（TN）和总磷（TP）浓度。选择河流流量、水温、水流时间、降雨量、溶解氧以及上游 TN 或 TP 浓度作为两种模型的初始输入。分别选择月度、双月和季度数据集来训练两种模型，并选择未用于训练的相同月度数据集来测试模型，以比较其泛化性能。通过试错分析和遗传算法（GA）分别对 ANN 和 SVM 模型的参数进行优化。结果表明，所提出的 SVM 模型具有更好的泛化能力，因为它避免了过度训练的发生，并且基于结构风险最小化（SRM）原理优化的参数更少。此外，基于季度数据集训练的 TN 和 TP SVM 模型的预测精度均大于相应的 ANN 模型。因此，SVM 模型将是一种强大的替代方法，因为它是一种高效、经济的工具，可以在低风险的情况下准确预测水质。两种模型的敏感性分析表明，在旱季降低上游输入浓度和在平均或洪水季节减少沿程 NPS 排放应是改善昌乐河水质的有效方法。如果有必要的水质和水文数据，甚至是季度数据，那么此处开发的 SVM 方法可以很容易地应用于其他受 NPS 污染的河流。
