title: Support vector machine-an alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river?
abstract: 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.
authors.affiliation: Department of Natural Resources, College of Environment and Natural Resources, Zhejiang University, 866 Yu Hang Tang Road, Hangzhou, 310058, Zhejiang Province, China, liumei@zju.edu.cn.
authors.forename: Mei
authors.initials: M
authors.lastname: Liu
authors.forename: Jun
authors.initials: J
authors.lastname: Lu
mesh_terms: D000383:Agriculture; D002681:China; D003198:Computer Simulation; D004784:Environmental Monitoring / Q000379:methods; D004787:Environmental Pollution / Q000032:analysis; D016571:Neural Networks, Computer; D009584:Nitrogen / Q000032:analysis; D010758:Phosphorus / Q000032:analysis; D045483:Rivers / Q000737:chemistry*; D012621:Seasons; D060388:Support Vector Machine; D014874:Water Pollutants, Chemical / Q000032:analysis*; D014877:Water Pollution, Chemical / Q000032:analysis*; D060753:Water Quality
chemical_list: D014874:Water Pollutants, Chemical; D010758:Phosphorus; D009584:Nitrogen