title: [Establishment of the predictive model of source eutrophication using artificial neural network].
abstract: OBJECTIVE
  To establish the predictive model of eutrophication in the main water source in Zhengzhou City.
  
  
  METHODS
  Water temperature (WT), secchi-depth (SD), total phosphorus (TP), total nitrogen (TN), light illuminance (LI), chemical oxygen demand (CODMn), chlorophyll-a (Chla) were monitored in Xiliu lake and Huayuankou pool. Grading points method and comprehensive trophic state index method were used to evaluate the trophic state. Backpropagation artificial neural network with Levenberg-Marquardt algorithm was used to establish the forcasting model of eutrophication after the raw data normalized treated using standardization function.
  
  
  RESULTS
  The results of evaluation of grade method revealed that the two waters source were in nutritional state and the tendencies of year grade indexes were from the lower critical value to eutrophic state to higher critical value of eutrophication of xiliu lake. The scope of hidden nodes was determined from 2 to 15 according to the calculated results using function of J = (mean square root of n + M) + a and the hidden nodes was 10 according to the training result. All of the physical chemistry factors were brought into the model. The training error was 1e-11 and the coefficient correlation of the network fitness result was 0.871. The fitting result was close to the aim and the predictive model of eutrophication in the main resource water in Zhengzhou City was established successfully.
  
  
  CONCLUSIONS
  Eutrophication forcasting model could be established using artificial neural network, and the method of artificial neural network should be better to meet the actual demand.
authors.affiliation: Department of Environmental Health, College of Public Health, Zhengzhou University, Zhengzhou 450001, China.
authors.forename: Songqin
authors.initials: S
authors.lastname: Yang
authors.forename: Huizhen
authors.initials: H
authors.lastname: Zhang
authors.forename: Yue
authors.initials: Y
authors.lastname: Ba
authors.forename: Xuemin
authors.initials: X
authors.lastname: Cheng
mesh_terms: D002681:China; D004784:Environmental Monitoring / Q000379:methods*; D005068:Eutrophication; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D014874:Water Pollutants, Chemical / Q000032:analysis*; D014881:Water Supply / Q000032:analysis
chemical_list: D014874:Water Pollutants, Chemical