Water Quality Analysis and Prediction Using Hybrid Time Series and Neural Network Models

Chagan Lake serves as an important ecological barrier in western Jilin. Accurate water quality series predictions for Chagan Lake are essential to the maintenance of water environment security. In the present study, a hybrid AutoRegressive Integrated Moving Average (ARIMA) and Radial Basis Function Neural Network (RBFNN) model is used to predict and examine the water quality [Total Nitrogen (TN), and Total Phosphorus (TP)] of Chagan Lake. The results reveal the following: (1) TN concentrations in Chagan Lake increased slightly from 2006 to 2011, though yearly variations in TP were not significant. The TN and TP levels were mainly classified as Grades IV and V, (2) The hybrid ARIMA and RBFNN model's RMSE values for the observed and predicted data were 0.139 and 0.036 mg L-1 for TN and TP, respectively, which indicated that the hybrid model describes TN and TP variations more comprehensively and accurately than single ARIMA and RBFNN model. The results serve as a theoretical basis for ecological and environmental monitoring of Chagan Lake and may help guide irrigation district and water project construction planning for western Jilin Province.