A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is
J. Agr. Sci. Tech
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 concentrationsfatcat:o65ljielwjcoleiabqomltp7ke