Abstract:
Chagan Lake serves as an important ecological barri
er in western Jilin. Accurate water
quality series predictions for Chagan Lake are esse
ntial to the maintenance of water
environment security. In the present study, a hybri
d AutoRegressive Integrated Moving
Average (ARIMA) and Radial Basis Function Neural Ne
twork (RBFNN) model is used to
predict and examine the water quality [Total Nitrog
en (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 Grad
es 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 accur
ately than single ARIMA and
RBFNN model. The results serve as a theoretical bas
is for ecological and environmental
monitoring of Chagan Lake and may help guide irriga
tion district and water project
construction planning for western Jilin Province.
Keywords:
ARIMA model, Chagan Lake, RBFNN model, Total N, Tot
al P.
