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

Authors
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, People’s Republic of China.
Abstract
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.

Keywords


1. A, R. H. and En, H. 2003. The Second Songhua River Diversion Project Record of Qian Gorlos Mongol Autonomous County. Liaoning Minorities Press, Liaoning. (in Chinese)
2. Abaurrea, J., Asín, J., Cebrián, A. C. and García-Vera, M. A. 2011. Trend Analysis of Water Quality Series Based on Regression Models with Correlated Errors. J. Hydrol., 400 (3): 341–352.
3. Ahmad, S., Khan, I. H. and Parida, B. P. 2001. Performance of Stochastic Approaches for Forecasting River Water Quality. Water Resour., 35: 4261-4266.
4. Al-Alawi, S. M., Abdul-Wahab, S. A. and Bakheit, C. S. 2008. Combining Principal Component Regression and Artificial Neural Networks for More Accurate Predictions of Ground-Level Ozone. Environ. Model. Softw., 23(4): 396-403.
5. Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. 1994. Time Series Analysis, Forecasting and Control. Prentice Hall, Englewood Cliffs, NJ.
6. Cryer, J. D., Chan, K. S. 2008. Time Series Analysis: With Applications in R, second ed. Springer, New York.
7. Dai, X. J. and Tian, W. 2011. Analysis and Countermeasures on Water Pollution of Lake Chagan. J. Arid Land Resour. Environ., 25(8): 179-184. (in Chinese)
8. Duan, H., Zhang, Y., Zhang, B., Song, K., Wang Z., Liu, D. and Li, F. 2008. Estimation of Chlorophyll-a Concentration and Trophic States for Inland Lakes in Northeast China from Landsat TM Data and Field Spectral Measurements. Int. J. Remote Sens., 29:767–786.
9. GB3838-2002 of China P. R. 2002. Environmental Quality Standard for Surface Water. China: Environmental Science. Ministry of Environmental Protection of the People’s Republic of China, China.
10. Karmakar, S. and Mujumdar, P. P. 2006. Grey Fuzzy Optimization Model for Water Quality Management of a River System. Adv. Water Resour., 29: 1088–1105.
11. Makridakis, S., Wheelwright, S. C. and Megee, V. E. 1986. Forecasting: Method and Application. Second Edition, Wiley, New York.
12. May, R. J., Dandy, G. C., Maier, H. R., Nixon, J. B. 2008. Application of Partial Mutual Information Variable Selection to ANN Forecasting of Water Quality in Water Distribution Systems. Environ. Modell. Softw., 23(10–11): 1289-1299.
13. Moody, J. and Darken, C. 1989. Fast Learning in Networks of Locally Tuned Processing Units. Neural Comput., 1: 281–294.
14. Parviz, L., Kholghi, M. and Hoorfar, A. 2010. A Comparison of the Efficiency of Parameter Estimation Methods in the Context of Stream Flow Forecasting. J. Agr. Sci. Tech., 12(1): 47-60.
15. Shen, J. L. and Zhang, J. L. 2009. Ecosystem Research of Chaganhu Conservation Zone Based on Remote Sensing Data and Object-oriented Classification. J. Earth Sci. Environ., 31(2): 212-215. (in Chinese)
16. Xu, L. Q. and Liu, S. Y. 2013. Study of Short-term Water Quality Prediction Model Based on Wavelet Neural Network. Math. Comput. Model., 58(3-4): 807–813.
17. Zhang, G. P. 2003. Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model. Neurocomput., 50: 159-175.
18. Zhu, L. L., Yan, B. X.,•Wang, L. X. and Pan, X. 2012. Mercury Concentration in the Muscle of Seven Fish Species from Chagan Lake, Northeast China. Environ.Monit. Assess., 184: 1299-1310.