Application of Artificial Neural Network in Environmental ‎Water Quality Assessment

Authors
H. CHU 1
W. LU 1
L. Zhang 2
L. Zhang 2
1 Department of Hydrology and Water Resources, College of Environment and Resources, Jilin University, ‎China.‎
2 Department of Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, China.‎
Abstract
Water quality assessment provides a scientific basis for water resources development and management. This case study proposes a Factor analysis- Hopfield neural network model (FHNN) based on factor analysis method and Hopfield neural network method. The results showed that the factor analysis (FA) technique was introduced to identify important water quality parameters. Results revealed that biochemical oxygen demand, permanganate index, ammonia nitrogen, nitrogen, Cu, Zn and Pb were the most important parameters in assessing water quality variations of the study area. Considering these parameters, water samples of the sampling sites were classified as follows: six into Class III, eight into Class IV, and six into Class V. Afterwards, a water quality map was based on the results of water quality assessment by Factor analysis-Hopfield neural network model. It showed that the southwestern part of the study area had a generally optimum water quality, while in the northeastern part, the quality was seriously degraded. Factor Analysis-Hopfield Neural Network was much better than the Hopfield Neural Network in effectively reducing the degree of Hopfield neural network over-fitting caused by the inputs, thereby achieving more reasonable results. The comparisons with BPANN, fuzzy assessment method, and the Nemerow index method indicated that the FHNN model provided more reliable judgment and valuable information than the three other water quality classification methods.

Keywords

1. Akbal, F., Gürel, L., Bahadır, T., Güler, İ., Bakan, G. and Büyükgüngör, H. 2011. Multivariate Statistical Techniques for the Assessment of Surface Water Quality at the Mid-Black Sea Coast of Turkey. Water Air Soil Pollu., 216: 21–37.
2. Ali, E., Melike, G., Alpaslan, E., Cigdem, T., Aysegul, U., Dursun, Z. S., Aysegul, T. and Izzet, O. 2010. Water Quality Assessment and Meta Model Development in Melen Watershed–Turkey. J. Environ. Manage., 91(7): 1526-1545.
3. Almeida,C. A., Quintar, S., González, P. and Mallea, M. A. 2007. Influence of Urbanization and Tourist Activities on the Water Quality of the Potrero de los Funes River (San Luis-Argentina). Environ. Moni. Assess., 133: 459–465.
4. Astel, A., Tsakovski, S., Barbieri, P. and Simeonov, V. 2007. Comparison of Self-Organizing Approach with Cluster and Principal Component Analysis for Large Environmental Data Sets. Water Res., 41: 4566–4578.
5. Bordalo, A. A., Teixeira, R. and Wiebe, W. J. 2006. A Water Quality Index Applied to an International Shared River Basin: The Case of the Douro River. Environ. Manage., 38: 910–920.
6. Boyacioglu, H. 2010. Utilization of the Water Quality Index Methods A Classification Tool. Environ. Moni. Assess., 167: 115–124.
7. Capolo, M., Andreussi, P. and Soldati, A. 1999. River Filled Forecasting with a Neural Network Model. Water Resource Res., 35: 1191–1197.
8. Chapagain, S. K., Pandey, V. P., Shrestha, S., Nakamura T. and Kazama, F. 2010. Assessment of Deep Groundwater Quality in Kathmandu Valley Using Multivariate Statistical Techniques. Water Air Soil Pollu., 210: 277–288.
9. Chen, R. M. and Huang, Y. M. 2001. Competitive Neural Network to Solve Scheduling Problems. Neurocomputing, 37: 177–196.
10. Cho, K. H., Sthiannopkao, S., Pachepsky, Y. A., Kim, K. W. and Kim, J. H. 2011. Prediction of Contamination Potential of Groundwater Arsenic in Cambodia, Laos, and Thailand Using Artificial Neural Network. Water Res., 45(17): 5535-5544.
11. Ding, S. F., Jia, W. K., Su, C. Y., Zhang L. W. and Liu, L. L. 2011. Research of Neural Network Algorithm Based on Factor Analysis and Cluster Analysis. Neural Compu. Appli., 20(2): 297-302.
12. Fu, L. 1995. Neural Networks in Computer Intelligence. McGraw-Hill, New York.
13. Fulazzaky, M. A., Seong, T. W. and Masirin, M. I. M. 2010. Assessment of Water Quality Status for the Selangor River in Malaysia. Water Air Soil Pollu., 205: 63–77.
14. GB3838. 2002. Environmental Quality Standard for Surface Water. Ministry of Environmental Protection of the People’s Republic of China, China.
15. Ghasemloo, N., Mobasheri, M. R. and Rezaei, Y. 2011. Vegetation Species Determination Using Spectral Characteristics and Artificial Neural Network (SCANN). J. Agr. Sci. Tech., 13(Suppl): 1223-1232.
16. Hopfield, J. J. 1982. Neural Networks and Physical Systems with Emergent Collective Computational Abilities. Proceedings of the National Academy of Sciences of the United States of America, 79: 2554–2558.
17. Hornik K. 1991. Approximation Capability of Multi-layer Feedforward Networks. Neural Networks. 4: 241–257.
18. Hussain, M., Ahmed, S. M. and Abderrahman, W. 2008. Cluster Analysis and Quality Assessment of Logged Water at an Irrigation Project, Eastern Saudi Arabia. J. Environ. Manage., 86(1): 297–307.
19. Karul, C., Soyupak, S., Cilesiz, A. F., Akbay, N. and Germen, E. 2000. Case Studies on the Use of Neural Networks in Eutrophication Modeling. Ecological Modelling, 134: 145–152.
20. Kevin, R. J., Simon, X. Y. and Roger, R. H. 2005. Pork Farm Odor Modeling Using Multiple-Component Multiple-Factor Analysis and Neural Networks. Appl. Soft Computing, 6: 53–61.
21. Khan, F., Husain, T. and Lumb, A. 2003. Water Quality Evaluation and Trend Analysis in Selected Watersheds of the Atlantic Region of Canada. Environ. Monit. Assess., 88: 221–242.
22. Lee, K. C., Han, I. and Kwon, Y. S. 1996. Hybrid Neural Network Models for Bankruptcy Predictions. Decision Support Sys., 18: 63–72.
23. Long, T. R., Guo, J. S. and Huo, G. Y. 2002. Hopfield Network Models of Water Quality Evaluation. J. Chongqing Jianzhu Univ., 24(2): 57-60.
24. Moustris, K. P., Ziomas, I. C. and Paliatsos, A. G. 2010. 3-day-ahead Forecasting of Regional Pollution Index for the Pollutants NO2, CO, SO2, and O3 Using Artificial Neural Networks in Athens, Greece. Water Air Soil Pollu., 209: 29–43.
25. Oliveira-Esquerre, K. P., Mori, M. and Bruns, R. E. 2002. Simulation of an Industrial Wastewater Treatment Plant Using Artificial Neural Networks and Principal Components Analysis. Brazil. Jour. Chem. Engin., 21: 81–87.
26. Ouyang, Y. 2005. Evaluation of River Water Quality Monitoring Stations by Principal Component Analysis. Water Research, 39: 2621–2635.
27. Panchal, G., Ganatra, A., Kosta, Y. P. and Panchal, D. 2011. Behaviour Analysis of Multilayer Perceptrons with Multiple Hidden Neurons and Hidden Layers. Inter. Jour. Compu. Theory Engin., 3(2): 332-337.
28. Prabu, P. C., Wondimu, L. and Tesso, M. 2011. Assessment of Water Quality of Huluka and Alaltu Rivers of Ambo, Ethiopia. J. Agr. Scie. Tech., 13(1): 131-138.
29. Sun, X., Deng, J., Gong, Q. J., Wang. Q. F., Yang, L. Q. and Zhao, Z. Y. 2009. Kohonen Neural Network and Factor Analysis Based Approach to Geochemical Data Pattern Recognition. J. Geochem. Explor., 103(1): 6-16.
30. Vignolo, A., Pochettino, A. and Cicerone, D. 2006. Water Quality Assessment Using Remote Sensing Techniques: Medrano Creek, Argentina. J. Environ. Manage., 81(4): 429-433.
31. Wen, U. P., Lan, K. M. and Shih, H. S. 2009. A review of Hopfield Neural Networks for Solving Mathematical Programming Problems. Euro. Jour. Operat. Res., 198: 675–687.
32. Yaleinoz, T., Cory, B. J. and Short, M. J. 2001. Hopfield Neural Network Approaches to Economic Dispatch Problems. Inter. Jour. Elec. Power Energy Sys., 23: 435–442.
33. Yazdani, M. R., Saghafian, B., Mahdian, M. H. and Soltani, S. 2009. Monthly Runoff Estimation Using Artificial Neural Networks. J. Agr. Sci. Tech., 11(3): 355-362.
Journal of Agricultural Science and Technology
Volume 15, Issue 2
March and April 2013
Pages 343-356