An application of different artificial intelligences techniques for water quality prediction

International Journal of Physical Sciences

Volumn 6, Issue 22, 2011, Pages 5298-5308

  Najah, A ^a   El Shafie, A ^a   Karim, O A ^a   Jaafar, O ^a   El Shafie, Amr H ^b  

^a UNIVERSITI KEBANGSAAN MALAYSIA   (Malaysia)

^b Faculty of Engineering   (Malaysia)

Author keywords

Artificial intelligence; Water quality prediction model

Indexed keywords

EID: 80054860156     PISSN: 19921950     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (30)

1. Chen Q, Mynett AE (2003). Integration of data mining techniques and heuristic knowledge in fuzzy logic modelling of eutrophication in Taihu Lake. Ecol. Modell. 162 (1/2), 55-67.
2. Chiewchanwattana S, Lursinsap C, Chu CH (2002). Time series Data Prediction Based on Reconstruction of Missing Samples and Selective Ensembling of FIR Neural Networks, Proceedings Of The 9th International Conference on Neural Information Processing.
3. Drucker H, Cortes C, Jackel D (1994). Boosting and Other Ensemble Methods. Neural Computation, pp. 1289-1301.
4. El-shafie AE, Noureldin M, Taha R, Basri H (2008). Neural network model for Nile River inflow forecasting analysis of historical inflow data. J. Appl. Sci., 8 (24): 4487-4499.
5. El-shafie A, Mukhlisin M, Najah AA, Taha MR (2011). Performance of artificial neural network and regression techniques for rainfall-runoff prediction. Int. J. Phys. Sci. 6(8):1997-2003, 18 April.
6. Evgeniou T, Pontil M, Poggio T (2000). Regularization networks and support vector machines, Advances in Computational Mathematics, 13(1): 1-50.
7. Freeman JA, Skapura (1991). Neural networks algorithms, applications, and programming techniques. Addison-Wesley DM.
8. Grubert JP (2003). Acid deposition in the eastern United States and neural network predictions for the future. J. Environ. Eng. Sci. 2 (2): 99-109.
9. Heathcote IW (1998). Integrated Watershed Management: Principles and Practices, John Wiley & Sons, Inc. New York.
10. Hornik K, White H (1989). Multilayer feed forward networks are universal approximators. Neural Netw., 2:359-366.
11. Kasabov NK (1996). Foundations of neural networks, fuzzy systems, and knowledge engineering MIT Press Cambridge.
12. Lee JHW, Huang Y, Dickmen M, Jayawardena AW (2003). Neural network modelling of coastal algal blooms. Ecological Modelling 159:179-201.
13. Lee JHW, Wong KTM, Huang Y, Jayawardena AW (2000). A real time early warning and modeling system for red tides in Hong Kong. In: Wang, Z.Y. (Ed.), Proceedings of the Eighth International Symposium on Stochastic Hydraulics. Balkema, Beijing, pp. 659-669.
14. Lek S, Delacoste M, Baran P, Dimopoulos I, Lauga J, Aulagnier S (1996). Application of neural networks to modeling nonlinear relationships in ecology. Ecol. Model 90:39-52.
15. Liong SY, Lim WH, Paudyal G (1999). Real time river stage forecasting for flood Bangladesh: neural network approach. J. Comput. Civil Eng. ASCE 14 (1): 1-8.
16. Najah A, Elshafie A, Karim OA, Jaffar O (2009). Prediction of Johor River water quality parameters using artificial neural networks. Eur. J. Sci. Res., 28(3):422-435 ISSN 1450-216X
17. Najah, A., El-Shafie A, Karim OA, Jaffer O (2010a). Water Quality Prediction Model Utilizing Integrated Wavelet-ANFIS Model with Cross Validation. Neural Computing and Applications J. DOI: 10.1007/s00521-010-0486-1
18. Najah AA, El-Shafie A, Karim OA, Jaafar O (2010b). Water Quality Prediction Model Utilizing Integrated Wavelet-ANFIS Model with Cross Validation, Neural. Comput. Appl., in press, doi: 10.1007/s00521-010-0486-1.
19. Najah A, Elshafie A, Karim OA, Jaffar O (2010c). Evaluation the efficiency of radial basis function neural network for prediction of water quality parameters. Eng. Int. Syst. 4: 221-231.
20. Nash JE, Sutcliffe JV (1970). River flow forecasting through conceptual models. Part 1: a discussion of principles. J. Hydrol. 10(3): 282-290.
21. Muller KR, Smola A, Ratch G, Scholkopf B, Kohlmorgen J, Vapnik V (2000). Using Support Vector Support Machines for Time Series Prediction, Image Processing Services Research Lab, AT&T Labs.
22. Muttil, N, Chau, KW (2006). Neural network and genetic programming for modelling coastal algal blooms. Int. J. Environ. Pollut., 28 (3/4): 223-238.
23. Makarynskyy O (2004). Improving wave predictions with artificial neural networks. Ocean Eng., 31: 709-724.
24. Masters T (1993). Practical Neural Network Recipes in C ++. San Diego: Academic Press.
25. Olden JD, Jackson DA (2001). Fish-habitat relationships in lakes: gaining predictive and explanatory insight by using artificial neural networks. Trans. Am. Fish. Soc., 130: 878-897.
26. Razavi SV, Jumaat MZ, Ahmed H EI-Shafie, Mohammadi P (2011a). General regression neural network (GRNN) for the first crack analysis prediction of strengthened RC one-way slab by CFRP Int. J. Phys. Sci., 6(10): 2439-2446
27. Razavi SV, Jumaat MZ, Ahmed H EI-Shafie (2011b). Using feedforward back propagation (FFBP) neural networks for compressive strength prediction of lightweight concrete made with different percentage of scoria instead of sand. Int. J. Phys. Sci., 6(6): 1325-1331
28. Rumelhart D, Hinton GE, Williams RJ (1986). Learning internal representations by error propagation, in parallel Distributed Processing vol. 1 chap. 8, ed by Rumelhart DE JL, McCelland, MIT Press, Cambridge, Mass.
29. Reckhow KH (1999). Water quality prediction and probability network models. Can. J. Fish. Aquat. Sci., 56: 1150-1158.
30. Vapnik VN (1995). The Nature of Statistical Learning Theory. Springer, New York.