Ranking water quality variables using feature selection algorithms to improve generalization capability of artificial neural networks

Scientific Research and Essays

Volumn 5, Issue 11, 2010, Pages 1254-1259

  Samandar, Ayhan ^a  

^a DUZCE UNIVERSITY   (Turkey)

Author keywords

Artificial neural networks; Melen river; Ranking; Water quality

Indexed keywords

EID: 77954404500     PISSN: None     EISSN: 19922248     Source Type: Journal    
DOI: None     Document Type: Article

References (27)

1. Altun H, Bilgil A, Fidan BC (2007). Treatment of multi-dimensional data to enhance neural network estimators in regression problem, Expert Syst. Appl. 32(2): 599-605.
2. Altun H, Bilgil A, Fidan BC (2007). Treatment of skewed multidimensional training data to facilitate the task of engineering neural models, Expert Systems with Applications 33(4): 978-983.
3. Altun H, Polat G (2007). Boosting Selection of Speech Related Features to Improve Performance of Multi-class SVMs in Emotion Detection, Expert Syst. Appl. 36(1): 8197-8203.
4. Chapra SC (1997). Surface Water Quality Modelling, McGraw-Hill International Edition p. 843
5. Cox BA (2003). A review of currently available in-stream water-quality models and their applicability for simulating dissolved oxygen in lowland rivers. Sci. Total Environ. pp. 314-316, 335-377.
6. Grubert JP (1995). Application of neural networks in strafed flow stability analysis. J. Hydraulic Eng. 12: 523-532.
7. Gümrükçüoǧlu M, Baştürk O (2007). Determination of Waste Load in Sakarya River by Using Geographical Information Systems, National Geographical Information Systems Congress, (in Turkish) KTÜ, Trabzon/Turkey.
8. Guyon I, Elisseeff A (2003). An introduction to variable and feature selection. J. Mach. Learn. Res. 3: 1157-1182.
9. Kannel PR, Lee S, Lee YS, Kanel SR, Pelletier GJ (2007). Application of automated QUAL2Kw for water quality modeling and management in the Bagmati River, Nepal Ecologigal Modelling pp. 503-517.
10. Karul C, Soyupak S, Çilesiz AF, Akbay N, ve Germen E (2000). Case studies on the use of neural networks in eutrophication modeling, Ecol. Mod. 134: 145-153.
11. Karunanithi N, Grenney WJ, Whitly D, Bovee K (1994). Noural networks for river low prediction. J. Comput. Civ. Eng. pp. 8: 201-220.
12. Minns AW (1998). Artifical neural networks as subsymbolic process descriptors, Belkama, Rotterdam, The Netherlands. neural network approach for modelling and prediction of algal blooms, Ecol. Mod. pp. 96: 11-28.
13. Nagy HM, Watanabe K, Hirano M (2002). Prediction of sediment load concentration in rivers using artificial neural network model, J. Hydraulic. Eng. 128: 588-595.
14. Recknagel F, French M, Harkonen P, ve Yabunaka K (1997). Artificial
15. Şamandar A (2004), Water Quality Modelling in Big Melen River and Its Tributaries, Ph.D. Thesis. SAU, Sakarya, Turkey.
16. Sanchez L, Arroyo V, Garcia J, Koev K, Revilla J (1998). Use of neural networks in design of coastal sewage system. J. Hydraulic Eng. pp. 124: 457-464.
17. Scardi M (1996). Artificial neural networks as empirical models for estimating phytoplankton production, Mar. Ecol. Ser. pp. 139: 289-299.
18. Sengorur B, Dogan E, Koklu R, Samandar A (2006). Dissolved Oxygen Estimation Using Artificial Neural Network For Water Quality Control, Fresenius Environ. Bull. pp. 15: 1064-1067.
19. Şenörür B, Koklu R, Şamandar A, Polat H (2008). Evalation of Water Quality in Melen River, World Water Congress and Exhibition, Austria
20. Shiklomanov IA (1999). Water Transfer As One of the Most Important Ways to Eliminate Water Resources Deficits and to Solve Water Management Problems, Proceedings of the International Interbasin Water Transfers Workshop, Paris, April, pp. 203-208.
21. Soyupak S, Karaer F, Gürbüz H, Kivrak E, Şentürk E, ve Yazici A (2003). A neural network based approach for calculating dissolved oxygen profiles in reservoirs, Neural Comput. Appl. pp. 12: 166-172.
22. Soyupak S, ve Chen DG (2004). Fuzzy logic model to estimate pseudo steady state chlorophyll-a concentrations in reservoirs, Environ. Mod. Ass. 9(1): 51-59.
23. Soyupak S, Yazici A, Qilesiz AF, ve Akbay N (2001). A neural network based model for estimating dissolved oxygen in a very deep reservoir, The 16th Int. Symp. On Computer and Inf. Sciences, ISCIS XVI, November 5-7, Antalya, Turkey.
24. Sümer B, Ileri R, Şamandar A, Şengörür B (2001), Water Quality in Melen River and Its Brunches, J. Ecol. Environ. (in Turkish) 10(39): 13-18.
25. Wilson HEC (2004). Short term forecasting of algal blooms in drinking water reservoirs using artificial neural networks, A PhD Thesis submitted to School of Earth and Environmental Sciences, The University of Adelaide, Australia.
26. Yabunaka K, Hosomi M, ve Murakami A (1997). Novel application of a back-propagation artificial neural network model formulated to predict algal bloom, Wat. Res, pp. 36: 89-97.
27. Yüceer A, ve i̇nkayali NG (2004). Investigation of Water Quality of Lower Seyhan River by QUAL2E Model, SKKD, (in Turkish) 14(3): 1-8.