Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): A comparative study

Environmental Monitoring and Assessment

Volumn 186, Issue 1, 2014, Pages 597-619

  Heddam, Salim ^a  

^a UNIVERSITY OF SKIKDA   (Algeria)

Author keywords

ANFIS; Dissolved oxygen; DO; Grid partition; Keyword: Modeling; MLR; Subtractive clustering

Indexed keywords

ANFIS; DO; GRID PARTITION; MLR; SUBTRACTIVE CLUSTERING;

DISSOLVED OXYGEN; LINEAR REGRESSION; STATISTICAL TESTS; TRACKING (POSITION);

FUZZY SYSTEMS;

DISSOLVED OXYGEN; RIVER WATER;

COMPARATIVE STUDY; CONCENTRATION (COMPOSITION); DISSOLVED OXYGEN; FUZZY MATHEMATICS; PARTITION COEFFICIENT; REGRESSION ANALYSIS;

ADAPTIVE NEURO FUZZY INFERENCE SYSTEM; ARTICLE; BIOLOGICAL MODEL; BIOSENSOR; CLUSTER ANALYSIS; COMPARATIVE STUDY; CONCENTRATION (PARAMETERS); CONDUCTANCE; FUZZY SYSTEM; INTERMETHOD COMPARISON; MULTIPLE LINEAR REGRESSION ANALYSIS; PH; RANDOMIZATION; STATISTICAL MODEL; VALIDATION PROCESS; WATER TEMPERATURE;

KLAMATH RIVER; UNITED STATES;

EID: 84891328285     PISSN: 01676369     EISSN: 15732959     Source Type: Journal    
DOI: 10.1007/s10661-013-3402-1     Document Type: Article

References (40)

1. Basant, N., Gupta, S., Malik, A., & Singh, K. P. (2010). Linear and nonlinear modeling for simultaneous prediction of dissolved oxygen and biochemical oxygen demand of the surface water-a case study. Chemometrics and Intelligent Laboratory Systems, 104, 172-180. doi: 10.1016/j.chemolab.2010.08. 005.
2. Benoit, P., Gratton, Y., & Mucci, A. (2006). Modeling of dissolved oxygen levels in the bottom waters of the Lower St. Lawrence Estuary: coupling of benthic and pelagic processes. Marine Chemistry, 102, 13-32. doi: 10.1016/j.marchem.2005.09.015.
3. Boano, F., Revelli, R., & Ridolfi, L. (2006). Stochastic modelling of DO and BOD components in a stream with random inputs. Advances in Water Resources, 29, 1341-1350. doi: 10.1016/j.advwatres.2005.10.007.
4. Chau, K. W. (2006). A review on integration of artificial intelligence into water quality modelling. Marine Pollution Bulletin, 52, 726-733. doi: 10.1016/j.marpolbul.2006.04.003.
5. Chen, D., Lu, J., & Shen, Y. (2010). Artificial neural network modelling of concentrations of nitrogen, phosphorus and dissolved oxygen in a non-point source polluted river in Zhejiang Province, southeast China. Hydrologie Continentale, 24, 290-299. doi: 10.1002/hyp.7482.
6. Chiu, S. (1994). Fuzzy model identification based on cluster estimation. Journal of Intelligent & Fuzzy Systems, 2, 267-278.
7. Chiu, S. (1996). Method and software for extracting fuzzy classification rules by subtractive clustering. In: Fuzzy Information Proceeding Society, Biennial Conference of the North American, pp. 461-465.
8. Diamantopoulou, M. J., Antonopoulos, V. Z., & Papamichail, D. M. (2007). Cascade correlation artificial neural networks for estimating missing monthly values of water quality parameters in rivers. Water Resour Manage, 21, 649-662. doi: 10.1007/s11269-006-9036-0.
9. Eftekhari, M., & Katebi, S. D. (2008). Extracting compact fuzzy rules for nonlinear system modeling using subtractive clustering, GA and unscented filter. Applied Mathematical Modelling, 32, 2634-2651. doi: 10.1016/j.apm.2007. 09.023.
10. Facchini, A., Mocenni, C., Marwan, N., Vicino, A., & Tiezzi, E. (2007). Nonlinear time series analysis of dissolved oxygen in the Orbetello Lagoon (Italy). Ecological Modelling, 203, 339-348. doi: 10.1016/j.ecolmodel. 2006.12.001.
11. Giusti, E., & Marsili-Libelli, S. (2009). Spatio-temporal dissolved oxygen dynamics in the Orbetello lagoon by fuzzy pattern recognition. Ecological Modelling, 220, 2415-2426. doi: 10.1016/j.ecolmodel.2009.06.007.
12. Heddam, S., Bermad, A., & Dechemi, N. (2012). ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study. Environmental Monitoring and Assessment, 184, 1953-1971. doi: 10.1007/s10661-011-2091-x.
13. Hull, V., Parrella, L., & Falcucci, M. (2008). Modelling dissolved oxygen dynamics in coastal lagoons. Ecological Modelling, 211, 468-480. doi: 10.1016/j.ecolmodel.2007.09.023.
14. Ishibuchi H., Nakashima T., Murata T. (1999). Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Transactions on Systems Man and Cybernetics-Part B: Cybernetics, Vol. 29, NO. 5, October. Doi: 10.1109/3477.790443.
15. Jamieson, T. S., Schiff, S. L., & Taylor, W. D. (2013). Using stable isotopes of dissolved oxygen for the determination of gas exchange in the Grand River, Ontario, Canada. Water Research, 47, 781-790. doi: 10.1016/j.watres.2012. 11.001.
16. Jang, J. S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685.
17. Jang, J. S. R., & Gulley, N. (1996). Fuzzy logic toolbox: reference manual. Natick: The MathWorks Inc.
18. Kayombo, S., Mbwette, T. S. A., Mayo, A. W., Katima, J. H. Y., & Jorgensen, S. E. (2000). Modelling diurnal variation of dissolved oxygen in waste stabilization ponds. Ecological Modelling, 127, 21-31.
19. Li-Hua, C., & Li, L. (2008). Evaluation of dissolved oxygen in water by artificial neural network and sample optimization. Journal of Central South University of Technology, 15(s2), 416-420. doi: 10.1007/s11771-008-0498-5.
20. Liu, S., Yan, M., Tai, H., Xu, L., & Li, D. (2012). Prediction of dissolved oxygen content in aquaculture of hyriopsis cumingii using elman neural network. D. Li and Y. Chen (Eds.): computer and computing technologies in agriculture V (CCTA) 2011, Part III. IFIP Advances in Information and Communication Technology, 370, 508-518. doi: 10.1007/978-3-642-27275-2-57.
21. Lohani, A. K., Goel, N. K., & Bhatia, K. K. S. (2006). Takagi-Sugeno fuzzy inference system for modeling stage-discharge relationship. Journal of Hydrology, 331, 146-160. doi: 10.1016/j.jhydrol.2006.05.007.
22. Mandal, S., Debnath, M., Ray, S., Ghosh, P. B., Roy, M., & Ray, S. (2012). Dynamic modelling of dissolved oxygen in the creeks of Sagar Island, Hooghly-Matla estuarine system, West Bengal, India. Applied Mathematical Modelling, 36, 5952-5963. doi: 10.1016/j.apm.2011.10.013.
23. Martin, N., McEachern, P., Yu, T., & Zhu, D. Z. (2013). Model development for prediction and mitigation of dissolved oxygen sags in the Athabasca River, Canada. Science of the Total Environment, 443, 403-412. doi: 10.1016/j.scitotenv.2012.10.030.
24. Misra, A. K. (2011). Modeling the depletion of dissolved oxygen due to algal bloom in a lake by taking Holling type-III interaction. Applied Mathematics and Computation, 217, 8367-8376. doi: 10.1016/j.amc.2011.03.034.
25. Misra, A. K., Chandra, P., & Raghavendra, V. (2011). Modeling the depletion of dissolved oxygen effect of time delay. Advances in Water Resources, 34, 1232-1238. doi: 10.1016/j.advwatres.2011.05.010.
26. Money, E., Carter, G. P., & Serre, M. L. (2009). Using river distances in the space/time estimation of dissolved oxygen along two impaired river networks in New Jersey. Water Research, 43, 1948-1958. doi: 10.1016/j.watres.2009.01.034.
27. Nakova, E., Linnebank, F. E., Bredeweg, B., Salles, P., & Uzunov, Y. (2009). The river MESTA case study: a qualitative model of dissolved oxygen in aquatic ecosystems. Ecological Informatics, 4, 339-357. doi: 10.1016/j.ecoinf.2009.09.015.
28. Nayak, P. C., Sudheer, K. P., Rangan, D. M., & Ramasastri, K. S. (2005). Short-term flood forecasting with a neurofuzzy model. Water Resources Research, 41, W04004. doi: 10.1029/2004WR003562.
29. Pinto, U., Maheshwari, B., Shrestha, S., & Morris, C. (2012). Modelling eutrophication and microbial risks in peri-urban river systems using discriminant function analysis. Water Research, 46, 6476-6488. doi: 10.1016/j.watres.2012.09.025.
30. Radwan, M., Willems, P., El-Sadek, A., & Berlamont, J. (2003). Modelling of dissolved oxygen and biochemical oxygen demand in river water using a detailed and a simplified model. International Journal of River Basin Management, 1(2), 97-103. doi: 10.1080/15715124.2003.9635196.
31. Ranković, V., Radulović, J., Radojević, I., Ostojić, A., & Ćomić, L. (2010). Neural network modeling of dissolved oxygen in the Grua reservoir, Serbia. Ecological Modelling, 221, 1239-1244. doi: 10.1016/j.ecolmodel.2009.12.023.
32. Rounds, S.A., Sullivan, A.B. (2009). Review of Klamath River total maximum daily load models from Link River Dam to Keno Dam, Oregon: U.S. Geological Survey Administrative Report, 37P. http://www.usbr.gov/mp/kbao/ waterquality/klamath-river-model-review-final.pdf.
33. Singaraja, C., Chidambaram, S., Prasanna, M. V., Paramaguru, P., Johnsonbabu, G., Thivya, C., et al. (2011). A study on the behavior of the dissolved oxygen in the shallow coastal wells of Cuddalore District, Tamilnadu, India. Water Quality Exposure Health. doi: 10.1007/s12403-011-0058-3.
34. Stefan, H. G., & Fang, X. (1994). Dissolved oxygen model for regional lake analysis. Ecological Modelling, 71, 37-68.
35. Sullivan, A.B., Rounds, S.A., Deas, M.L., and Sogutlugil, I.E. (2012). Dissolved oxygen analysis, TMDL model comparison, and particulate matter shunting-preliminary results from three model scenarios for the Klamath River upstream of Keno Dam, Oregon: U.S. Geological Survey Open-File Report 2012-1101, 30 p.
36. USGS (2013). http://ga.water.usgs.gov/edu/dissolvedoxygen.html.
37. Wei, M., Bai, B., Sung, A. H., Liu, Q., Wang, J., & Cather, M. E. (2007). Predicting injection profiles using ANFIS. Information Sciences, 177, 4445-4461. doi: 10.1016/j.ins.2007.03.021.
38. Williams, R. J., & Boorman, D. B. (2012). Modelling in-stream temperature and dissolved oxygen at sub-daily time steps: an application to the River Kennet, UK. Science of the Total Environment, 423, 104-110. doi: 10.1016/j.scitotenv.2012.01.054.
39. Yager, R. R., & Filev, D. P. (1994). Approximate clustering via the mountain method. IEEE Transactions on Systems, Man, and Cybernetics, 24(8), 1279-1284.
40. Yu, W., & Li, X. (2009). Online fuzzy modeling with structure and parameter learning. Expert Systems with Applications, 36, 7484-7492. doi: 10.1016/j.eswa.2008.09.016.