A reduced-order adaptive neuro-fuzzy inference system model as a software sensor for rapid estimation of five-day biochemical oxygen demand

Journal of Hydrology

Volumn 495, Issue , 2013, Pages 175-185

  Noori, Roohollah ^a   Safavi, Salman ^a   Nateghi Shahrokni, Seyyed Afshin ^a  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

Adaptive neuro fuzzy inference system; Five day biochemical oxygen demand (BOD5); Proper orthogonal decomposition; Reduced order model; Uncertainty analysis

Indexed keywords

ACCURACY ANALYSIS; ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; FIVE-DAY BIOCHEMICAL OXYGEN DEMAND (BOD5); MONTE-CARLO SIMULATIONS; PEARSON CORRELATION COEFFICIENTS; PROPER ORTHOGONAL DECOMPOSITIONS; REDUCED ORDER MODELS; ROOT MEAN SQUARE ERRORS;

BIOCHEMICAL OXYGEN DEMAND; CORRELATION METHODS; FUZZY SYSTEMS; INTELLIGENT SYSTEMS; MEAN SQUARE ERROR; PRINCIPAL COMPONENT ANALYSIS; TRACKING (POSITION); WATER CONSERVATION; WATER MANAGEMENT; WATER SUPPLY;

UNCERTAINTY ANALYSIS;

ADAPTIVE MANAGEMENT; BIOCHEMICAL OXYGEN DEMAND; FUZZY MATHEMATICS; NUMERICAL MODEL; PREDICTION; SOFTWARE; THRESHOLD; WATER QUALITY;

GILAN; IRAN; SEFIDROOD BASIN;

EID: 84878898613     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2013.04.052     Document Type: Article

References (48)

1. Abbaspour K.C., Yang J., Maximov I., Siber R., Bogner K., Mieleitner J., Zobrist J., Srinivasan R. Modeling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. J. Hydrol. 2007, 333:413-430.
2. Aqil M., Kita I., Yano A., Nishiyama S. Analysis and prediction of flow from local source in a river basin using a neuro-fuzzy modeling tool. J. Environ. Manage. 2007, 85:215-223.
3. Beltran J.L., Ferrer R., Guiteras J. Multivariate calibration of polycyclic aromatic hydrocarbon mixtures from excitation-emission fluorescence spectra. Anal. Chim. Acta 1998, 373:311-319.
4. Brown M., Harris C. Neuro-Fuzzy Adaptive Modeling and Control 1994, Prentice-Hall, New Jersey. second ed.
5. Chang F.J., Chang Y.T. Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv. Water Resour. 2006, 29:1-10.
6. Chau K.W., Wu C.L., Li Y.S. Comparison of several flood forecasting models in Yangtze River. J. Hydrol. Eng. 2005, 10:485-491.
7. Chen W., Chau K.W. Intelligent manipulation and calibration of parameters for hydrological models. Int. J. Environ. Pollut. 2006, 28:432-447.
8. Chiang Y.M., Chang L.C., Tsai M.J., Wang Y.F., Chang F.J. Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks. Hydrol. Earth Syst. Sci. 2011, 15:185-196.
9. Chiu S.L. Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. 1994, 2:267-278.
10. Coulibaly P., Anctil F., Bobee B. Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J. Hydrol. 2000, 230:244-257.
11. Dogan E., Ates A., Yilmaz E.C., Erem B. Application of artificial neural networks to estimate wastewater treatment plant inlet biochemical oxygen demand. Environ. Prog. Sustain. 2008, 27:439-446.
12. Dogan E., Sengorur B., Koklu R. Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique. J. Environ. Manage. 2009, 90:1229-1235.
13. Dybowski, R., 1997. Assigning Confidence Intervals to Neural Network Predictions. Technical Report. Division of Infection (St Thomas' Hospital), King's College, London, UK.
14. Einax J.W., Kampe O., Truckenbrodt D. Assessing the deposition and remobilization behavior of metals between river water and river sediment using partial least squares regression. Freshwater J. Anal. Chem. 1998, 361:149-154.
15. Einax J.W., Aulinger A., Tumpling W.V., Prange A. Quantitative description of element concentrations in longitudinal river profiles by multiway PLS models. Freshwater J. Anal. Chem. 1999, 363:655-661.
16. Esfahanian V., Ashrafi K. Equation-Free/Galerkin-Free reduced-order modeling of the shallow water equations based on proper orthogonal decomposition. J. Fluid Eng. 2009, 131:1-13.
17. Finnoff W., Hergert F., Zimmermann H.G. Improving model selection by nonconvergent methods. Neural Networks 1993, 6:771-783.
18. Hamed M.M., Khalafallah M.G., Hassanien E.A. Prediction of wastewater treatment plant performance using artificial neural networks. Environ. Modell. Softw. 2004, 19:919-928.
19. Jain A., Indurthy S.K.V.P. Comparative analysis of event based rainfall-runoff modeling techniques-deterministic, statistical, and artificial neural networks. J. Hydrol. Eng. 2003, 8:93-98.
20. Jang J.S.R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transact. Syst. Man Cybernet. 1993, 23:665-685.
21. Jang J.S.R., Sun C.T. Neuro-fuzzy modeling and control. Proceed. IEEE 1995, 83:378-406.
22. Kecman V. Support vector machines: an introduction. Support Vector Machines: Theory and Applications 2005, Springer-Verlag, New York. L. Wang (Ed.).
23. Kisi O., Ozturk O. Adaptive neurofuzzy computing technique for evapotranspiration estimation. J. Irrig. Drain. E (ASCE) 2007, 133:368-379.
24. Marce R., Comerma M., Garcia J.C., Armengol J. A neuro-fuzzy modeling tool to estimate fluvial nutrient loads in watersheds under time-varying human impact. Limnol. Oceanogr.-Meth. 2004, 2:342-355.
25. Moghaddamnia A., Remesan R., Hassanpour Kashani M., Mohammadi M., Han D., Piri J. Comparison of LLR, MLP, Elman, NNARX and ANFIS Models-with a case study in solar radiation estimation. J. Atm. Sol.-Terr. Phys. 2009, 71:975-982.
26. Moghaddamnia A., Ghafari Gousheh M., Piri J., Amin S., Han D. Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv. Water Resour. 2009, 32:88-97.
27. 5 monitoring in river systems: active and online prediction. Environ. Prog. Sustain. 2011, 10.1002/ep.10611.
28. 5 in river systems using reduced-order support vector machine. Environ. Earth Sci. 2011, 10.1007/s12665-011-1487-9.
29. Noori R., Karbassi A.R., Moghaddamnia A., Han D., Zokaei-Ashtiani M.H., Farokhnia A., Ghafari-Gousheh M. Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction. J. Hydrol. 2011, 401:177-189.
30. Oliveira-Esquerre K.P., Mori M., Bruns R.E. Simulation of an industrial wastewater treatment plant using artificial neural networks and principal component analysis. Braz. J. Chem. Eng. 2002, 19:365-370.
31. Oliveira-Esquerre K.P., Seborg D.E., Bruns R.E., Mori M. Application of steady-state and dynamic modeling for the prediction of the BOD of an aerated lagoon at a pulp and paper mill Part I. Linear approaches. Chem. Eng. J. 2004, 104:73-81.
32. Oliveira-Esquerre K.P., Seborg D.E., Mori M., Bruns R.E. Application of steady-state and dynamic modeling for the prediction of the BOD of an aerated lagoon at a pulp and paper mill Part II. Nonlinear approaches. Chem. Eng. J. 2004, 105:61-69.
33. Onkal-Engin G., Demir I., Engin S.N. Determination of the relationship between sewage odourand BOD by neural networks. Environ. Modell. Softw. 2005, 20:843-850.
34. Ouyang Y., Nkedi-Kizza P., Wu Q.T., Shinde D., Huang C.H. Assessment of seasonal variations in surface water quality. Water Res. 2006, 40:3800-3810.
35. Ravindran S.S. A reduced-order approach for optimal control of fluids using proper orthogonal decomposition. Int. J. Numer. Methods Fluids 2000, 34:425-448.
36. Rustum R., Adeloye A.J., Scholz M. Applying Kohonen self-organizing map as a software sensor to predict biochemical oxygen Demand. Water Environ. Res. 2008, 80:32-40.
37. Shrestha S., Kazama F. Assessment of surface water quality using multivariate statistical techniques: a case study of the Fuji river basin, Japan. Environ. Modell. Softw. 2007, 22:464-475.
38. Singh K.P., Malik A., Sinha S. Water quality assessment and apportionment of pollution sources of Gomti river (India) using multivariate statistical techniques: a case study. Anal. Chim. Acta 2005, 538:355-374.
39. Singh K.P., Basant A., Malik A., Jain G. Artificial neural network modeling of the river water quality-A case study. Ecol. Model. 2009, 220:888-895.
40. Singh K.P., Basant N., Malik A., Jain G. Modeling the performance of "up-flow anaerobic sludge blanket" reactor based wastewater treatment plant using linear and nonlinear approaches - a case study. Anal. Chim. Acta 2010, 658:1-11.
41. Singh K.P., Gupta S., Kumar A., Shukla S.P. Linear and nonlinear modeling approaches for urban air quality prediction. Sci. Total Environ. 2012, 426:244-255.
42. Tibshirani, R., 1994. A comparison of some error estimates for neural network models. Technical Working Paper No. 94-10. Department of Statistics, University of Toronto.
43. Vega M., Pardo R., Barrado E., Deban L. Assessment of seasonal and polluting effects on the quality of river water by exploratory data analysis. Water Res. 1998, 32:3581-3592.
44. Wang W.C., Cheng C.T., Chau K.W., Xu D.M. Calibration of Xinanjiang model parameters using hybrid genetic algorithm based fuzzy optimal model. J. Hydroinform. 2012, 14:784-799.
45. Wu C.L., Chau K.W., Li Y.S. Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques. Water Resour. Res. 2009, 10.1029/2007WR006737.
46. Yel E., Yalpir S. Prediction of primary treatment effluent parameters by Fuzzy Inference System (FIS) approach. Procedia Comput. Sci. 2011, 3:659-665.
47. Zhao L., Chai T. Wastewater BOD forecasting model for optimal operation using robust time-delay neural network. Lect. Notes Comput. Sci. 2005, 3498:1028-1033.
48. Zhu J., Zurcher J., Rao M., Meng M.Q.H. An on-line wastewater quality predication system based on a time-delay neural network. Eng. Appl. Artif. Intell. 1998, 11:747-758.