Intermittent Streamflow Forecasting by Using Several Data Driven Techniques

Water Resources Management

Volumn 26, Issue 2, 2012, Pages 457-474

  Kisi, Ozgur ^a   Nia, Alireza Moghaddam ^b   Gosheh, Mohsen Ghafari ^b   Tajabadi, Mohammad Reza Jamalizadeh ^b   Ahmadi, Azadeh ^c  

^a ERCIYES UNIVERSITY   (Turkey)

^b UNIVERSITY OF ZABOL   (Iran)

^c ISFAHAN UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

ANFIS; Forecast; Intermittent streamflows; Neural networks; Support vector machine

Indexed keywords

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; ANFIS; COMPARISON RESULT; CORRELATION COEFFICIENT; DATA DRIVEN TECHNIQUE; FORECAST; IRRIGATION SYSTEMS; LOCAL LINEAR REGRESSION; ROOT MEAN SQUARE ERRORS; STREAMFLOW FORECASTING; SUPPORT VECTOR; SVM MODEL;

FORECASTING; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; LUNAR SURFACE ANALYSIS; MEAN SQUARE ERROR; NEURAL NETWORKS; QUALITY MANAGEMENT; STREAM FLOW; SUPPORT VECTOR MACHINES; WATER CONSERVATION; WATER MANAGEMENT; WATER QUALITY; WATER SUPPLY;

INFORMATION MANAGEMENT;

ARTIFICIAL NEURAL NETWORK; FLOW MODELING; FORECASTING METHOD; FUZZY MATHEMATICS; PERFORMANCE ASSESSMENT; STREAMFLOW;

THRACE;

EID: 84855232291     PISSN: 09204741     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11269-011-9926-7     Document Type: Article

References (53)

1. Aksoy H, Bayazit M (2000) A daily intermittent streamflow simulator. Turk J Eng Environ Sci 24: 265-276.
2. Antar MA, Elassiouti I, Allam MN (2006) Rainfall-runoff modelling using artificial neural networks technique: a Blue Nile catchment case study. Hydrol Process 20: 1201-1216.
3. Bhadra A, Bandyopadhyay A, Singh R, Raghuwanshi NS (2010) Rainfall-runoff modeling: comparison of two approaches with different data requirements. Water Resour Manage 24: 37-62.
4. Boser BE, Guyon IM, Vapnik V (1992) A training algorithm for optimal margin classifiers. In: Haussler D (ed) 5th annual ACM workshop on COLT. ACM Press, Pittsburgh, pp 144-152.
5. Chang F-J, Chen Y-C (2001) A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction. J Hydrol 245: 153-164.
6. Chang CC, Lin CJ (2004) LIBSVM-a library for support vector machines. Available at: http://www. csie. ntu. edu. tw/~cjlin/libsvm/index. html.
7. Chau KW, Wu CL, Li YS (2005) Comparison of several flood forecasting models in Yangtze River. J Hydrol Eng ASCE 10(6): 485-491.
8. Cheng CT, Lin JY, Sun YG, Chau KW (2005) Long-term prediction of discharges in Manwan Hydropower using adaptive-network-based fuzzy inference systems models. Lect Notes Comput Sci 3612: 1152-1161.
9. Cigizoglu HK (2005) Application of generalized regression neural networks to intermittent flow forecasting and estimation. J Hydrol Eng ASCE 10(4): 336-341.
10. Cigizoglu HK, Kisi O (2005) Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data. Nord Hydrol 36(1): 49-64.
11. Cigizoglu HK, Metcalfe A, Adamson PT (2002) Bivariate stochastic modeling of ephemeral streamflow. Hydrol Process 16(7): 1451-1465.
12. Cortes C, Vapnik V (1995) Support vector networks. M Learn 20: 273-297.
13. Drake JT (2000) Communications phase synchronization using the adaptive network fuzzy inference system. PhD Thesis, New Mexico State University, Las Cruces, New Mexico, USA.
14. Durrant PJ (2001) winGamma: a non-linear data analysis and modelling tool with applications to flood prediction. PhD thesis, Department of Computer Science, Cardiff University, Wales, UK.
15. Evsukoff AG, Lima BSLP, Ebecken NFF (2011) Long-term runoff modeling using rainfall forecasts with application to the Iguaçu river basin. Water Resour Manage 25: 963-985.
16. Gunn S (1998) Support vector machines for classification and regression. Technical report: faculty of engineering, science and mathematics school of electronics and computer science, Available on: http://www. soton. ac. uk/.
17. Guyon I, Boser B, Vapnik V (1993) Automatic capacity tuning of very large approximation, regression estimation, and signal processing. In: Mozer M, Jordan M, Petsche T (eds) Advances in neural information processing systems 9. MIT Press, Cambridge, pp 281-287.
18. Han D, Chan L, Zhu N (2007) Flood forecasting using support vector machines. J Hydroinform 09. 4: 267-276.
19. Haykin S (1994) Neural network, a comprehensive foundation. IEEE Press, New York.
20. Haykin S (1998) Neural networks-a comprehensive foundation, 2nd edn. Prentice-Hall, Upper Saddle River, pp 26-32.
21. Haykin S (1999) Neural Network: a comprehensive foundation, 2nd edn. Prentice Hall, New Jersey.
22. Hecht-Nielsen R (1991) Neurocomputing. Addison-Wesley, New York.
23. Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Manage Cybern 23(3): 665-685.
24. Jang J-SR, Sun C-T, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, Upper Saddle River.
25. Jones AJ (1998) The winGammaTM user guide. University of Wales, Cardiff, pp 1998-2001.
26. Jones AJ (2004) New tools in non-linear modelling and prediction. Comput Manag Sci 1: 109-149.
27. Karunanithi N, Grenney WJ, Whitley D, Bovee K (1994) Neural networks for river flow prediction. J Comput Civ Eng, ASCE 8(2): 201-220.
28. Kisi O (2004) River flow modeling using artificial neural networks. J Hydrol Eng, ASCE 9(1): 60-63.
29. Kisi O (2006) Daily pan evaporation modelling using a neuro-fuzzy computing technique. J Hydrol 329: 636-646.
30. Kisi O (2009) Neural networks and wavelet conjunction model for intermittent streamflow forecasting. J Hydrol Eng, ASCE 14(8): 773-782.
31. Kisi O, Cigizoglu HK (2007) Comparison of different ANN techniques in river flow prediction. Civ Eng Environ Syst 24(3): 211-231.
32. Lin JY, Cheng CT, Chau KW (2006) Using support vector machines for long-term discharge prediction. Hydrolog Sci J 51(4): 599-612.
33. Lin G-F, Chen G-R, Huang P-Y, Chou Y-C (2009) Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods. J Hydrol 372: 17-29.
34. Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS (2004) A neuro-fuzzy computing technique for modeling hydrological time series. J Hydrol 291(1-2): 52-66.
35. Nourani V, Komasi M, Mano A (2009) A multivariate ANN-wavelet approach for rainfall-runoff modeling. Water Resour Manage 23: 2877-2894.
36. Penrose R (1955) A generalized inverse for matrices. Proc Camb Phil Soc 51: 406-413.
37. Penrose R (1956) On best approximate solution of linear matrix equations. Proc Camb Phil Soc 52: 17-19.
38. Sahoo GB, Ray C (2006) Flow forecasting for a Hawaii stream using rating curves and neural networks. J Hydrol 317: 63-80.
39. Salas JD (1993) Analysis and modeling of hydrologic time series. In: Maidment DR (ed) Chapter 19 in handbook of hydrology. McGraw-Hill, New York.
40. Schölkopf B, Burges C, Vapnik V (1995) Extracting support data for a given task. In: Fayyad UM, Uthurusamy R (eds) Proceedings, first international conference on knowledge discovery & data mining. AAAI Press, Menlo Park.
41. Singh KK, Pal M, Singh VP (2010) Estimation of mean annual flood in Indian catchments using backpropagation neural network and M5 model tree. Water Resour Manage 24: 2007-2019.
42. Sivakumar B, Jayawardena AW, Fernando TMKG (2002) River flow forecasting: use of phase space reconstruction and artificial neural networks approaches. J Hydrol 265: 225-245.
43. Smola AJ, Scholkopf B (1998). A tutorial on support vector regression. NeuroCOLT2 Technical Report Series, NC2-TR-1998-030.
44. Srikanthan R, McMahon TA (1980a) Stochastic time series modelling of arid zone streamflows. Hydrol Sci Bull 25: 423-434.
45. Srikanthan R, McMahon TA (1980b) Stochastic generation of monthly flows for ephemeral streams. J Hydrol 47: 19-40.
46. Sudheer KP, Jain SK (2003) Radial basis function neural network for modeling rating curves. J Hydrol Eng, ASCE 8(3): 161-164.
47. Tripathi S, Srinivas VV, Nanjundiah RS (2006) Downscaling of precipitation for climate change scenarios: a support vector machine approach. J Hydrol 330: 621-640.
48. Vapnik V, Lerner A (1963) Pattern recognition using generalized portrait method. Autom Rem Contr 24: 774-780.
49. Vapnik V, Golowich S, Smola A (1997) Support vector method for function VC-dimension classifiers. In: Hanson SJ, Cowan JD, Lee Giles C (eds) Advances in neural information processing systems 5: 147-155.
50. Wang W-C, Chau K-W, Cheng C-T, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374: 194-306.
51. Yevjevich V (1972) Stochastic processes in hydrology. Water Resources Publ, Fort Collins.
52. Zadeh MR, Amin S, Khalili D, Singh VP (2010) Daily outflow prediction by multi layer perceptron with logistic sigmoid and tangent sigmoid activation functions. Water Resour Manage 24: 2673-2688.
53. Zealand CM, Burn DH, Simonovic SP (1999) Short term streamflow forecasting using artificial neural networks. J Hydrol 214: 32-48.