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Intermittent Streamflow Forecasting by Using Several Data Driven Techniques

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Abstract

Forecasting intermittent streamflows is an important issue for water quality management, water supplies, hydropower and irrigation systems. This paper compares the accuracy of several data driven techniques, that is, adaptive neuro fuzzy inference system (ANFIS), artificial neural networks (ANNs) and support vector machine (SVM) for forecasting daily intermittent streamflows. The results are also compared with those of the local linear regression (LLR) and the dynamic local linear regression (DLLR). Intermittent streamflow data from two stations, Uzunkopru and Babaeski, in Thrace region located in north-western Turkey are used in the study. The root mean square error and correlation coefficient were used as comparison criteria. The comparison results indicated that the ANFIS, ANN and SVM models performed better than the LLR and DLLR models in forecasting daily intermittent streamflows. The ANN and ANFIS gave the best forecasts for the Uzunkopru and Babaeski stations, respectively.

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References

  • Aksoy H, Bayazit M (2000) A daily intermittent streamflow simulator. Turk J Eng Environ Sci 24:265–276

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • Chang F-J, Chen Y-C (2001) A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction. J Hydrol 245:153–164

    Article  Google Scholar 

  • Chang CC, Lin CJ (2004) LIBSVM—a library for support vector machines. Available at: http://www.csie.ntu.edu.tw/~cjlin/libsvm/index.html

  • Chau KW, Wu CL, Li YS (2005) Comparison of several flood forecasting models in Yangtze River. J Hydrol Eng ASCE 10(6):485–491

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Cigizoglu HK (2005) Application of generalized regression neural networks to intermittent flow forecasting and estimation. J Hydrol Eng ASCE 10(4):336–341

    Article  Google Scholar 

  • 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

    Google Scholar 

  • Cigizoglu HK, Metcalfe A, Adamson PT (2002) Bivariate stochastic modeling of ephemeral streamflow. Hydrol Process 16(7):1451–1465

    Article  Google Scholar 

  • Cortes C, Vapnik V (1995) Support vector networks. M Learn 20:273–297

    Google Scholar 

  • Drake JT (2000) Communications phase synchronization using the adaptive network fuzzy inference system. PhD Thesis, New Mexico State University, Las Cruces, New Mexico, USA

  • 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

  • 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

    Article  Google Scholar 

  • 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/.

  • 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

    Google Scholar 

  • Han D, Chan L, Zhu N (2007) Flood forecasting using support vector machines. J Hydroinform 09.4:267– 276

    Google Scholar 

  • Haykin S (1994) Neural network, a comprehensive foundation. IEEE Press, New York

    Google Scholar 

  • Haykin S (1998) Neural networks—a comprehensive foundation, 2nd edn. Prentice-Hall, Upper Saddle River, pp 26–32

    Google Scholar 

  • Haykin S (1999) Neural Network: a comprehensive foundation, 2nd edn. Prentice Hall, New Jersey

    Google Scholar 

  • Hecht-Nielsen R (1991) Neurocomputing. Addison-Wesley, New York

    Google Scholar 

  • Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Manage Cybern 23(3):665–685

    Article  Google Scholar 

  • 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

    Google Scholar 

  • Jones AJ (1998) The winGammaTM user guide. University of Wales, Cardiff, pp 1998–2001

    Google Scholar 

  • Jones AJ (2004) New tools in non-linear modelling and prediction. Comput Manag Sci 1:109–149

    Article  Google Scholar 

  • Karunanithi N, Grenney WJ, Whitley D, Bovee K (1994) Neural networks for river flow prediction. J Comput Civ Eng, ASCE 8(2):201–220

    Article  Google Scholar 

  • Kisi O (2004) River flow modeling using artificial neural networks. J Hydrol Eng, ASCE 9(1):60–63

    Article  Google Scholar 

  • Kisi O (2006) Daily pan evaporation modelling using a neuro-fuzzy computing technique. J Hydrol 329:636–646

    Article  Google Scholar 

  • Kisi O (2009) Neural networks and wavelet conjunction model for intermittent streamflow forecasting. J Hydrol Eng, ASCE 14(8):773–782

    Article  Google Scholar 

  • Kisi O, Cigizoglu HK (2007) Comparison of different ANN techniques in river flow prediction. Civ Eng Environ Syst 24(3):211–231

    Article  Google Scholar 

  • Lin JY, Cheng CT, Chau KW (2006) Using support vector machines for long-term discharge prediction. Hydrolog Sci J 51(4):599–612

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Nourani V, Komasi M, Mano A (2009) A multivariate ANN-wavelet approach for rainfall–runoff modeling. Water Resour Manage 23:2877–2894

    Article  Google Scholar 

  • Penrose R (1955) A generalized inverse for matrices. Proc Camb Phil Soc 51:406–413

    Article  Google Scholar 

  • Penrose R (1956) On best approximate solution of linear matrix equations. Proc Camb Phil Soc 52:17–19

    Article  Google Scholar 

  • Sahoo GB, Ray C (2006) Flow forecasting for a Hawaii stream using rating curves and neural networks. J Hydrol 317:63–80

    Article  Google Scholar 

  • Salas JD (1993) Analysis and modeling of hydrologic time series. In: Maidment DR (ed) Chapter 19 in handbook of hydrology. McGraw-Hill, New York

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Smola AJ, Scholkopf B (1998). A tutorial on support vector regression. NeuroCOLT2 Technical Report Series, NC2-TR-1998-030

  • Srikanthan R, McMahon TA (1980a) Stochastic time series modelling of arid zone streamflows. Hydrol Sci Bull 25:423–434

    Article  Google Scholar 

  • Srikanthan R, McMahon TA (1980b) Stochastic generation of monthly flows for ephemeral streams. J Hydrol 47:19–40

    Article  Google Scholar 

  • Sudheer KP, Jain SK (2003) Radial basis function neural network for modeling rating curves. J Hydrol Eng, ASCE 8(3):161–164

    Article  Google Scholar 

  • Tripathi S, Srinivas VV, Nanjundiah RS (2006) Downscaling of precipitation for climate change scenarios: a support vector machine approach. J Hydrol 330:621–640

    Google Scholar 

  • Vapnik V, Lerner A (1963) Pattern recognition using generalized portrait method. Autom Rem Contr 24:774–780

    Google Scholar 

  • 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

  • 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

    Article  Google Scholar 

  • Yevjevich V (1972) Stochastic processes in hydrology. Water Resources Publ, Fort Collins

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Zealand CM, Burn DH, Simonovic SP (1999) Short term streamflow forecasting using artificial neural networks. J Hydrol 214:32–48

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Dept. of Civil Eng., Faculty of Eng., University of Erciyes, Kayseri, Turkey

    Ozgur Kisi

  2. Dept. of Watershed and Range Management, Faculty of Natural Resources, Univ. of Zabol, Zabol, Iran

    Alireza Moghaddam Nia, Mohsen Ghafari Gosheh & Mohammad Reza Jamalizadeh Tajabadi

  3. Department of Civil Engineering, Isfahan University of Technology, Isfahan, Iran

    Azadeh Ahmadi

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  1. Ozgur Kisi
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  2. Alireza Moghaddam Nia
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  3. Mohsen Ghafari Gosheh
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  4. Mohammad Reza Jamalizadeh Tajabadi
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  5. Azadeh Ahmadi
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Correspondence to Ozgur Kisi.

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Kisi, O., Nia, A.M., Gosheh, M.G. et al. Intermittent Streamflow Forecasting by Using Several Data Driven Techniques. Water Resour Manage 26, 457–474 (2012). https://doi.org/10.1007/s11269-011-9926-7

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  • DOI: https://doi.org/10.1007/s11269-011-9926-7

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