Skip to main content
SpringerLink
Log in

Lake Level Forecasting Using Wavelet-SVR, Wavelet-ANFIS and Wavelet-ARMA Conjunction Models

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

Accurate predicting of lake level fluctuations is essential and basic in water resources management for water supply purposes. The predicting of lake level is complicated because of it is affected by nonlinear hydrological processes. This paper applies integrated wavelet and auto regressive moving average (ARMA), adaptive neuro fuzzy inference system (ANFIS) and support vector regression (SVR) models for forecasting monthly lake level fluctuations. First, lake level time series is decomposed into low and high frequency components by using discrete wavelet transform. Then, each component is separately predicted by using ARMA, ANFIS and SVR models. Finally, the predicted components are summed to obtain estimated original lake level time series. The performance of the proposed WSVR (Wavelet-SVR), WANFIS (Wavelet-ANFIS) and WARMA (Wavelet-ARMA) models is compared with single ARMA, SVR and ANFIS models. Results show that the integrated models give better precision in forecasting lake levels in the study region when compared to single models. WSVR model is found to be slightly better than the other integrated models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407(1):28–40

    Article  Google Scholar 

  • Altunkaynak A, Ozger M, Sen Z (2003) Triple diagram model of level fluctuations in Lake Van, Turkey. Hydrol Earth Syst Sci 7(2):235–244

    Article  Google Scholar 

  • Aytek A, Kisi O, Guven A (2014) A genetic programming technique for forecasting lake level fluctuations of Lake Van in Turkey. Hydrol Res 45(4–5):529–539

    Article  Google Scholar 

  • Box GE, Jenkins GM, Reinsel GC (1970) Time series analysis forecasting and control. Holden Day, San Francisco

    Google Scholar 

  • Box GEP, Jenkins GM, Reinsel GC (1997) Time series analysis: forecasting and control. China Statistic Press, Beijing

    Google Scholar 

  • Chen SH, Lin YH, Chang LC, Chang FJ (2006) The strategy of building a flood forecast model by neuro-fuzzy network. Hydrol Process 20:1525–1540

    Article  Google Scholar 

  • Chou CM, Wang RY (2002) On-line estimation of unit hydrographs using the wavelet-based LMS algorithm. Hydrol Sci J 47(5):721–738

    Article  Google Scholar 

  • Cimen M (2008) Estimation of daily suspended sediments using support vector machines. Hydrol Sci J 53(3):656–666

    Article  Google Scholar 

  • El-Shafie A, Taha MR, Noureldin A (2007) A neuro-fuzzy model for inflow forecasting of the Nile River at Aswan High Dam. Water Resour Manag 21(3):533–556

    Article  Google Scholar 

  • Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice-Hall, Eaglewood Cliffs, pp 665–685

    Google Scholar 

  • Kadioglu M, Sen Z, Batur F (1994) The greatest soda-water lake in the world and how it is influenced by climatic change. Ann Geophys 15:1489–1497

    Article  Google Scholar 

  • Kazeminezhad MH, Etemad-shahidi A, Mousavi SJ (2005) Application of fuzzy inference system in the prediction of wave parameters. Ocean Eng 32:1709–1725

    Article  Google Scholar 

  • Keskin ME, Terzi O, Taylan D (2004) Fuzzy logic model approaches to daily pan evaporation estimation in Western Turkey. Hydrol Sci J 49(6):1001–1010

    Google Scholar 

  • Keskin ME, Taylan D, Terzi O (2006) Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series. Hydrol Sci J 51(4):588–598

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Kisi O, Cimen M (2009) Evapotranspiration modelling using support vector machines. Hydrol Sci J 54(5):918–928

    Article  Google Scholar 

  • Kisi O, Cimen M (2011) Wavelet-support vector machine conjunction model for monthly streamflow forecasting. J Hydrol 399(1):450, 132–140

    Google Scholar 

  • Kisi O, Cimen M (2012) Precipitation forecasting by using wavelet support vector machine conjunction model. Eng Appl Artif Intell 25:783–792

    Article  Google Scholar 

  • Kisi O, Ozturk O (2007) Adaptive neurofuzzy computing technique for evapotranspiration estimation. J Irrig Drain E 133(4):368–379

    Article  Google Scholar 

  • Kisi O, Shiri J (2011) Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models. Water Resour Manag 25:3135–3152

    Article  Google Scholar 

  • Kisi O, Shiri J, Nikoofar B (2012) Forecasting daily lake levels using artificial intelligence approaches. Comput Geosci 41:169–180

    Article  Google Scholar 

  • Mallat SG (1989) A theory for multi resolution signal decomposition: the wavelet representation. Trans Pattern Anal Mach Intell 11:674–693

    Article  Google Scholar 

  • Mohammadi K, Eslami HR, Kahawita R (2006) Parameter estimation of an ARMA model for river flow forecasting using goal programming. J Hydrol 331(1):293–299

    Article  Google Scholar 

  • Moosavi V, Vafakhah M, Shirmohammadi B, Behnia N (2013) A wavelet -ANFIS hybrid model for groundwater level forecasting for different prediction periods. Water Resour Manag 27:1301–1321

    Article  Google Scholar 

  • Platt JC (1999) Fast training of support vector machines using sequential minimal optimization. In: Sch olkopf B, Burges CJC, Smolar AJ (eds) Advances in Kernel methods—support vector learning. MIT Press, Cambridge

    Google Scholar 

  • Radhika Y, Shashi M (2009) Atmospheric temperature prediction using support vector machines. IJCTE 1(1):55–58

    Article  Google Scholar 

  • Rajaee T (2010) Wavelet and neuro-fuzzy conjunction approach for suspended sediment prediction. Clean-Soil Air Water 38(3):275–286

    Article  Google Scholar 

  • Salas JD, Delleur JW, Yevjevich V, Lane WL (1980) Applied modeling of hydrologic time series. Water Resources Publications, Littleton

  • Sen Z, Kadioglu M, Batur E (2000) Stochastic modelling of the Van Lake monthly level fluctuations in Turkey. Theor Appl Climatol 65:99–110

    Article  Google Scholar 

  • Shiri J, Kisi O, Yoon H, Lee KK, Nazemi AH (2013) Predicting groundwater level fluctuations with meteorological effect implications- A comparative study among soft computing techniques. Comput Geosci 56:32–44

    Article  Google Scholar 

  • Shirmohammadi B, Vafakhah M, Moosavi V, Moghaddamnia A (2012) Application of several data-driven techniques for predicting groundwater level. Water Resour Manag. doi:10.1007/s11269-012-0194-y

    Google Scholar 

  • Sivapragasam C, Liong S-Y, Pasha MFK (2001) Rainfall and runoff forecasting with SSA–SVM approach. J Hydroinf 3(3):141–152

    Google Scholar 

  • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern SMC-15(1):116–132

    Article  Google Scholar 

  • Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

    Book  Google Scholar 

  • Wang W, Ding S (2003) Wavelet network model and its application to the predication of hydrology. Nat Sci 1:67–71

    Google Scholar 

  • Wei S, Yang H, Song J, Abbaspour K, Xu Z (2013) A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows. Hydrol Sci J 58(2):374–389

    Article  Google Scholar 

  • Wu CL, Chau KW, Fan C (2010a) Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. J Hydrol 389:146–167

    Article  Google Scholar 

  • Wu J, Liu M, Jin L (2010b) Least square support vector machine ensemble for daily rainfall forecasting based on linear and nonlinear regression, Advances in neural network research and applications. Lect Notes Electr Eng 67(1):55–64

    Article  Google Scholar 

  • Yarar A (2014) A hybrid wavelet and neuro-fuzzy model for forecasting the monthly streamflow data. Water Resour Manag 28:553–565

    Article  Google Scholar 

  • Yarar A, Onucyıldız M, Copty NK (2009) Modelling level changes in lakes using neuro-fuzzy and artificial neural networks. J Hydrol 365:329–334

    Article  Google Scholar 

  • Zhou HC, Peng Y, Liang G-H (2008) The research of monthly discharge predictor-corrector model based on wavelet decomposition. Water Resour Manag 22(2):217–227

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Water Engineering Department, Tabriz University, Tabriz, Iran

    Maryam Shafaei

  2. Civil Engineering Department, Hydraulics Division, Erciyes University, 38039, Kayseri, Turkey

    Ozgur Kisi

Authors
  1. Maryam Shafaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ozgur Kisi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maryam Shafaei.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shafaei, M., Kisi, O. Lake Level Forecasting Using Wavelet-SVR, Wavelet-ANFIS and Wavelet-ARMA Conjunction Models. Water Resour Manage 30, 79–97 (2016). https://doi.org/10.1007/s11269-015-1147-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-015-1147-z

Keywords

Search

Navigation