Skip to main content
Log in

Prediction of Inflow at Three Gorges Dam in Yangtze River with Wavelet Network Model

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

A wavelet network model is developed to predict the inflow of Three Gorges dam in Yangtze River, China. The model makes use of the multi-resolution analysis of wavelet analysis and the nonlinear capability of artificial neural network. The short and long term input runoff of Three Gorges dam, such as annual mean discharge, seasonal mean discharge of 10 days period, daily mean discharge and annual maximum flood peak discharge, have been predicted with the wavelet network model (WNM). At the same time a kind of threshold auto-regressive model (TAR) has also applied for those predictions. The comparison of WNM with TAR has been executed. The results show that the accuracy of model predictions with WNM is generally better than that with TAR. The suggested wavelet network model is functional and feasible for runoff prediction.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Anctil F, Coulibaly P (2004) Wavelet analysis of the inter-annual variability in southern Quebec stream flow. J Clim 17:163–173. doi:10.1175/1520-0442(2004)017<0163:WAOTIV>2.0.CO;2

    Article  Google Scholar 

  • Aussem A, Campbell J, Murfagh F (1998) Wavelet-based feature extraction and decomposition strategies for financial forecasting. J Comput Intell Finance 6:1–17

    Google Scholar 

  • Bayazit M, Aksoy H (2001) Using wavelets for data generation. J Appl Stat 28(2):157–166. doi:10.1080/02664760020016073

    Article  Google Scholar 

  • Bowden GJ, Maier HR, Dandy GC (2002) Optimal division of data for neural network models in water resources applications. Water Resour Res 38(2):1–11. doi:10.1029/2001WR000266

    Article  Google Scholar 

  • Box GH, Jenkins G (1970) Time series analysis: forecasting and control. Holden-Day, San Francisco

    Google Scholar 

  • Chen SY (2005) Theories and methods of variable fuzzy sets in water resources and flood control system. Dalian University of Technology Press, Dalian

    Google Scholar 

  • Chou CM (2007) Applying multi-resolution analysis to differential hydrological grey models with dual series. J Hydrol (Amst) 332:174–186. doi:10.1016/j.jhydrol.2006.06.031

    Article  Google Scholar 

  • Chui CK (1995) Wavelet—a tutorial in theory and applications. Xi’an Jiao Tong University Press, Xi’an

    Google Scholar 

  • Deng JL (1992) Grey prediction and grey decision. Hua Zhong Li Gong University Press, Wuhan

    Google Scholar 

  • Ding J, Deng YR (1988) Stochastic hydrology. Chengdu University of Science and Technology Press, Chengdu

    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 Manage 21:533–556. doi:10.1007/s11269-006-9027-1

    Article  Google Scholar 

  • French MN, Krajewski WF, Cuykendall RR (1992) Rainfall forecasting in space and time using a neural network. J Hydrol (Amst) 137:1–31. doi:10.1016/0022-1694(92)90046-X

    Article  Google Scholar 

  • Half AH, Halff HM, Azmoodeh M (1993) Predicting runoff from rainfall using artificial neural networks. Proc Engng Hydrol ASCE 761–765

  • Hua B, Xiong W, Cheng H (2008) Application of fuzzy support vector machine to runoff forecast. Eng J Wuhan Univ 41(1):5–8

    Google Scholar 

  • Huang WR, Catherine M (2008) Multiple-station neural network for modeling tidal currents across Shinnecock Inlet, USA. Hydrol Process 22:1136–1149. doi:10.1002/hyp.6671

    Article  Google Scholar 

  • Jayawardena AW, Lai F (1994) Analysis and prediction of chaos in rainfall and streamflow time series. J Hydrol (Amst) 153:23–52. doi:10.1016/0022-1694(94)90185-6

    Article  Google Scholar 

  • Jin JL, Ding J (2002) Water resource systems engineering. Sichuan Science and Technology Press, Sichuan

    Google Scholar 

  • Karunasinghe DSK, Liong SY (2006) Chaotic time series prediction with a global model: artificial neural network. J Hydrol 153:23–52, 323:92–105

    Google Scholar 

  • Kisi O (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng 12(5):532–539. doi:10.1061/(ASCE)1084-0699(2007)12:5(532)

    Article  Google Scholar 

  • Kisi O (2008a) Stream flow forecasting using neuro-wavelet technique. Hydrol Process 22(20):4142–4152. doi:10.1002/hyp.7014

    Article  Google Scholar 

  • Kisi O (2008b) River flow forecasting and estimation using different artificial neural network techniques. Hydrol Res 39(1):27–40. doi:10.2166/nh.2008.026

    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. doi:10.1080/10286600600888565

    Article  Google Scholar 

  • Kumar P, Georgiou EF (1993) A multicomponent decomposition of spatial rainfall fields 1. Segregation of large- and small-scale features using wavelet transforms. Water Resour Res 29(8):2515–2532. doi:10.1029/93WR00548

    Article  Google Scholar 

  • Labat D, Ababou R, Mangin A (2000) Rainfall–runoff relations for karstic springs Part II: continuous wavelet and discrete orthogonal multi-resolution analyses. J Hydrol (Amst) 238:149–187. doi:10.1016/S0022-1694(00)00322-X

    Article  Google Scholar 

  • Li XB, Ding J, Li HQ (1999) Combing neural network models based on wavelet transform. J Hydraul 2:1–4

    Google Scholar 

  • Mallat SG (1989) Multifrequency channel decomposition of images and wavelet models. IEEE Trans ASSP 37(12):2091–2110

    Article  Google Scholar 

  • Partal T, Kisi O (2007) Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. J Hydrol (Amst) 342(1–2):199–212. doi:10.1016/j.jhydrol.2007.05.026

    Article  Google Scholar 

  • Qing GH, Ding J, Liu GD (2002) Self-adapted BP algorithm and its application to flood forecast of river. Advance Water Sci 13(1):37–41

    Google Scholar 

  • Raman H, Sunilkumar N (1995) Multivariate modeling of water resource time series using artificial neural network. Hydrol Sci J 40(21):145–163

    Article  Google Scholar 

  • Shensa MJ (1992) Discrete wavelet transform: wedding the A Trous and Mallat algorithm. IEEE Trans Signal Process 40:2464–2482. doi:10.1109/78.157290

    Article  Google Scholar 

  • Smith LC, Turcotte DL, Sacke BI (1998) Stream flow characterization and feature detection using a discrete wavelet transform. Hydrol Process 12:233–249. doi:10.1002/(SICI)1099-199802)12:2<233::AID-HYP573>3.0.CO;2-3

    Article  Google Scholar 

  • Tesong H, Lam KC, Ng ST (2001) River flow time series prediction with a range dependent neural network. Hydrol Sci J 46(5):729–745

    Google Scholar 

  • Tokar AS, Johnson PA (1999) Rain–runoff modeling using artificial neural network. J Hydrol Engng ASCE 4(3):232–239. doi:10.1061/(ASCE)1084-0699(1999)4:3(232)

    Article  Google Scholar 

  • Tong H (1990) Non-linear time series: a dynamical system approach. Oxford university press, Oxford

    Google Scholar 

  • Wang WS, Ding J (2003a) Study on periodicity and long term forecast of annual maximum peak discharge at Yichang station of Yangtze River. J Sichuan Univ 35(1):20–23

    Google Scholar 

  • Wang WS, Ding J (2003b) Wavelet network model and its application to the prediction of hydrology. Nature and Science 1(1):63–67

    Google Scholar 

  • Wang WS, Ding J, Li YQ (2005) Hydrology wavelet analysis. Chemical Industry Press, Beijing

    Google Scholar 

  • Wang HF, Huang WJ, Wang WS et al (2006a) Cuntan station of the Yangtze River annual runoff forecasting with set pair analysis method. J Heilongjiang Hydraul Eng Coll 33(4):3–5

    Google Scholar 

  • Wang W, Pieter HAJM, Van Gelder JK et al (2006b) Forecasting daily streamflow using hybrid ANN models. J Hydrol (Amst) 324:383–399. doi:10.1016/j.jhydrol.2005.09.032

    Article  Google Scholar 

  • Wang WS, Xiang HL, Ding J (2001) Predication of hydrology and water resources with nearest neighbor bootstrapping regressive model. Water Resources and Power 19(2):8–10, 14

    Google Scholar 

  • Zhao KQ (2000) Set pair analysis and its elementary application. Zhejiang Science and Technology Press, Hangzhou

    Google Scholar 

  • Zhang LM (1994) Artificial neural network and its application. Fudan University Press, Shanghai

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wensheng Wang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, W., Jin, J. & Li, Y. Prediction of Inflow at Three Gorges Dam in Yangtze River with Wavelet Network Model. Water Resour Manage 23, 2791–2803 (2009). https://doi.org/10.1007/s11269-009-9409-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-009-9409-2

Keywords

Navigation