Quality and quantity of the river parameters modeling using conjunction artificial neural network and wavelet

Publish Year: 1395
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:

WRM06_276

تاریخ نمایه سازی: 6 بهمن 1395

Abstract:

The paper describes the training, validation and application of artificial neural network (ANN) and wavelet models for computing the 11 quality and quantity parameters of the Jajrood River (Iran) in which two ANN models were identified, validated and tested for the computation of parameters in the Jajrood river water. Both the models employed eleven input water quality and quantity variables measured in river water over a period of 40 years each month at two different latyan and roudak stations. The performance of the ANN models was assessed through the coefficient of determination (R2) (square of the correlation coefficient), root mean square error (RMSE), SSE and bias computed from the measured and model computed values of the dependent variables. The model computed values of 11parameters by both the ANN models were in close agreement with their respective measured values in the river water. Relative importance and contribution of the input variables to the model output was evaluated through the partitioning approach. The identified ANN models can be used as tools for the computation of water quality and quantity parameters.

Authors

Maryam Khalilzadeh Poshtegal

Ph.D Candidate in Civil Environmental Engineering, K.N.Toosi University of Technology, Tehran, Iran

Mojtaba Noury

Research Manager, Iran Water Resource Management Company

Kaveh Madani

Senior lecturer center of environmental policy imperial college London

Seyed Ahmad Mirbagheri

Department of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran

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  • Hydrology, Volume 372, Issues 1-4, 15 June 2009, PP. 17-29. ...
  • Hydrology, Volume 372, Issues 1-4, 15 June 2009, PP. 17-29. ...
  • Maier, H.R. and G.C. Dandy, 1996. The use of artificial ...
  • Maren, A., C. Harston, and R. Pap, Handbook of Neural ...
  • Mojtaba Noury et al, 2014, Water Level Elevation Variations Modeling ...
  • Nourani, V., Alami, M., Aminfar, M., 2009.A combined neural-wavele model ...
  • Nourani, V., Komasi, M., Mano, A., 2009.A multivariate ANN-Wavelt approach ...
  • Partal T, Kisi O (2007) Wavelet and Neuro-fuzzy conjunction model ...
  • Raman, H. and R. Sunilkumar, 1995. Multivariate modelling of water ...
  • Ranjithan, S., J. W. Eheart, and J. H. Garrett Jr., ...
  • Rumelhart D.E., Hinton E. and Williams J. (1986), Learning internal ...
  • Skoulikidis N.T. (2002). Hydrochemical character and spatiotemporal variations in a ...
  • Wang, W.C., Chau, K.W., Lin Qiu, C.T.C., 2009. A comparison ...
  • several artificial intelligence methods for forecasting monthly discharge time series, ...
  • Wang, W.C., Chau, K.W., Lin Qiu, C.T.C., 2009. A comparison ...
  • several artificial intelligence methods for forecasting monthly discharge time series, ...
  • Wu, C.L. Chau, K.W. and Li, Y.S., 2009.Methods to improve ...
  • Wu, C.L. Chau, K.W. and Li, Y.S., 2009.Methods to improve ...
  • performance in daily flows prediction, Jourmal of Hydrology, Volume 372, ...
  • _ _ aramet _ _ _ _ _ _ (EYE), ...
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