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River Water Prediction Modeling Using Neural Networks, Fuzzy and Wavelet Coupled Model

Abstract

In this paper, new prediction model introduced by coupling of neural networks model, fuzzy model and wavelet model for the water resources management. Artificial neural network (ANN), fuzzy, wavelet and adaptive neuro-fuzzy inference system (ANFIS) are found to be a sturdy tool to model many non-linear hydrological processes. Wavelet transformation will improve the ability of a prediction model by capturing valuable information on different resolution levels. The target of this research is to compare our model with other famous data-driven models for monthly forecasting of water quality parameter chemical oxygen demand (COD) level monitored at Nizamuddin station, New Delhi, India of river Yamuna based on the past history. The data has been decomposed into wavelet domain constitutive sub series using Daubechies wavelet at level 8 (Db8). Statistical behavior of wavelet domain constitutive series has been studied. The foretelling performance of the wavelet coupled model has been compared with classical neuro fuzzy, artificial neural network and regression models. The result shows that the wavelet coupled model produces considerably higher leads to comparison to neuro fuzzy, neural network, regression models.

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Acknowledgments

Authors are thankful to the University Grant Commission (UGC), Government of India for financial support (F. 41-803/2012 (SR)); Central Pollution Control Board (CPCB), Government of India for providing the research data; Guru Gobind Singh Indraprastha University, Delhi (India) for providing research facilities. The first author is thankful to Sant Baba Bhag Singh Institute of Engineering and Technology for providing study leave to pursue research degree.

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Correspondence to Rashmi Bhardwaj.

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Parmar, K.S., Bhardwaj, R. River Water Prediction Modeling Using Neural Networks, Fuzzy and Wavelet Coupled Model. Water Resour Manage 29, 17–33 (2015). https://doi.org/10.1007/s11269-014-0824-7

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  • DOI: https://doi.org/10.1007/s11269-014-0824-7

Keywords

  • Water management
  • Hydrological model
  • Water quality prediction
  • Neural network
  • Fuzzy logic
  • Daubechies wavelet