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

Content Provider	Springer Nature Link
Author	Parmar, Kulwinder Singh Bhardwaj, Rashmi
Copyright Year	2014
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 (Db$_{8}$). 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.
Starting Page	17
Ending Page	33
Page Count	17
File Format	PDF
ISSN	09204741
Journal	Water Resources Management
Volume Number	29
Issue Number	1
e-ISSN	15731650
Language	English
Publisher	Springer Netherlands
Publisher Date	2014-11-21
Publisher Institution	European Water Resources Association (EWRA)
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Water management Hydrological model Water quality prediction Neural network Fuzzy logic Daubechies wavelet Hydrogeology Hydrology/Water Resources Geotechnical Engineering & Applied Earth Sciences Atmospheric Sciences Civil Engineering Environment
Content Type	Text
Resource Type	Article
Subject	Water Science and Technology Civil and Structural Engineering

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