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Neural Computing and Applications

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23-10-2019 | Original Article

Neuro-fuzzy-wavelet hybrid approach to estimate the future trends of river water quality

Authors: Kulwinder Singh Parmar, Sidhu Jitendra Singh Makkhan, Sachin Kaushal

Published in: Neural Computing and Applications | Issue 12/2019

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Abstract

Water is the basic need for life to exist on this planet earth; rivers play a vital role to fulfill this need for the supply of freshwater. Due to spontaneous growth of industrialization and urbanization near the important rivers, most of them have been polluted to a severe extent and the future of these rivers and living organism depending on the water from them is on threat. Thus, various prediction models have been developed by researchers to build an accurate forecasting model to access the future quality of rivers with least forecasting error. Time series models have been developed to form such prediction, but most of them were unsuccessful in handling nonlinear problems. Artificial neural network (ANN) and adaptive neuro-fuzzy interface system have proven to be an efficient tool to handle such nonlinear situations. In this study, in addition to the above methods, wavelet transformation has been used to develop a forecasting model to generate forecasts close to actual values. The biochemical oxygen demand of river Yamuna at sample site of Nizamuddin (Delhi) is predicted using the past monthly averaged data. Statistical analysis has been used to study the nature of the wavelet domain constitutive series considered. The results obtained indicate that the neuro-fuzzy-wavelet-coupled model leads to considerably superior outcomes compared to neuro-fuzzy, ANN and regression models.

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Title: Neuro-fuzzy-wavelet hybrid approach to estimate the future trends of river water quality
Authors: Kulwinder Singh Parmar
Sidhu Jitendra Singh Makkhan
Sachin Kaushal
Publication date: 23-10-2019
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 12/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-019-04560-8

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