• 1 Department of Physical Planning Development, Yusuf Maitama Sule University Kano, Kano, Nigeria
  • 2 Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam
  • 3 Department of Civil Engineering, Sharda University, Greater Noida, Uttar Pradesh, India
  • 4 Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam
  • 5 Institute of Energy Infrastructure (IEI), Civil Engineering Department, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor, Malaysia
  • 6 Soil and Environment Microbiology Team, Department of Biology, Faculty of Sciences, Moulay Ismail University, Meknes, Morocco
  • 7 Water Engineering Dept., Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
  • 8 Department of Electrical and Electronic, Kano University of Science & Technology, Wudil, Wudil, Nigeria
  • 9 Sustainable Management of Natural Resources and Environment Research Group, Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
Environ Sci Pollut Res Int, 2020 Nov;27(33):41524-41539.
PMID: 32686045 DOI: 10.1007/s11356-020-09689-x


In recent decades, various conventional techniques have been formulated around the world to evaluate the overall water quality (WQ) at particular locations. In the present study, back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and one multilinear regression (MLR) are considered for the prediction of water quality index (WQI) at three stations, namely Nizamuddin, Palla, and Udi (Chambal), across the Yamuna River, India. The nonlinear ensemble technique was proposed using the neural network ensemble (NNE) approach to improve the performance accuracy of the single models. The observed WQ parameters were provided by the Central Pollution Control Board (CPCB) including dissolved oxygen (DO), pH, biological oxygen demand (BOD), ammonia (NH3), temperature (T), and WQI. The performance of the models was evaluated by various statistical indices. The obtained results indicated the feasibility of the developed data intelligence models for predicting the WQI at the three stations with the superior modelling results of the NNE. The results also showed that the minimum values for root mean square (RMS) varied between 0.1213 and 0.4107, 0.003 and 0.0367, and 0.002 and 0.0272 for Nizamuddin, Palla, and Udi (Chambal), respectively. ANFIS-M3, BPNN-M4, and BPNN-M3 improved the performance with regard to an absolute error by 41%, 4%, and 3%, over other models for Nizamuddin, Palla, and Udi (Chambal) stations, respectively. The predictive comparison demonstrated that NNE proved to be effective and can therefore serve as a reliable prediction approach. The inferences of this paper would be of interest to policymakers in terms of WQ for establishing sustainable management strategies of water resources.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.