Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index
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RESEARCH ARTICLE
Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index Sani Isah Abba 1 & Quoc Bao Pham 2,3 & Gaurav Saini 4 & Nguyen Thi Thuy Linh 5 & Ali Najah Ahmed 6 & Meriame Mohajane 7,8 & Mohammadreza Khaledian 9,10 & Rabiu Aliyu Abdulkadir 11 & Quang-Vu Bach 12 Received: 31 December 2019 / Accepted: 10 June 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract 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. Keywords Artificial intelligence . Linear model . Water quality index . Ensemble learning . Yamuna River Sani Isah Abba and Quoc Bao Pham contributed equally to this work. Responsible Editor: Xianliang Yi Corresponding author at: Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam * Quang-Vu Bach [email protected]
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Soil and Environment Microbiology Team, Department of Biology, Faculty of Sciences, Moulay Ismail University, Meknes, Morocco
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Water Sciences and Environment Engineering Team, Department of Geology, Faculty of Sciences, Moulay Ismail University, Meknes, Morocco
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Department of Physical Planning Development, Yusuf Maitama Sule University Kano, Kano, Nigeria
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Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam
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Facu
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