Abstract
Water quality index (WQI) is an indicator of the quality of any ground water storage in the form of a single number representing a combination of different water quality parameter Different parameters like that pH, total dissolved solids (TDS), electrical conductivity (ECE), nitrate, sulphate, total hardness, calcium hardness, magnesium hardness, etc. are critical to assess the WQI. Additionally, the precision in the prediction of this parameter affects the quality of the result. In this research, Extreme Learning Model (ELM) and three hybrid variants of the same model, namely, RBF-ELM, Online Sequencing-ELM (OS-ELM), Biogeography-based optimization-ELM (BBO-ELM) were tested for the prediction of WQI for ground water quality. A time series river water quality dataset was used to develop and test the models. The performance of the proposed models are evaluated using various fitness indices such as, the correlation of coefficient (r), root mean square error (RMSE), Kling-Gupta Efficiency (KGE), the index of agreement (d). Based on the comparisons, BBO-ELM was indicated as a possible alternative or substitute to assist the water quality assessment for the groundwater and can be readily applied an efficient data-driven methodology. BBO-ELM emerged as the better generalized hybrid model for calculating WQI.
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Acknowledgement
The financial assistance for data collection of of water quality parameters for this work was provided through Board of Research and Nuclear Sciences (BRNS Project Ref. No.: 36(4)/14/10/2014-BRNS) under Department of Atomic Energy, India.
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Gupta, A.N., Kumar, D. & Singh, A. Evaluation of Water Quality Based on a Machine Learning Algorithm and Water Quality Index for Mid Gangetic Region (South Bihar plain), India. J Geol Soc India 97, 1063–1072 (2021). https://doi.org/10.1007/s12594-021-1821-0
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DOI: https://doi.org/10.1007/s12594-021-1821-0