Abstract
This research investigates the capabilities of four data-driven models, namely artificial neural network, wavelet-based artificial neural network (WANN), gene expression programming, and support vector machine, to predict water quality parameter, i.e., sodium absorption ratio (SAR) of the groundwater of the Ardabil plain, Ardabil Province, Iran. A combination of data sets including electrical conductivity, total dissolved solids, and sodium was considered as input data of the models. The models’ performances were compared with the classical multiple linear regression model. The models were calibrated and validated using the measured quality data for the period of year 2005 to 2015. The performance of the models was evaluated using various statistical criteria, scatter, and PDF plots. Results showed that the data-driven models are more capable in simulating the SAR data than the classical multiple linear regression model. The WANN model provided reliable predictions of the SAR values rather than the other models.
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Availability of data and material (data transparency)
The datasets used in this study were compiled from the Regional Water Organization in Iran. They are available from the corresponding author upon reasonable request.
Code availability
The code generated in this study is available from the corresponding author on reasonable request.
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Conceptualization, writing—review and editing—were performed by MHK, methodology, formal analysis, and investigation by MRN, data collection and data analysis by RJ, and all authors read and approved the final manuscript.
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Hasanpour Kashani, M., Nikpour, M.R. & Jalali, R. Water quality prediction using data-driven models case study: Ardabil plain, Iran. Soft Comput 27, 7439–7448 (2023). https://doi.org/10.1007/s00500-022-07684-7
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DOI: https://doi.org/10.1007/s00500-022-07684-7