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Comparison of machine learning models for predicting fluoride contamination in groundwater

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Abstract

Groundwater is an especially important freshwater source for water supplies in the Maku area of northwest Iran. The groundwater of the area contains high concentrations of fluoride and is, therefore, important in predicting the fluoride contamination of the groundwater for the purpose of planning and management. The present study aims to evaluate the ability of the extreme learning machine (ELM) model to predict the level of fluoride contamination in the groundwater in comparison to multilayer perceptron (MLP) and support vector machine (SVM) models. For this purpose, 143 water samples were collected in a five-year period, 2004–2008. The samples were measured and analyzed for electrical conductivity, pH, major chemical ions and fluoride. To develop the models, the data set—including Na+, K+, Ca2+ and HCO3 − concentrations as the inputs and fluoride concentration as the output—was divided into two subsets; training/validation (80% of data) and testing (20% of data), based on a cross-validation technique. The radial basis-based ELM model resulted in an R 2 of 0.921, an NSC of 0.9071, an RMSE of 0.5638 (mg/L) and an MABE of 0.4635 (mg/L) for the testing data. The results showed that the ELM models performed better than MLP and SVM models for prediction of fluoride contamination. It was observed that ELM models learned faster than the other models during model development trials and the SVM models had the highest computation time.

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Authors and Affiliations

  1. Department of Earth Sciences,Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran

    Rahim Barzegar & Asghar Asghari Moghaddam

  2. Department of Bioresource Engineering, Faculty of Agricultural and Environmental Sciences, McGill University, Ste Anne de Bellevue, QC, H9X 3V9, Canada

    Jan Adamowski

  3. School of Geology, University College of Science, University of Tehran, Tehran, Iran

    Elham Fijani

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  1. Rahim Barzegar
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  4. Elham Fijani
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Barzegar, R., Asghari Moghaddam, A., Adamowski, J. et al. Comparison of machine learning models for predicting fluoride contamination in groundwater. Stoch Environ Res Risk Assess 31, 2705–2718 (2017). https://doi.org/10.1007/s00477-016-1338-z

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