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Evaluation of machine learning algorithms for groundwater quality modeling

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Abstract

Groundwater quality is typically measured through water sampling and lab analysis. The field-based measurements are costly and time-consuming when applied over a large domain. In this study, we developed a machine learning-based framework to map groundwater quality in an unconfined aquifer in the north of Iran. Groundwater samples were provided from 248 monitoring wells across the region. The groundwater quality index (GWQI) in each well was measured and classified into four classes: very poor, poor, good, and excellent, according to their cut-off values. Factors affecting groundwater quality, including distance to industrial centers, distance to residential areas, population density, aquifer transmissivity, precipitation, evaporation, geology, and elevation, were identified and prepared in the GIS environment. Six machine learning classifiers, including extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), artificial neural networks (ANN), k-nearest neighbor (KNN), and Gaussian classifier model (GCM), were used to establish relationships between GWQI and its controlling factors. The algorithms were evaluated using the receiver operating characteristic curve (ROC) and statistical efficiencies (overall accuracy, precision, recall, and F-1 score). Accuracy assessment showed that ML algorithms provided high accuracy in predicting groundwater quality. However, RF was selected as the optimum model given its higher accuracy (overall accuracy, precision, and recall = 0.92; ROC = 0.95). The trained RF model was used to map GWQI classes across the entire region. Results showed that the poor GWQI class is dominant in the study area (covering 66% of the study area), followed by good (19% of the area), very poor (14% of the area), and excellent (< 1% of the area) classes. An area of very poor GWQI was observed in the north. Feature analysis indicated that the distance to industrial locations is the main factor affecting groundwater quality in the region. The study provides a cost-effective methodology in groundwater quality modeling that can be duplicated in other regions with similar hydrological and geological settings.

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Acknowledgements

We would like to express our appreciation to the Regional Water organization of Mazandaran for providing the hydrological and meteorological data.

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Authors and Affiliations

  1. Rouzbahan Institute of Higher Education, Sari, Iran

    Soheil Sahour

  2. Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran

    Matin Khanbeyki

  3. Department of Range and Watershed Management and Dept. of Water Eng. and Environment, Faculty of Natural Resources, University of Guilan, Sowmeh Sara 1144, Guilan, Iran

    Vahid Gholami

  4. Department of Geological and Environmental Sciences, Western Michigan University, Kalamazoo, MI, 49008, USA

    Hossein Sahour & Hadi Karimi

  5. Department of Computer Sciences, Western Michigan University, Kalamazoo, MI, 49008, USA

    Irene Kahvazade

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All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by Soheil Sahour, Matin Khanbeyki, Vahid Gholami, Hossein Sahour, Irene Kahvazade, and Hadi Karimi. The first draft of the manuscript was written by Soheil Sahour and Matin Khanbeyki, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Sahour, S., Khanbeyki, M., Gholami, V. et al. Evaluation of machine learning algorithms for groundwater quality modeling. Environ Sci Pollut Res 30, 46004–46021 (2023). https://doi.org/10.1007/s11356-023-25596-3

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