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Groundwater quality assessment using random forest method based on groundwater quality indices (case study: Miandoab plain aquifer, NW of Iran)

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Abstract

Miandoab plain aquifer, with 1150-km2 area, supplies a significant portion of the agricultural and drinking water demands of the area. In recent years, it has been faced with a significant decline in water level as a consequence of the deterioration of groundwater quality. Therefore, a scientific study of the groundwater resources in the study area for quantitative and qualitative management is necessary. One of the important indicators for assessing and zoning of the groundwater quality is the measurement of the concentrations of the ions and determining the groundwater quality index (GQI) by combining ion concentrations and their relationship with reliable standards. For this purpose, in October 2018, 75 water samples from the groundwater resources of the Miandoab plain aquifer were collected and chemically analyzed. To minimize the uncertainties, the fuzzy groundwater quality index was used by the fuzzification of the GQI method. Also, the random forest (RF) algorithms, as a learning method based on an ensemble of decision trees, were used for the assessment of groundwater quality. The RF technique has advantages over the other methods due to having high prediction accuracy, the ability to learn nonlinear relationships, and the ability to determine the important variables in the prediction. In the validation and comparison of methods, fuzzy groundwater quality index method with more accuracy is identified as a more reliable method in groundwater quality evaluation for drinking purposes. Based on the RFGQI results, 20, 16, 15, 26, and 23% of the Miandoab plain aquifer, respectively, has a suitable, acceptable, moderate, unsuitable, and absolutely unsuitable groundwater quality. Overall, the results of this study showed that the random forest method can be used as a reliable method for groundwater vulnerability, investigating and properly managing or monitoring of the aquifers.

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Authors and Affiliations

  1. Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran

    Hossein Norouzi & Asghar Asghari Moghaddam

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  1. Hossein Norouzi
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  2. Asghar Asghari Moghaddam
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Correspondence to Hossein Norouzi.

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Responsible Editor: Amjad Kallel

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Norouzi, H., Moghaddam, A.A. Groundwater quality assessment using random forest method based on groundwater quality indices (case study: Miandoab plain aquifer, NW of Iran). Arab J Geosci 13, 912 (2020). https://doi.org/10.1007/s12517-020-05904-8

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